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|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
66,070,459 | <p><strong>I could be wrong,</strong> it should not differ whether if it is a classification or regression. Think about it mathematically.</p>
<p><strong>Generally speaking</strong>, having <code>softmax</code> in the hidden layers is not preferred because we want every neuron to be independent from each other. If you ... | 2021-02-05 20:56:20.433000+00:00 | 2021-02-05 21:27:39.437000+00:00 | 2021-02-05 21:27:39.437000+00:00 | null | 66,069,636 | <p>I have done manual hyperparameter optimization for ML models before and always defaulted to <em>tanh</em> or <em>relu</em> as hidden layer activation functions. Recently, I started trying out Keras Tuner to optimize my architecture and accidentally left <em>softmax</em> as a choice for hidden layer activation.</p>
<... | 2021-02-05 19:43:28.030000+00:00 | 2021-02-05 21:27:39.437000+00:00 | null | python|keras|softmax | ['https://arxiv.org/pdf/1410.5401v2.pdf'] | 1 |
13,017,935 | <p>Daniel Lemire has a couple of papers on pre-sorting to increase compression and performance.
Here's the latest: <a href="http://arxiv.org/abs/1207.2189" rel="nofollow">http://arxiv.org/abs/1207.2189</a></p>
<p>You might look at his EWah variant as well. </p>
<p>The prevailing feeling is that Bitmap array compressi... | 2012-10-22 18:59:16.480000+00:00 | 2013-02-12 22:35:03.423000+00:00 | 2013-02-12 22:35:03.423000+00:00 | null | 11,924,954 | <p>I have developing word align bitmap compression algorithm for data indexing.algorithm is based on the WAH compression research paper.compression bitmap perform well on bit-wise operation and it's very space efficient. but modifying the compressed bitmap not very efficient ,because modifying need splitting compressed... | 2012-08-12 19:01:42.053000+00:00 | 2013-02-12 22:35:03.423000+00:00 | null | c++|database|performance|compression|bit-manipulation | ['http://arxiv.org/abs/1207.2189', 'https://github.com/lemire/javaewah', 'https://github.com/lemire/EWAHBoolArray'] | 3 |
64,773,818 | <p>Many people use "Doc2Vec" to refer to the word2vec-like algorithm introduced by a paper titled <a href="https://arxiv.org/abs/1405.4053" rel="nofollow noreferrer">Distributed Representation of Sentences and Documents</a> (by Le & Mikolov). That paper calls the algorithm 'Paragraph Vector', without usin... | 2020-11-10 17:32:04.837000+00:00 | 2020-11-11 20:12:59.113000+00:00 | 2020-11-11 20:12:59.113000+00:00 | null | 64,772,221 | <p>I am a student (computer science). This is my first question in stackoverflow. I really would appreciate your help! (The package I am referring to is called 'word2vec', thats why the tags/title are a bit confusing to choose.)</p>
<p>In the description of the doc2vec function (here <a href="https://cran.r-project.org... | 2020-11-10 15:52:26.460000+00:00 | 2020-11-21 08:27:51.943000+00:00 | 2020-11-21 08:27:51.943000+00:00 | r|word2vec|doc2vec | ['https://arxiv.org/abs/1405.4053'] | 1 |
43,505,854 | <p>A way to get around this without rooting the phone is to send your packets via multicast UDP*. These packets will make it from GO1 to GO2. </p>
<p>There are some side effects to this: </p>
<ul>
<li><p>To use this for networking you must perform encapsulation and routing at the OSI Application level (not efficie... | 2017-04-19 20:53:29.257000+00:00 | 2017-04-19 20:53:29.257000+00:00 | null | null | 36,867,687 | <p>I have two Android KitKat phones, both are running WiFi-Direct groups as Group Owners, let's call them GO1 and GO2</p>
<p>I managed to connect GO1 as a legacy client to GO2 without breaking any of the (previously set) wifi-direct groups.</p>
<p>The problem is that, as you might know, the GO IP address is hardcoded... | 2016-04-26 14:19:00.243000+00:00 | 2017-04-19 20:53:29.257000+00:00 | 2016-04-27 10:52:02.297000+00:00 | android|ip|ipv6|wifi-direct|wifip2p | ['https://arxiv.org/pdf/1601.00028.pdf'] | 1 |
48,436,520 | <p>You might be interested in my paper <a href="https://arxiv.org/pdf/1801.07779.pdf" rel="noreferrer">The WiLI benchmark dataset for written
language identification</a>. I also benchmarked a couple of tools.</p>
<p>TL;DR:</p>
<ul>
<li>CLD-2 is pretty good and extremely fast</li>
<li><a href="https://pypi.python.org/... | 2018-01-25 05:58:05.390000+00:00 | 2018-02-06 05:43:02.820000+00:00 | 2018-02-06 05:43:02.820000+00:00 | null | 43,377,265 | <p>I am using both <a href="http://www.nltk.org/" rel="noreferrer">Nltk</a> and <a href="http://scikit-learn.org/stable/" rel="noreferrer">Scikit Learn</a> to do some text processing. However, within my list of documents I have some documents that are not in English. For example, the following could be true:</p>
<pre>... | 2017-04-12 18:41:32.477000+00:00 | 2022-04-14 01:45:44.743000+00:00 | 2017-11-29 08:27:32.963000+00:00 | python|scikit-learn|nlp|nltk | ['https://arxiv.org/pdf/1801.07779.pdf', 'https://pypi.python.org/pypi/langdetect?', 'https://github.com/MartinThoma/lidtk'] | 3 |
51,525,403 | <p><strong>Segmentation Accuracy</strong></p>
<p>This is a pretty common problem addressed in image segmentation literature, e.g., <a href="https://stackoverflow.com/questions/13974167/how-to-test-accuracy-of-segmentation-algorithm">here is a StackOverflow post</a></p>
<p>One common approach is to consider the ratio ... | 2018-07-25 18:21:27.220000+00:00 | 2018-08-03 18:39:27.487000+00:00 | 2018-08-03 18:39:27.487000+00:00 | null | 51,525,036 | <p>I have implemented several clustering algorithms on an image dataset.
I'm interested in deriving the success rate of clustering. I have to detect the tumor area, in the original image I know where the tumor is located, I would like to compare the two images and obtain the percentage of success.
Following images:</p>... | 2018-07-25 17:56:05.493000+00:00 | 2018-08-03 18:39:27.487000+00:00 | 2018-07-27 05:45:45.623000+00:00 | python|image-processing|cluster-analysis|analysis | ['https://stackoverflow.com/questions/13974167/how-to-test-accuracy-of-segmentation-algorithm', 'https://arxiv.org/abs/1703.06870', 'http://www.cs.cmu.edu/~aayushb/pixelNet/', 'https://dspace2.flinders.edu.au/xmlui/bitstream/handle/2328/27165/Powers%20Evaluation.pdf?sequence=1&isAllowed=y', 'https://en.wikipedia.org/wi... | 31 |
47,011,724 | <p>This is normal behaviour and happens because your network is too confident of the quality of the input and doesn't learn to rely on the past (on it's internal state) enough, relying soley on the input. When you apply the network to its own output in the generation setting, the input to the network is not as reliable... | 2017-10-30 09:24:15.610000+00:00 | 2017-10-30 09:24:15.610000+00:00 | null | null | 43,459,013 | <p>For several days now, I am trying to build a simple sine-wave sequence generation using LSTM, without any glimpse of success so far.</p>
<p>I started from the <a href="https://github.com/pytorch/examples/tree/master/time_sequence_prediction" rel="noreferrer">time sequence prediction example</a></p>
<p>All what I w... | 2017-04-17 20:23:11.273000+00:00 | 2018-03-07 08:36:10.770000+00:00 | 2018-03-07 08:36:10.770000+00:00 | python|machine-learning|deep-learning|lstm|pytorch | ['https://arxiv.org/abs/1506.03099'] | 1 |
55,873,035 | <h2>Update</h2>
<p><a href="https://github.com/apple/swift/pull/25286" rel="nofollow noreferrer">This</a> implementation of a random number generator in an interval has been merged into the standard library and should perform better than before:</p>
<pre><code>// s = upperBound; r1, r2 = random numbers from generator... | 2019-04-26 18:17:09.863000+00:00 | 2019-07-23 21:01:31.747000+00:00 | 2019-07-23 21:01:31.747000+00:00 | null | 55,872,415 | <p>I have used Int.random() method and arc4random_uniform() for number generation speed tests.<br>
Both tests were run in macOS console with build configuration set to release.
Below are codes which I have used for testing. </p>
<pre><code>public func randomGen1() {
let n = 1_000_000
let startTime = CFAbsolute... | 2019-04-26 17:26:41.613000+00:00 | 2019-07-23 21:01:31.747000+00:00 | 2019-04-26 17:33:32.537000+00:00 | swift|random | ['https://github.com/apple/swift/pull/25286', 'https://stackoverflow.com/questions/55548872/shuffle-struct-by-int/55549494#55549494', 'http://xoshiro.di.unimi.it/', 'https://github.com/mattgallagher/CwlUtils/blob/e4186dae4ba55ffa478264c8477d01a48fd2b459/Sources/CwlUtils/CwlRandom.swift#L80', 'https://lemire.me/blog/201... | 9 |
59,938,045 | <p>40% accuracy is not good. It needs to train more. You should rescale images to <code>128 or 256</code> to save time. Also try increasing epoch count to something like 100 or minimize loss to at least around 1 before testing. Another thing is class imbalance. </p>
<p>According to this, <a href="https://arxiv.org/abs... | 2020-01-27 19:57:38.153000+00:00 | 2020-01-27 19:57:38.153000+00:00 | null | null | 59,937,540 | <p>I am following this guide to learn image classification with neural networks:</p>
<p><a href="https://www.tensorflow.org/tutorials/keras/classification" rel="nofollow noreferrer">https://www.tensorflow.org/tutorials/keras/classification</a></p>
<p>And I implement this code for my custom dataset. I have 2300 gray s... | 2020-01-27 19:19:10.547000+00:00 | 2020-01-28 16:09:05.880000+00:00 | 2020-01-28 16:09:05.880000+00:00 | python|tensorflow|machine-learning|keras|neural-network | ['https://arxiv.org/abs/1708.07747'] | 1 |
62,836,902 | <p>This question have been answered by <a href="https://arxiv.org/pdf/1605.05274.pdf" rel="nofollow noreferrer">R.Grigore(2016)</a> in his paper <em>Java Generics are Turing Complete</em></p>
<p>Take the following Java code as an example to his suggestion:</p>
<pre><code>//an empty interface
interface Z {}
//4 generic... | 2020-07-10 14:53:30.250000+00:00 | 2020-07-10 14:53:30.250000+00:00 | null | null | 3,451,519 | <p>The JLS mentions in the type inference algorithm (§15.12.2):</p>
<blockquote>
<p>It is possible that the process above yields an infinite type. This is permissible,
and Java compilers must recognize such situations and represent them appropriately using cyclic data structures.</p>
</blockquote>
<p>However, I'm... | 2010-08-10 17:08:20.650000+00:00 | 2020-07-10 14:53:30.250000+00:00 | 2010-08-11 13:28:39.157000+00:00 | java|programming-languages|wildcard|type-inference | ['https://arxiv.org/pdf/1605.05274.pdf'] | 1 |
60,628,756 | <p>I like your approach! When you mention your optimization I think a good way to go about it is by rotating the hexagonal grid and translating it till you find the least amount of circles that cover the region. You don't need to rotate 360 since the pattern is symmetric so just 360/6.</p>
<p>I've been working on this... | 2020-03-11 03:15:12.600000+00:00 | 2020-03-13 00:59:05.403000+00:00 | 2020-03-13 00:59:05.403000+00:00 | null | 10,648,621 | <p><strong>The following problem:</strong>
Given is an arbitrary polygon. It shall be covered 100% with the minimum number of circles of a given radius.</p>
<p><strong>Note:</strong>
1) Naturally the circles have to overlap.
2) I try to solve the problem for ARBITRARY polygons. But also solutions for CONVEX polygons a... | 2012-05-18 07:40:55.300000+00:00 | 2020-03-13 00:59:05.403000+00:00 | 2017-05-23 12:32:32.410000+00:00 | matlab|geometry | ['https://arxiv.org/abs/2003.04839'] | 1 |
46,173,528 | <p>You can use the idea of face-embeddings, which for example is proposed in the highly-cited paper <a href="https://arxiv.org/abs/1503.03832" rel="noreferrer">FaceNet</a> and implemented in <a href="https://cmusatyalab.github.io/openface/" rel="noreferrer">OpenFace</a> (which also comes pre-trained).</p>
<p>The genera... | 2017-09-12 10:03:32.393000+00:00 | 2017-09-12 10:03:32.393000+00:00 | 2020-06-20 09:12:55.060000+00:00 | null | 46,168,182 | <p>First of all here is my <a href="https://github.com/alucard001/OpenCV-Face-Recognition-and-Comparison/blob/master/Open%20CV.ipynb" rel="noreferrer">github link for the question</a>.</p>
<p>And here is my question:</p>
<p>I would like to do a face comparison function using Python. And I can successfully(?) recogni... | 2017-09-12 04:55:55.683000+00:00 | 2021-10-19 14:41:04.830000+00:00 | 2018-09-27 10:02:08.430000+00:00 | python|opencv|neural-network|convolution|face-recognition | ['https://arxiv.org/abs/1503.03832', 'https://cmusatyalab.github.io/openface/', 'https://cmusatyalab.github.io/openface/demo-2-comparison/'] | 3 |
58,753,908 | <p>In the original paper, the author pushes one sample to the experience replay buffer and randomly samples 32 transitions to train the model in the minibatch fashion. The samples took from interacting with the environment is not directly feeding to the model. To increase the speed of training, the author store samples... | 2019-11-07 17:13:49.143000+00:00 | 2019-11-09 14:55:36.403000+00:00 | 2019-11-09 14:55:36.403000+00:00 | null | 58,600,089 | <p>I am confused why dqn with experience replay algorithm would perform gradient descent step for every step in a given episode? This will fit only one step, right? This would make it extremely slow. Why not after each episode ends or every time the model is cloned?</p> | 2019-10-29 00:37:53.163000+00:00 | 2019-11-09 14:55:36.403000+00:00 | 2019-10-29 01:02:33.857000+00:00 | deep-learning|reinforcement-learning | ['https://github.com/openai/baselines', 'https://arxiv.org/abs/1803.00933', 'https://arxiv.org/abs/1803.00933', 'https://arxiv.org/abs/1507.04296', 'https://web.stanford.edu/class/psych209/Readings/MnihEtAlHassibis15NatureControlDeepRL.pdf'] | 5 |
36,406,057 | <p>in my experience, NaNs, when training a network usually happen because of two problems:</p>
<ul>
<li>first, mathematical error, e.g. log of negative value. It could happen when you are using log() in your loss function.</li>
<li>Second, there is a value that becomes too big so python can't handle.</li>
</ul>
<p>In... | 2016-04-04 14:54:07.443000+00:00 | 2016-04-06 22:30:51.983000+00:00 | 2016-04-06 22:30:51.983000+00:00 | null | 36,381,488 | <p>I am training a simple feed-forward model with 3 or 4 hidden layers and dropouts between each (hidden layer + non linearity) combination.
Sometimes after a few epochs (about 10-11) the model starts outputting Infs and NaNs as the error of the NLL and the accuracy falls to 0.0%. This problem does not happen when I d... | 2016-04-03 04:03:04.203000+00:00 | 2016-04-06 22:30:51.983000+00:00 | null | python|theano|deep-learning | ['http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf', 'http://arxiv.org/abs/1502.01852'] | 2 |
46,021,189 | <blockquote>
<p>use a kd-tree</p>
</blockquote>
<p>Unfortunately, in high dimensions this data structure suffers severely from the <a href="https://en.wikipedia.org/wiki/Curse_of_dimensionality" rel="nofollow noreferrer">curse of dimensionality</a>, which causes its search time to be comparable to the brute force se... | 2017-09-03 07:25:31.820000+00:00 | 2017-09-03 07:25:31.820000+00:00 | null | null | 3,962,775 | <p>So I have about 16,000 75-dimensional data points, and for each point I want to find its k nearest neighbours (using euclidean distance, currently k=2 if this makes it easiser)</p>
<p>My first thought was to use a kd-tree for this, but as it turns out they become rather inefficient as the number of dimension grows.... | 2010-10-18 19:46:36.487000+00:00 | 2017-09-03 07:26:10.790000+00:00 | 2017-09-03 07:26:10.790000+00:00 | algorithm|data-structures|computational-geometry|nearest-neighbor|dimensionality-reduction | ['https://en.wikipedia.org/wiki/Curse_of_dimensionality', 'https://en.wikipedia.org/wiki/Dimensionality_reduction', 'https://en.wikipedia.org/wiki/Dimensionality_reduction#Principal_component_analysis_.28PCA.29', 'https://en.wikipedia.org/wiki/Nearest_neighbor_search#Approximate_nearest_neighbor', 'https://arxiv.org/pd... | 8 |
61,577,834 | <p>Document based databases have a big advantage over relational databases as they do not require defining a schema upfront- before being able to enter any data.</p>
<p>Also, you should use a document database if your data is not relational and cannot be stored in a table but rather is a set of images, or for example n... | 2020-05-03 16:22:55.093000+00:00 | 2022-03-11 08:28:16.757000+00:00 | 2022-03-11 08:28:16.757000+00:00 | null | 441,441 | <p>Why should I use document based database like CouchDB instead of using relational database.
Are there any typical kinds of applications or domains where the document based database is more suitable than the relational database?</p> | 2009-01-14 00:21:48.840000+00:00 | 2022-04-07 21:05:03.183000+00:00 | null | database|couchdb|relational|non-relational-database | ['https://arxiv.org/ftp/arxiv/papers/1509/1509.08035.pdf'] | 1 |
71,943,162 | <p>There is this interesting paper <a href="https://arxiv.org/pdf/1802.06222.pdf" rel="nofollow noreferrer">Efficient GAN-based anomaly detection</a>.<br />
To evaluate the anomaly detection, they use the following experimental setting</p>
<blockquote>
<p>MNIST: We generated 10 different datasets from MNIST by successi... | 2022-04-20 16:28:32.400000+00:00 | 2022-04-20 16:28:32.400000+00:00 | null | null | 71,942,290 | <p>There's something about GAN's training that i don't understand. I am making a GAN for Anomaly Detection. To start I followed this guide <a href="https://www.tensorflow.org/tutorials/generative/dcgan" rel="nofollow noreferrer">here</a> to create a DCGAN (and understand how it works) and then move into the Anomaly Det... | 2022-04-20 15:24:05.590000+00:00 | 2022-04-20 16:28:32.400000+00:00 | null | python|tensorflow|deep-learning|generative-adversarial-network|anomaly-detection | ['https://arxiv.org/pdf/1802.06222.pdf'] | 1 |
57,136,880 | <p>L2 utilization and hit rate are orthogonal concepts.</p>
<p>L2 utilization % measures how many operations (reads/writes/atomics) the L2 cache performed, compared to its peak performance. You can alternatively think of this as a proxy for "how much L2 bandwidth did I use" given there is a fixed bandwidth between L1... | 2019-07-21 20:42:24.077000+00:00 | 2019-07-21 20:42:24.077000+00:00 | null | null | 57,135,152 | <p>I'm doing expriments by using cuda.</p>
<p>I thought that if L2 cache hit ratio is high, performance will increase.</p>
<p>However, from nvprof, L2 cache utilization is low even though L2 cache hit rate is about 93%.</p>
<p>Why this happens? Are there examples that make it happen?</p> | 2019-07-21 16:47:08.780000+00:00 | 2019-07-21 20:42:24.077000+00:00 | null | caching|cuda|gpu | ['https://arxiv.org/pdf/1903.07486.pdf', 'https://i.stack.imgur.com/k3aG1.png', 'https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions', 'https://docs.nvidia.com/cuda/parallel-thread-execution/index.html#data-movement-and-conversion-instructions', 'https://docs.nvid... | 7 |
70,006,822 | <p>So, In this paper : <a href="https://arxiv.org/pdf/2004.07464.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2004.07464.pdf</a>
They have combined image embedding and text embedding by concatenating them.</p>
<pre><code>X = TE + IE
</code></pre>
<p>Here X is fusion embedding with TE and IE as text and image e... | 2021-11-17 15:04:52.033000+00:00 | 2021-11-17 15:04:52.033000+00:00 | null | null | 44,786,174 | <p>I know the meaning and methods of word embedding(skip-gram, CBOW) completely. And I know, that Google has a word2vector API that by getting the word can produce the vector.
but my problem is this: we have a clause that includes the subject, object, verb... that each word is previously embedded by the Google API, no... | 2017-06-27 17:12:36.157000+00:00 | 2021-11-17 15:04:52.033000+00:00 | null | nlp|information-retrieval|word2vec|google-api-python-client|word-embedding | ['https://arxiv.org/pdf/2004.07464.pdf', 'https://arxiv.org/abs/1708.03629', 'https://github.com/vyraun/Half-Size'] | 3 |
44,093,296 | <p>There is nothing wrong. The problem is that increasing layers does not automatically means a higher accuracy (otherwise machine learning would be kind of solved, because if you need a better accuracy in an image classifier you would just add +1 layer to an inception and claim a victory).</p>
<p>To show you that thi... | 2017-05-21 04:47:03.857000+00:00 | 2017-05-21 04:47:03.857000+00:00 | null | null | 44,092,936 | <p>I'm learning TensorFlow, and trying to create a simple two layer neural network.</p>
<p>The tutorial code <a href="https://www.tensorflow.org/get_started/mnist/pros" rel="nofollow noreferrer">https://www.tensorflow.org/get_started/mnist/pros</a> starts with this simple network, to get 92% accuracy:</p>
<pre><code>... | 2017-05-21 03:40:05.703000+00:00 | 2017-05-21 04:47:03.857000+00:00 | null | machine-learning|tensorflow|neural-network | ['https://arxiv.org/pdf/1512.03385.pdf', 'https://i.stack.imgur.com/msoTt.jpg'] | 2 |
46,787,296 | <p>The fact that the model trains on its own predictions is the whole point of Q-learning: it is a concept called bootstrapping, which means reusing your experience. The insight behind this is:</p>
<ul>
<li>The Agent is initialized with some weights</li>
<li>These weights represent the Agent's current representation o... | 2017-10-17 09:50:12.563000+00:00 | 2017-10-17 10:28:19.490000+00:00 | 2017-10-17 10:28:19.490000+00:00 | null | 46,783,760 | <p>When I am training my model I have the following segment:</p>
<pre><code>s_t_batch, a_batch, y_batch = train_data(minibatch, model2)
# perform gradient step
loss.append(model.train_on_batch([s_t_batch, a_batch], y_batch))
</code></pre>
<p>where <code>s_t, a_</code> corresponds to current states and actions that we... | 2017-10-17 06:25:25.803000+00:00 | 2019-10-19 08:00:48.420000+00:00 | 2019-10-19 08:00:48.420000+00:00 | deep-learning|reinforcement-learning|openai-gym|q-learning | ['https://en.wikipedia.org/wiki/Temporal_difference_learning', 'https://medium.com/emergent-future/simple-reinforcement-learning-with-tensorflow-part-0-q-learning-with-tables-and-neural-networks-d195264329d0', 'http://arxiv.org/abs/1701.07274', 'http://vict0rsch.github.io/thesis/thesisVictorSchmidt.pdf'] | 4 |
18,682,668 | <p>First of all, take a look on File size, here is detailed <a href="http://arxiv.org/pdf/cs/0502012.pdf" rel="nofollow">Performance measurements</a></p> | 2013-09-08 10:26:03.417000+00:00 | 2013-09-08 10:26:03.417000+00:00 | null | null | 18,681,936 | <p>I have a requirement to load a file containing up to 1 million lines of string data. My first thought is to use C# 5.0 async to load the data whilst not blocking the UI thread. If the user tries to access something that relies on the data they will get a loading message.</p>
<p>Still I would like the fastest possib... | 2013-09-08 08:38:16.257000+00:00 | 2013-09-08 19:10:23.917000+00:00 | 2013-09-08 19:10:23.917000+00:00 | c# | ['http://arxiv.org/pdf/cs/0502012.pdf'] | 1 |
52,743,448 | <p>The general answer whenever it comes to the question of "which is faster?" is always: measure how fast each approach runs your application scenario to find out. In this case, I would say that the first approach would seem preferable most of the time (if you had to pick one of those two options for some reason). Unle... | 2018-10-10 15:12:21.693000+00:00 | 2018-10-10 15:12:21.693000+00:00 | null | null | 52,729,965 | <p>Based on my study, there are 2 different strategies to implement tiled version of convolution with cuda. I want to know more about this, and would like to see how they compare with each other, what is the advantage and disadvantage of each strategy, and how to choose. Below is the implementations of the two differen... | 2018-10-09 22:02:59.140000+00:00 | 2018-10-10 15:12:21.693000+00:00 | 2018-10-09 22:17:29.353000+00:00 | c++|3d|cuda|deep-learning|convolution | ['https://petewarden.com/2015/04/20/why-gemm-is-at-the-heart-of-deep-learning/', 'https://arxiv.org/abs/1509.09308'] | 2 |
69,508,740 | <p>Looks great, as you have already followed most of the solutions to resolve gradient exploding problem. Below is the list of all solutions you can try</p>
<p><strong>Solutions to avoid Gradient Exploding problem</strong></p>
<ol>
<li><p><em>Appropriate Weight initialization:</em> utilise appropriate weight Initializa... | 2021-10-09 16:42:13.367000+00:00 | 2021-10-29 16:33:42.460000+00:00 | 2021-10-29 16:33:42.460000+00:00 | null | 69,427,103 | <p>I have a gradient exploding problem which I couldn't solve after trying for several days. I implemented a custom message passing graph neural network in TensorFlow which is used to predict a continuous value from graph data. Each graph is associated with one target value. Each node of a graph is represented by a nod... | 2021-10-03 17:05:28.273000+00:00 | 2021-10-29 16:33:42.460000+00:00 | 2021-10-24 12:36:43.910000+00:00 | python|tensorflow|machine-learning|keras|gradient | ['https://arxiv.org/abs/1502.03167', 'https://keras.io/api/optimizers/', 'https://machinelearningmastery.com/gentle-introduction-backpropagation-time/', 'https://keras.io/api/layers/regularizers/'] | 4 |
57,385,673 | <p>The Batch normalization in LSTM is not that easy to implement. Some papers present some amazing results <a href="https://arxiv.org/pdf/1603.09025.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1603.09025.pdf</a> called Recurrent Batch normalization. The authors apply following the equations</p>
<p><a href="ht... | 2019-08-07 00:55:29.967000+00:00 | 2019-08-07 00:55:29.967000+00:00 | null | null | 48,544,953 | <p>I am trying to use batch normalization in LSTM using keras in R. In my dataset the target/output variable is the <code>Sales</code> column, and every row in the dataset records the <code>Sales</code> for each day in a year (2008-2017). The dataset looks like below:</p>
<p><a href="https://i.stack.imgur.com/mFJgq.pn... | 2018-01-31 14:49:03.390000+00:00 | 2019-08-07 00:55:29.967000+00:00 | null | r|tensorflow|keras|recurrent-neural-network|batch-normalization | ['https://arxiv.org/pdf/1603.09025.pdf', 'https://i.stack.imgur.com/2FhjQ.png', 'https://github.com/OlavHN/bnlstm'] | 3 |
60,792,910 | <p>Target tracking is a very difficult problem. In target tracking you will have <strong>two main issues</strong>: the motion uncertainty problem, and the origin uncertainty problem. The first one refers to the way you model object motion so you can predict its future state, and the second refers to the issue of data a... | 2020-03-21 20:27:41.103000+00:00 | 2020-03-21 20:34:19.133000+00:00 | 2020-03-21 20:34:19.133000+00:00 | null | 60,592,851 | <p>I'm trying to create an application that will be able to track rapidly moving objects in video/camera feed, however have not found any CV/DL solution that is good enough. Can you recommend any computer vision solution for tracking fast moving objects on regular laptop computer and web cam? A demo app would be ideal.... | 2020-03-08 22:54:46.580000+00:00 | 2020-06-08 11:39:46.317000+00:00 | null | opencv|deep-learning | ['https://www.mdpi.com/1424-8220/20/4/1110', 'https://arxiv.org/abs/1511.05121', 'https://towardsdatascience.com/the-unscented-kalman-filter-anything-ekf-can-do-i-can-do-it-better-ce7c773cf88d', 'https://stackoverflow.com/questions/2764238/image-processing-what-are-occlusions/60644446#60644446'] | 4 |
72,182,806 | <p>As you said, <code>train_test_split</code> interprets each list of tags as a label, it doesn't matter what it contains. A sample with tags <code>[1, 2, 3]</code> will not be identified the same as a sample with tags <code>[1, 2]</code>. Hence, you cannot flatten the <code>tags</code> column to check the label counts... | 2022-05-10 08:14:05.597000+00:00 | 2022-05-10 08:14:05.597000+00:00 | null | null | 72,182,217 | <p>I have multilabel dataset (<code>pd.DataFrame</code>) which looks like this:
<a href="https://i.stack.imgur.com/AuOxq.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/AuOxq.png" alt="" /></a></p>
<p>This is value_counts of flatten <code>tags</code> column:</p>
<pre><code>101 4450171
86 393... | 2022-05-10 07:26:35.447000+00:00 | 2022-05-10 08:14:05.597000+00:00 | 2022-05-10 07:56:21.367000+00:00 | python|pandas|scikit-learn|split|dataset | [] | 0 |
66,679,501 | <p>I was able to bring your code to a version where it would at least converge. In summary, I think there might be multiple problems with it: the normalization (why those values?), some unnecessary relus, too high learning rate, MSE loss instead of cross-entropy and mainly I don't think the softmax in the bottleneck la... | 2021-03-17 18:55:39.553000+00:00 | 2021-03-18 12:25:51.280000+00:00 | 2021-03-18 12:25:51.280000+00:00 | null | 66,667,949 | <p>I'm trying to build a simple autoencoder for MNIST, where the middle layer is just 10 neurons. My hope is that it will learn to classify the 10 digits, and I assume that would lead to the lowest error in the end (wrt reproducing the original image).</p>
<p>I have the following code, which I've already played around ... | 2021-03-17 06:22:52.777000+00:00 | 2021-03-19 01:09:50.773000+00:00 | 2021-03-17 09:53:51.540000+00:00 | python|pytorch|autoencoder|mnist | ['https://www.quora.com/Does-anyone-ever-use-a-softmax-layer-mid-neural-network-rather-than-at-the-end', 'https://arxiv.org/abs/1611.01144', 'https://arxiv.org/abs/1609.02200'] | 3 |
41,315,489 | <p>Stochastic Gradient Descent seems to require significant overparameterization in order to learn, here's one paper along those lines -- <a href="https://arxiv.org/abs/1301.3583" rel="nofollow noreferrer">"Big Neural Networks Waste Capacity"</a></p> | 2016-12-24 17:33:52.760000+00:00 | 2016-12-24 17:33:52.760000+00:00 | null | null | 41,314,819 | <p>I think I'm missing something obvious here but would love some help figuring this out. </p>
<p>Say I have a million words and want to embed them as part of my model.
With TF I can do an embedding lookup, though I need to provide a matrix of size [1m*space_size]. So for 50 dimensions that comes out to 50M trainable... | 2016-12-24 16:08:11.587000+00:00 | 2016-12-26 19:19:35.393000+00:00 | null | tensorflow|deep-learning | ['https://arxiv.org/abs/1301.3583'] | 1 |
45,297,317 | <p>As of SUMO 0.29.0, acceleration is not one of the <a href="http://www.sumo.dlr.de/wiki/TraCI/Vehicle_Value_Retrieval" rel="nofollow noreferrer">variables exposed by the SUMO TraCI API of a vehicle</a> - primarily because it is not one of the state variables of the most common car following models.</p>
<p>You will n... | 2017-07-25 08:01:40.243000+00:00 | 2017-07-25 08:01:40.243000+00:00 | null | null | 45,287,511 | <p>I am using <code>veins-4.5</code> <code>omnet++ 5</code> and <code>sumo 0.29.0</code>.
How can I access the <em>acceleration</em> of a vehicle in veins?</p>
<p>Thanks a lot.</p> | 2017-07-24 18:23:29.063000+00:00 | 2017-07-25 08:01:40.243000+00:00 | 2017-07-25 06:27:26.650000+00:00 | omnet++|veins | ['http://www.sumo.dlr.de/wiki/TraCI/Vehicle_Value_Retrieval', 'http://arxiv.org/abs/1403.4881'] | 2 |
55,811,323 | <p>This problem is a well studied problem: the problem of <strong>Journey planning in public transportation networks</strong>.<br>
Your approach based on Bellman-Ford might become problematic and too expensive depending on the network since you can't consider that a vertex has been 'visited', or that the shortest path ... | 2019-04-23 12:33:19.597000+00:00 | 2019-04-23 12:33:19.597000+00:00 | null | null | 55,802,336 | <p>So suppose you are searching for a train ride. You would be interested in the price of the ride and also the amount of time the ride will take. Now suppose that you have a graph where each edge has a cost and a duration and you want to find the shortest duration path in the graph that doesn't go over a given maximum... | 2019-04-22 23:27:57.550000+00:00 | 2019-04-23 23:59:48.697000+00:00 | 2019-04-23 23:59:48.697000+00:00 | c++|algorithm|bellman-ford | ['http://www.filipyoo.com/multi-objectives-shortest-paths-algorithms-for-multi-transfer-flight-routes/', 'https://arxiv.org/pdf/1504.05140.pdf', 'https://i11www.iti.kit.edu/extra/publications/dpsw-isftr-13.pdf'] | 3 |
29,853,535 | <p>Yes, the <a href="http://eclipseclp.org" rel="noreferrer" title="ECLiPSe">ECLiPSe</a> system does this.</p>
<p>As you suggest, it takes into account a number of simple built-in predicates (such as <code>integer/1, =/2, !/0</code>) for indexing purposes. Your example then executes deterministically, without choicep... | 2015-04-24 17:11:33.927000+00:00 | 2015-04-25 17:04:36.530000+00:00 | 2015-04-25 17:04:36.530000+00:00 | null | 29,605,132 | <p>I want to know how smart first argument indexing is implemented on various Prolog implementations.</p>
<p>In particular, simple type-test goals like <code>integer/1</code> right after a clause "neck" <em>could</em> contribute to better indexing.
Consider:</p>
<pre><code>foo(h(X),X).
foo([],nil).
foo([_|_],cons).
f... | 2015-04-13 12:18:26.433000+00:00 | 2019-01-27 16:03:04.750000+00:00 | 2015-04-13 17:12:52.820000+00:00 | indexing|prolog | ['http://eclipseclp.org', 'http://arxiv.org/abs/1012.4240'] | 2 |
20,955,187 | <p>An alternative approach is to use something like generative backpropagation. In this scenario, you train a neural network updating the weights AND the input values. The given values are used as the output values since you can compute an error value directly. This approach has been used in dimensionality reduction, m... | 2014-01-06 17:02:27.733000+00:00 | 2014-01-06 17:02:27.733000+00:00 | null | null | 15,514,618 | <p>I'm having trouble with some of the concepts in machine learning through neural networks. One of them is <a href="http://en.wikipedia.org/wiki/Delta_Rule" rel="noreferrer">backpropagation</a>. In the weight updating equation, </p>
<pre><code>delta_w = a*(t - y)*g'(h)*x
</code></pre>
<p><code>t</code> is the "targe... | 2013-03-20 03:12:20.327000+00:00 | 2019-04-02 08:42:01.440000+00:00 | 2017-04-19 04:50:59.180000+00:00 | machine-learning|neural-network|unsupervised-learning | ['http://bioinformatics.oxfordjournals.org/cgi/reprint/21/20/3887', 'http://arxiv.org/abs/1312.5394'] | 2 |
15,514,709 | <p>The most common thing to do is train <a href="http://en.wikipedia.org/wiki/Autoencoder" rel="noreferrer">an autoencoder</a>, where the desired outputs are equal to the inputs. This makes the network try to learn a representation that best "compresses" the input distribution.</p>
<p><a href="http://www.freepatentson... | 2013-03-20 03:22:39.767000+00:00 | 2013-03-20 03:22:39.767000+00:00 | null | null | 15,514,618 | <p>I'm having trouble with some of the concepts in machine learning through neural networks. One of them is <a href="http://en.wikipedia.org/wiki/Delta_Rule" rel="noreferrer">backpropagation</a>. In the weight updating equation, </p>
<pre><code>delta_w = a*(t - y)*g'(h)*x
</code></pre>
<p><code>t</code> is the "targe... | 2013-03-20 03:12:20.327000+00:00 | 2019-04-02 08:42:01.440000+00:00 | 2017-04-19 04:50:59.180000+00:00 | machine-learning|neural-network|unsupervised-learning | ['http://en.wikipedia.org/wiki/Autoencoder', 'http://www.freepatentsonline.com/5590218.html', 'http://arxiv.org/pdf/cs/0608115.pdf', 'http://www.rimtengg.com/coit2007/proceedings/pdfs/40.pdf'] | 4 |
47,311,604 | <p>The fundamental difficulty when it comes to adding new users in your system is that you need retraining to be able to give meaningful predictions to new users. Even if you were able to dynamically resize the embedding matrices, what values would you use for the parameters describing the new user?</p>
<p>Taking this... | 2017-11-15 15:43:49.173000+00:00 | 2017-11-15 15:43:49.173000+00:00 | null | null | 47,272,031 | <p>I have a TensorFlow recommendation system based off <a href="https://github.com/songgc/TF-recomm" rel="nofollow noreferrer"><code>TF-recomm</code></a>. Each user has <code>1+numFactors</code> numbers associated with her: a vector of <code>numFactors</code>, and an offset of a single number. Each task also has a bias... | 2017-11-13 19:26:54.860000+00:00 | 2017-11-15 15:43:49.173000+00:00 | null | python|tensorflow|recommendation-engine | ['https://pdfs.semanticscholar.org/6c02/053805434162e0fed26e1d5e035eb1071249.pdf', 'https://arxiv.org/pdf/1511.06939.pdf', 'https://github.com/mesuvash/NNRec', 'https://maciejkula.github.io/spotlight/sequence/implicit.html'] | 4 |
65,950,643 | <p>Here is a more efficient and more stable implementation. Assuming <code>zi</code> and <code>zj</code> are interlaced!</p>
<pre><code>class NT_Xent(tf.keras.layers.Layer):
""" Normalized temperature-scaled CrossEntropy loss [1]
[1] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple... | 2021-01-29 07:55:30.137000+00:00 | 2021-12-27 15:29:01.250000+00:00 | 2021-12-27 15:29:01.250000+00:00 | null | 62,793,043 | <p>As the title suggests, I'm trying train a model based on the SimCLR framework (seen in this paper: <a href="https://arxiv.org/pdf/2002.05709.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2002.05709.pdf</a> - the NT_Xent loss is stated in equation (1) and Algorithm 1).</p>
<p>I have managed to create a numpy v... | 2020-07-08 10:45:50.177000+00:00 | 2021-12-27 15:29:01.250000+00:00 | null | python|tensorflow|scikit-learn|backpropagation|cosine-similarity | ['https://github.com/gabriel-vanzandycke/tf_layers'] | 1 |
62,793,878 | <p>I managed to figure it out myself!
I did not realise there was a Tensorflow implementation of the cosine similarity function "tf.keras.losses.CosineSimilarity"</p>
<p>Here is my code:</p>
<pre><code>import tensorflow as tf
# Define the contrastive loss function, NT_Xent (Tensorflow version)
def NT_Xent_tf... | 2020-07-08 11:33:39.013000+00:00 | 2020-07-08 11:33:39.013000+00:00 | null | null | 62,793,043 | <p>As the title suggests, I'm trying train a model based on the SimCLR framework (seen in this paper: <a href="https://arxiv.org/pdf/2002.05709.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/2002.05709.pdf</a> - the NT_Xent loss is stated in equation (1) and Algorithm 1).</p>
<p>I have managed to create a numpy v... | 2020-07-08 10:45:50.177000+00:00 | 2021-12-27 15:29:01.250000+00:00 | null | python|tensorflow|scikit-learn|backpropagation|cosine-similarity | [] | 0 |
69,420,043 | <p>Hyperparameter tuning is typically done on the validation set of a train-val-test split, where each split will have something along the lines of 70%, 10%, and 20% of the entire dataset respectively. As a baseline, random search can be used while <a href="https://arxiv.org/abs/1206.2944" rel="nofollow noreferrer">Bay... | 2021-10-02 20:29:58.507000+00:00 | 2021-10-02 20:29:58.507000+00:00 | null | null | 69,419,809 | <p>I almost finished my time series model, collected enough data and now I am stuck at hyperparameter optimization.</p>
<p>And after lots of googling I found new & good library called ultraopt, but problem is that how much amount of fragment of data should I use from my total data (~150 GB) for hyperparameter tunin... | 2021-10-02 19:55:56.360000+00:00 | 2021-10-03 08:58:59.760000+00:00 | 2021-10-03 08:58:59.760000+00:00 | python|performance|machine-learning|large-data|hyperparameters | ['https://arxiv.org/abs/1206.2944', 'https://scikit-optimize.github.io/stable/auto_examples/bayesian-optimization.html'] | 2 |
69,420,445 | <p>A good python library for hyper-parameter tuning is <a href="https://arxiv.org/pdf/1603.06560.pdf" rel="nofollow noreferrer"><code>keras tuner</code></a>. You can utilize different tuners in this library, but for the large data, as you've mentioned, <a href="https://arxiv.org/pdf/1603.06560.pdf" rel="nofollow norefe... | 2021-10-02 21:37:08.380000+00:00 | 2021-10-02 21:37:08.380000+00:00 | null | null | 69,419,809 | <p>I almost finished my time series model, collected enough data and now I am stuck at hyperparameter optimization.</p>
<p>And after lots of googling I found new & good library called ultraopt, but problem is that how much amount of fragment of data should I use from my total data (~150 GB) for hyperparameter tunin... | 2021-10-02 19:55:56.360000+00:00 | 2021-10-03 08:58:59.760000+00:00 | 2021-10-03 08:58:59.760000+00:00 | python|performance|machine-learning|large-data|hyperparameters | ['https://arxiv.org/pdf/1603.06560.pdf', 'https://arxiv.org/pdf/1603.06560.pdf'] | 2 |
64,588,418 | <p>Remember that fine-tuning a pre-trained model like Bert usually requires a much smaller number of epochs than models trained from scratch. In fact <a href="https://arxiv.org/pdf/1810.04805.pdf" rel="nofollow noreferrer">the authors of Bert recommend between 2 and 4 epochs</a>. Further training often translates to ov... | 2020-10-29 09:37:58.480000+00:00 | 2020-10-29 09:37:58.480000+00:00 | null | null | 63,096,908 | <p>I'm training a classification model with custom layers on top of BERT. During this, the training performance of this model is going down with increasing epochs ( after the first epoch ) .. I'm not sure what to fix here - is it the model or the data?</p>
<p>( for the data it's binary labels, and balanced in the numbe... | 2020-07-26 06:36:45.657000+00:00 | 2020-11-03 23:43:59.867000+00:00 | 2020-11-03 23:43:59.867000+00:00 | tensorflow|machine-learning|nlp|language-model | ['https://arxiv.org/pdf/1810.04805.pdf'] | 1 |
52,443,301 | <p>In the context of autoencoders the input and output of the model is the same. So, if the input values are in the range [0,1] then it is acceptable to use <code>sigmoid</code> as the activation function of last layer. Otherwise, you need to use an appropriate activation function for the last layer (e.g. <code>linear<... | 2018-09-21 11:58:45.940000+00:00 | 2019-06-25 07:22:23.607000+00:00 | 2019-06-25 07:22:23.607000+00:00 | null | 52,441,877 | <p>I wrote a vanilla autoencoder using only <code>Dense</code> layer.
Below is my code:</p>
<pre class="lang-py prettyprint-override"><code>iLayer = Input ((784,))
layer1 = Dense(128, activation='relu' ) (iLayer)
layer2 = Dense(64, activation='relu') (layer1)
layer3 = Dense(28, activation ='relu') (layer2)
layer4 =... | 2018-09-21 10:35:59.863000+00:00 | 2019-06-25 07:22:23.607000+00:00 | 2018-09-21 12:37:43.647000+00:00 | machine-learning|neural-network|keras|autoencoder|cross-entropy | ['https://blog.keras.io/building-autoencoders-in-keras.html', 'https://arxiv.org/abs/1708.08487', 'https://www.youtube.com/watch?v=xTU79Zs4XKY', 'http://www.dmi.usherb.ca/~larocheh/index_en.html', 'https://youtu.be/xTU79Zs4XKY?t=330'] | 5 |
44,936,598 | <p>From what I read, I'd be surprised if they're using neural networks. Here's how they say they detect anomalies:</p>
<blockquote>
<p>Detect outliers in a population by building a profile of a “typical” user or machine to know when one starts to stray from the pack.</p>
</blockquote>
<p>Doing anomaly detection lik... | 2017-07-05 21:53:38.927000+00:00 | 2017-07-05 21:53:38.927000+00:00 | null | null | 44,935,473 | <p>I'm very impressed from the new <a href="https://www.elastic.co/products/x-pack/machine-learning" rel="nofollow noreferrer">x-pack</a> ML of the elastic stack. It seems their technique learns data patterns over time and can predict anomalies in multiple domains.</p>
<p><a href="https://i.stack.imgur.com/TrGx5.png" ... | 2017-07-05 20:30:32.270000+00:00 | 2018-07-23 16:14:58.170000+00:00 | 2017-07-06 06:39:31.113000+00:00 | elasticsearch|machine-learning|anomaly-detection|rnn | ['https://arxiv.org/abs/1706.03762'] | 1 |
49,606,866 | <blockquote>
<ol>
<li>Use Convolution2D layers and LSTM layer</li>
</ol>
</blockquote>
<p>In this technique, you stack convolution and LSTM layers. The convolutional layers help you to learn the spatial features and the LSTM helps you learn the correlation in time.</p>
<blockquote>
<p>2.Use ConvLSTM2D</p>
</b... | 2018-04-02 07:11:14.890000+00:00 | 2018-04-11 08:01:36.633000+00:00 | 2018-04-11 08:01:36.633000+00:00 | null | 49,603,498 | <p>Are <code>1</code> and <code>2</code> the same?</p>
<ol>
<li>Use <code>Convolution2D</code> layers and <code>LSTM</code> layers </li>
<li>Use <code>ConvLSTM2D</code></li>
</ol>
<p>If there is any difference, could you explain it for me?</p> | 2018-04-01 23:14:53.400000+00:00 | 2018-04-13 16:40:43.003000+00:00 | 2018-04-13 16:40:43.003000+00:00 | python|tensorflow|keras | ['https://arxiv.org/abs/1506.04214', 'https://stackoverflow.com/questions/49468918/appplication-of-convlstm2d-layers/49472074#49472074'] | 2 |
49,770,553 | <p>They are not exactly the same, here is why:</p>
<h3>1. Use <code>Convolution2D</code> layers and <code>LSTM</code> layers</h3>
<p>As it is known, <code>Convolution2D</code> serves well for capturing image or spatial features, whilst <code>LSTM</code> are used to detect correlations over time. However, by stacking ... | 2018-04-11 08:46:57.363000+00:00 | 2018-04-11 08:46:57.363000+00:00 | null | null | 49,603,498 | <p>Are <code>1</code> and <code>2</code> the same?</p>
<ol>
<li>Use <code>Convolution2D</code> layers and <code>LSTM</code> layers </li>
<li>Use <code>ConvLSTM2D</code></li>
</ol>
<p>If there is any difference, could you explain it for me?</p> | 2018-04-01 23:14:53.400000+00:00 | 2018-04-13 16:40:43.003000+00:00 | 2018-04-13 16:40:43.003000+00:00 | python|tensorflow|keras | ['https://arxiv.org/abs/1506.04214v1', 'https://keras.io/layers/recurrent/#convlstm2d', 'https://github.com/keras-team/keras/blob/master/keras/layers/recurrent.py', 'https://github.com/keras-team/keras/blob/master/keras/layers/convolutional_recurrent.py'] | 4 |
65,766,155 | <p>As Ivan already noted you have a class imbalance problem. This can be resolved via:</p>
<ol>
<li><p><strong>Online hard negative mining:</strong> at each iteration after computing the loss, you can sort all elements in the batch belonging to "no DR" class and keep only the worst <code>k</code>. Then you es... | 2021-01-17 21:30:06.740000+00:00 | 2021-01-18 07:31:56.007000+00:00 | 2021-01-18 07:31:56.007000+00:00 | null | 65,762,961 | <p>I am relatively new to the deep learning landscape, so please don't be as mean as Reddit! It seems like a general question so I won't be giving my code here as it doesn't seem necessary (if it is, here's the link to <a href="https://colab.research.google.com/drive/1x6DOqow1dnZvy29_UZ3nHAKZLaNKr9hM?usp=sharing" rel="... | 2021-01-17 16:12:12.567000+00:00 | 2022-08-05 16:05:04.970000+00:00 | 2021-01-18 00:28:51.830000+00:00 | python|deep-learning|pytorch|conv-neural-network | ['https://arxiv.org/abs/1604.03540', 'https://stackoverflow.com/a/52161194/1714410', 'https://stackoverflow.com/a/58213245/1714410', 'https://stackoverflow.com/a/64365532/1714410'] | 4 |
29,876,936 | <p>Solved it by giving absolute path. I was trying all combinations of paths and also gave absolute path /var/www/arxiv/static/data/name.json and it worked.</p> | 2015-04-26 11:18:38.003000+00:00 | 2015-04-26 11:18:38.003000+00:00 | null | null | 29,876,315 | <p>I am using f = open('name.json','w+') to create a new file and write to it. But i am unable to create the file. Apache server logs show "No such file exists."</p> | 2015-04-26 10:10:09.927000+00:00 | 2015-04-26 11:18:38.003000+00:00 | 2015-04-26 10:31:43.320000+00:00 | python|apache|flask | [] | 0 |
39,249,179 | <p>You will need backtracking, because it is possible to add numbers to the Sudoku board which don't violate any rules immediately, but which will lead to a contradiction later on. If you take any unique-solution Sudoku problem and arbitrarily place any number anywhere, you are bound to experience just this.</p>
<p>I ... | 2016-08-31 12:04:53.170000+00:00 | 2016-08-31 12:04:53.170000+00:00 | 2017-05-23 11:47:14.977000+00:00 | null | 39,246,124 | <p>I am trying to to generate a Sudoku board using this script:</p>
<p>The problem is that I don't know how to validate to generate unique numbers on columns and squares.</p>
<p>Actually is just validating and generating unique numbers only on rows.</p>
<p>Here is that code :
<div class="snippet" data-lang="js" data... | 2016-08-31 09:43:43.907000+00:00 | 2018-08-16 16:38:54.950000+00:00 | 2018-08-16 16:38:54.950000+00:00 | javascript|arrays|validation|math|sudoku | ['https://arxiv.org/abs/cs/0011047', 'https://stackoverflow.com/questions/tagged/sudoku?sort=votes'] | 2 |
4,635,560 | <p>Asking the question in such a general way does not permit very specific advice to be given.</p>
<p>I'd begin the analysis by looking for ways to evaluate or rewrite the function using groups of variables that interact closely, creating intermediate expressions that can be used to make the final evaluation. You may... | 2011-01-08 18:54:26.003000+00:00 | 2011-01-09 09:22:47.240000+00:00 | 2011-01-09 09:22:47.240000+00:00 | null | 4,635,254 | <p>The question may seem vague, but let me explain it.</p>
<p>Suppose we have a function f(x,y,z ....) and we need to find its value at the point (x1,y1,z1 .....).</p>
<p>The most trivial approach is to just replace (x,y,z ...) with (x1,y1,z1 .....).</p>
<p>Now suppose that the function is taking a lot of time in ev... | 2011-01-08 17:48:44.687000+00:00 | 2011-01-09 09:22:47.240000+00:00 | 2011-01-09 00:52:02.030000+00:00 | algorithm|math|parallel-processing | ['http://cdsweb.cern.ch/record/401028/files/p837.pdf', 'http://arxiv.org/PS_cache/arxiv/pdf/1009/1009.3457v1.pdf', 'http://www.iacr.org/archive/ches2005/018.pdf', 'http://www.cs.ucsb.edu/~omer/DOWNLOADABLE/lu-cf98.pdf'] | 4 |
56,115,600 | <p>You can read the official paper here <a href="https://arxiv.org/pdf/1412.6980.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1412.6980.pdf</a></p>
<p>Your update looks somewhat like this (for brevity, sake I have omitted the warmup-phase):</p>
<p><code>new_theta = old_theta-learning_rate*momentum/(velocity+e... | 2019-05-13 15:35:47.110000+00:00 | 2020-02-05 14:29:34.257000+00:00 | 2020-02-05 14:29:34.257000+00:00 | null | 56,114,706 | <p>In the Adam optimization algorithm, the learning speed is adjusted according to the number of iterations. I don't quite understand Adam's design, especially when using batch training. When using batch training, if there are 19,200 pictures, each time 64 pictures are trained, it is equivalent to 300 iterations. If ou... | 2019-05-13 14:42:54.983000+00:00 | 2020-02-05 14:29:34.257000+00:00 | 2019-05-15 19:51:16.053000+00:00 | pytorch | ['https://arxiv.org/pdf/1412.6980.pdf'] | 1 |
49,187,418 | <blockquote>
<p>Is deep learning the only way to detect humans in a picture?</p>
</blockquote>
<p><strong>No.</strong> Is it the best way we know? Depends on your conditions.</p>
<p>The simplest way of detection is to generate lots of random bounding boxes and then solving the classification problem of the crop. He... | 2018-03-09 05:52:29.863000+00:00 | 2018-03-09 05:52:29.863000+00:00 | null | null | 49,156,852 | <p>I'm looking for a way to detect humans in a picture. For instance, regarding the picture below, I'd like to coarsely determine how many people are in the scene. I must be able to detect both standing and sitting people. I do not mind not detecting people located behind a physical object (such as the glass in the bus... | 2018-03-07 16:30:15.100000+00:00 | 2018-03-09 05:52:29.863000+00:00 | null | image-processing|deep-learning|image-segmentation | ['https://en.wikipedia.org/wiki/Viola%E2%80%93Jones_object_detection_framework', 'https://arxiv.org/pdf/1602.06541.pdf'] | 2 |
70,489,887 | <p>Fabric nodes use gRPC for communication and as such, it uses protobuf data serialization.</p>
<p>In Fabric, there is no need for the data sent over the wire to be human readable, as the data is anyway being constructed without human intervention.</p>
<p>You usually use JSON when you have payloads that are either exp... | 2021-12-26 21:09:08.340000+00:00 | 2021-12-26 21:09:08.340000+00:00 | null | null | 70,473,568 | <p>What are the advantages that protobuf provides which JSON cannot?</p>
<p>"<em>protobuff is faster than JSON</em>" - What does this mean?</p>
<p>Note: I am aware of the differences between protobuf and JSON.</p> | 2021-12-24 13:51:32.763000+00:00 | 2021-12-26 21:09:08.340000+00:00 | null | json|protocol-buffers|hyperledger-fabric|hyperledger | ['https://i.stack.imgur.com/Egb9t.png', 'https://i.stack.imgur.com/eUdmj.png', 'https://arxiv.org/abs/1901.00910'] | 3 |
62,361,833 | <p>A good overview of the different norms is shown in the <a href="https://arxiv.org/abs/1803.08494" rel="nofollow noreferrer">Group Normalization paper</a>.</p>
<p><a href="https://i.stack.imgur.com/EBEms.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/EBEms.png" alt="Norms overview"></a></p>
<p>I... | 2020-06-13 15:23:20.913000+00:00 | 2020-06-13 15:23:20.913000+00:00 | null | null | 62,356,985 | <p>I am really confused with the meaning of Instance Norm and whether I can use it with a batch size of 1. I am using PyTorch and nothing in the documentation says that batch size should be greater than 1.</p>
<p>I know that for BatchNorm the performance is adversely affected when batch size is less than 8 and hence i... | 2020-06-13 07:52:04.063000+00:00 | 2020-06-13 15:23:20.913000+00:00 | null | machine-learning|deep-learning|pytorch | ['https://arxiv.org/abs/1803.08494', 'https://i.stack.imgur.com/EBEms.png'] | 2 |
45,822,511 | <p>Common word2vec libraries, like gensim, do not provide such a facility for merging models. Inherently, the coordinates of words within-a-model are only comparable, in terms of distances and directions, with other words in-the-same-model – only by bring incrementally trained together do they get nudged to meaningful ... | 2017-08-22 16:10:21.853000+00:00 | 2017-08-22 16:10:21.853000+00:00 | null | null | 45,810,954 | <p>I have created two models using gensim word2vec. Now I want to merge these two models in a way that I get the union of these two models.</p>
<p>Eg: </p>
<ul>
1. Model one has following vocabulary
</ul>
<pre><code>{"Hi", "Hello", "World"}
</code></pre>
<ul>
2. Model two has the following vocabulary
</ul>
<pre><c... | 2017-08-22 07:03:01.030000+00:00 | 2017-10-12 12:46:28.437000+00:00 | 2017-08-22 08:20:23.910000+00:00 | machine-learning|nlp|deep-learning|gensim|word2vec | ['https://arxiv.org/abs/1309.4168', 'http://arxiv.org/abs/1506.06726', 'https://github.com/RaRe-Technologies/gensim/pull/1434'] | 3 |
55,794,895 | <p>It is a known thing with high capacity models. They are suprisingly resistant to overfitting which contradicts to the classical statistical learning theory that says that without explicit regularization you going to overfit. For example, <a href="https://www.padl.ws/papers/Paper%2010.pdf" rel="nofollow noreferrer">t... | 2019-04-22 13:04:59.640000+00:00 | 2019-04-22 13:10:09.337000+00:00 | 2019-04-22 13:10:09.337000+00:00 | null | 55,794,687 | <p>I trained a model, got decent results, but then I got greedy and I wanted even more accuracy, so, I trained the model for longer, and longer and longer, but to no avail, nothing happens! according to theory, at some point, the validation accuracy must start to decrease after too much training (the loss start to INCR... | 2019-04-22 12:47:52.120000+00:00 | 2019-04-22 13:31:39.567000+00:00 | 2019-04-22 13:31:39.567000+00:00 | python|tensorflow|machine-learning|keras|deep-learning | ['https://www.padl.ws/papers/Paper%2010.pdf', 'https://arxiv.org/pdf/1705.08741.pdf', 'https://arxiv.org/pdf/1710.10345.pdf'] | 3 |
57,994,105 | <p>Papers on analyzing deep learning models for sentiment analysis (<a href="https://arxiv.org/pdf/1809.08037.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1809.08037.pdf</a>, <a href="https://arxiv.org/pdf/1907.04613.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1907.04613.pdf</a>) show that the networks... | 2019-09-18 13:38:13.217000+00:00 | 2019-09-18 13:38:13.217000+00:00 | null | null | 57,991,521 | <p>We need to analyse a lot of articles relevant to political instability in a given country (things like the possibility of a coalition / a snap election etc).
The problem is that I could not find any labeled datasets which could be plugged into a neural network (CNN/LSTM in TensorFlow) so as to supervise it for real... | 2019-09-18 11:16:42.517000+00:00 | 2019-09-18 13:38:13.217000+00:00 | null | machine-learning|neural-network|nlp|sentiment-analysis | ['https://arxiv.org/pdf/1809.08037.pdf', 'https://arxiv.org/pdf/1907.04613.pdf'] | 2 |
61,386,604 | <p>Although the expression "<em>Adam is RMSProp with momentum</em>" is widely used indeed, it is just a very rough shorthand description, and it should not be taken at face value; already in the original <a href="https://arxiv.org/pdf/1412.6980.pdf" rel="nofollow noreferrer">Adam paper</a>, it was explicitly clarified ... | 2020-04-23 11:53:32.427000+00:00 | 2020-04-23 12:18:25.137000+00:00 | 2020-04-23 12:18:25.137000+00:00 | null | 61,381,648 | <p>Here is a <a href="https://www.tensorflow.org/api_docs/python/tf/keras/optimizers" rel="nofollow noreferrer">link</a> to tensorflow optimizers. And there you can see, that RMSprop takes momentum as argument while Adam does not do this. So I am confuzed. Adam optimization pretends to be a RMSprop optimization with mo... | 2020-04-23 07:30:58.123000+00:00 | 2020-04-23 13:08:07.157000+00:00 | 2020-04-23 13:08:07.157000+00:00 | tensorflow|optimization|keras|adam | ['https://arxiv.org/pdf/1412.6980.pdf', 'https://ruder.io/optimizing-gradient-descent/index.html#adam', 'https://cs231n.github.io/neural-networks-3/#ada', 'https://cntk.ai/pythondocs/cntk.learners.html#cntk.learners.adam', 'https://docs.microsoft.com/en-us/cognitive-toolkit/BrainScript-SGD-Block#converting-learning-rat... | 5 |
48,486,864 | <p>The usual representation is</p>
<pre><code> PREDICTED
[[41 2 0 0 0 0]
T [ 1 11 4 1 0 0]
R [ 0 1 12 0 0 0]
U [ 0 0 0 22 1 0]
E [ 0 0 0 0 7 0]
[ 0 0 0 0 0 20]]
</code></pre>
<p>As pointed out by M. Rath (+1), this is also what Tensorflow does. This means for 41 samples you correctly p... | 2018-01-28 13:29:21.860000+00:00 | 2018-01-28 13:29:21.860000+00:00 | null | null | 48,457,140 | <p>I have 6 classes and I used tf-slim in Tensorflow to obtained the confusion matrix such as</p>
<pre><code>[[41 2 0 0 0 0]
[ 1 11 4 1 0 0]
[ 0 1 12 0 0 0]
[ 0 0 0 22 1 0]
[ 0 0 0 0 7 0]
[ 0 0 0 0 0 20]]
</code></pre>
<p>My question is that what is confusion matrix order of the above ... | 2018-01-26 07:19:44.490000+00:00 | 2018-01-28 13:29:21.860000+00:00 | null | tensorflow|deep-learning|tf-slim | ['https://arxiv.org/pdf/1707.09725.pdf#page=48', 'https://github.com/MartinThoma/clana'] | 2 |
69,304,325 | <p>You might have misread the source code, the first sample you gave is not averaging the resut of <code>D</code> to compute its loss but instead uses the binary cross-entropy.</p>
<p>To be more precise:</p>
<ul>
<li><p>The first method (<strong>"GAN"</strong>) uses the BCE loss to compute the loss terms for ... | 2021-09-23 17:13:07.637000+00:00 | 2021-09-23 17:13:07.637000+00:00 | null | null | 69,302,763 | <p>I just find that in the code here:</p>
<p><a href="https://github.com/NUS-Tim/Pytorch-WGAN/tree/master/models" rel="nofollow noreferrer">https://github.com/NUS-Tim/Pytorch-WGAN/tree/master/models</a></p>
<p>The "generator" loss, <code>G</code>, between WGAN and WGAN-GP is different, for WGAN:</p>
<pre><cod... | 2021-09-23 15:18:59.933000+00:00 | 2022-01-06 15:57:12.017000+00:00 | 2021-09-24 08:16:26.943000+00:00 | deep-learning|neural-network|pytorch|backpropagation|generative-adversarial-network | ['https://github.com/NUS-Tim/Pytorch-WGAN/blob/master/models/gan.py#L83', 'https://arxiv.org/pdf/1704.00028v3.pdf', 'https://github.com/NUS-Tim/Pytorch-WGAN/blob/master/models/wgan_gradient_penalty.py#L174'] | 3 |
63,191,640 | <p>My understanding is that the authors are saying that one does not need to downsample image (or, any intermediate feature map) before applying let's say 3x3 convolution which is typical in DCNNs (e.g., VGG16 or ResNet) for feature extraction and followed by upsampling for semantic segmentation. In a typical encoder-d... | 2020-07-31 12:14:12.700000+00:00 | 2020-07-31 12:14:12.700000+00:00 | null | null | 55,007,114 | <p>i try to understand dilated convolution. I already familiar with increasing the size of the kernel by filling the gaps with zeros. Its usefull to cover a bigger area and get a better understanding about larger objects.
But please can someone explain me how it is possible that dilated convolutional layers keep the or... | 2019-03-05 16:14:10.223000+00:00 | 2020-07-31 12:14:12.700000+00:00 | 2019-03-05 18:59:24.123000+00:00 | deep-learning|conv-neural-network|deeplab | ['https://arxiv.org/pdf/1606.00915.pdf', 'https://i.stack.imgur.com/Aeqv9.png'] | 2 |
67,849,743 | <p>The interpretation in terms of holes applies to the filtration defined as a union of balls around each data point, or equivalently to the Čech or α-complex filtrations. The Rips filtration is often used as an easier to compute approximation of that filtration, but it does not have the same theoretical properties. Th... | 2021-06-05 12:45:54.790000+00:00 | 2021-06-05 12:45:54.790000+00:00 | null | null | 59,648,575 | <p>I use <code>RIPSER</code> library in python to analyze my <code>data(TDA)</code>. The <code>plot(data)</code> i have used is <code>2-d</code>, but when i use <code>ripser</code> to analyze, it gives me output with three homology <code>groups(H0, H1, H2)</code>. But this is not possible, since <code>H0</code> represe... | 2020-01-08 15:01:48.543000+00:00 | 2021-06-05 12:45:54.790000+00:00 | 2020-01-08 16:34:47.820000+00:00 | python|r|python-3.x|topology | ['https://arxiv.org/abs/1503.03669', 'https://gudhi.inria.fr/', 'https://github.com/mrzv/diode', 'https://www.mrzv.org/software/dionysus2/'] | 4 |
50,257,008 | <p>First, a bit of terminology:</p>
<ul>
<li><p>In <strong>ensembles</strong> (as I understand them) you have N models at test time and you combine their <strong>predictions</strong> (by voting, or even better combining probabilistic distributions and using as input for further decoding in case of autoregressive seq2s... | 2018-05-09 15:27:59.090000+00:00 | 2018-05-09 15:27:59.090000+00:00 | null | null | 42,837,829 | <p>I have a trained a tensorflow <a href="https://www.tensorflow.org/tutorials/seq2seq" rel="nofollow noreferrer">seq2seq</a> model for 30 epochs, and saved a checkpoint for each epoch. What I want to do now is combining the best X of those checkpoints (based on results on a development set). Specifically, I'm looking ... | 2017-03-16 15:06:42.943000+00:00 | 2018-05-09 15:27:59.090000+00:00 | 2017-05-23 10:29:57.447000+00:00 | tensorflow|deep-learning|ensemble-learning|sequence-to-sequence | ['http://www.statmt.org/wmt17/pdf/WMT39.pdf', 'https://arxiv.org/pdf/1803.05407.pdf', 'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/utils/avg_checkpoints.py', 'https://github.com/tensorflow/tensor2tensor/blob/master/tensor2tensor/bin/t2t_avg_all.py'] | 4 |
51,576,094 | <p>This paper contains some formulas that relate convolutional layer parameters to input and output shapes: <a href="https://arxiv.org/pdf/1603.07285.pdf" rel="nofollow noreferrer">A guide to convolution arithmetic for deep
learning</a></p> | 2018-07-28 23:45:27.293000+00:00 | 2018-07-28 23:45:27.293000+00:00 | null | null | 51,572,107 | <p>I'd like to generate an image of size (176,132), from a (60,1) Vector. </p>
<p>I don't know how to choose the Stride, Kernels, Padding and output paddings to best fit my problem... Is there any online resource that could help ?</p>
<p>I believe stacking a few TransposedConvolution2D layers helps, but how do each l... | 2018-07-28 14:03:53.060000+00:00 | 2018-07-28 23:45:27.293000+00:00 | null | python-3.x|machine-learning|keras | ['https://arxiv.org/pdf/1603.07285.pdf'] | 1 |
66,084,306 | <p>I am a physicist and programmer who has worked extensively on Qiskit. I have limited experience with things like machine learning but if I am not mistaken <a href="https://arxiv.org/pdf/1401.2142.pdf" rel="nofollow noreferrer">figure 13 on page 22 of this paper on Nearest-Neighbor methods</a> is precisely the circui... | 2021-02-07 03:34:54.610000+00:00 | 2021-02-07 03:34:54.610000+00:00 | null | null | 66,071,608 | <p>I did a small benchmarking to compare quantum version of algorithm to its classical version, and I found that quantum computing taking so much time in comparison to classical version.</p>
<p>I don't understand why its happening, it should be either similar to classical or better.</p>
<p>DataSet Explanation: 1 test d... | 2021-02-05 22:46:37.240000+00:00 | 2021-02-07 03:34:54.610000+00:00 | 2021-02-05 22:55:50.577000+00:00 | python|machine-learning|euclidean-distance|quantum-computing | ['https://arxiv.org/pdf/1401.2142.pdf', 'https://www.researchgate.net/figure/a-IBM-Q16-Melbourne-architecture-b-Previous-available-IBM-Q20-Tokyo-architecture_fig2_340963391'] | 2 |
62,723,657 | <p>The use of the <code>[CLS]</code> token to represent the entire sentence comes from the <a href="https://arxiv.org/pdf/1810.04805.pdf" rel="noreferrer">original BERT paper</a>, section 3:</p>
<blockquote>
<p>The first token of every sequence is always a special classification token ([CLS]). The final hidden state co... | 2020-07-03 23:10:29.270000+00:00 | 2020-09-09 00:15:55.043000+00:00 | 2020-09-09 00:15:55.043000+00:00 | null | 62,705,268 | <p>I am doing experiments on bert architecture and found out that most of the fine-tuning task takes the final hidden layer as text representation and later they pass it to other models for the further downstream task.</p>
<p>Bert's last layer looks like this :</p>
<p><a href="https://i.stack.imgur.com/m0jrg.png" rel="... | 2020-07-02 21:25:04.807000+00:00 | 2020-09-09 00:15:55.043000+00:00 | 2020-07-03 01:18:12.007000+00:00 | tensorflow|machine-learning|keras|deep-learning|bert-language-model | ['https://arxiv.org/pdf/1810.04805.pdf', 'https://huggingface.co/transformers/v3.0.2/model_doc/bert.html#bertmodel'] | 2 |
57,612,327 | <p>u r right. That method only takes the data uncertainty into account (assuming u don't fit the neural net while applying the noise). As a side note, alternatively when fitting the data using a neural net u may also apply mixture density networks (see one of the many tutorials). </p>
<p>More importantly, in order to ... | 2019-08-22 15:05:46.253000+00:00 | 2019-08-22 15:05:46.253000+00:00 | null | null | 33,323,571 | <p>I have inputs <code>x_1, ..., x_n</code> that have known 1-sigma uncertainties <code>e_1, ..., e_n</code>. I am using them to predict outputs <code>y_1, ..., y_m</code> on a trained neural network. How can I obtain 1-sigma uncertainties on my predictions? </p>
<p>My idea is to randomly perturb each input <code>x_i<... | 2015-10-24 21:29:35.420000+00:00 | 2019-08-22 15:05:46.253000+00:00 | 2015-10-24 21:36:15.323000+00:00 | machine-learning|neural-network | ['https://arxiv.org/abs/1908.00598'] | 1 |
61,037,182 | <p>I'm assuming you're using the scikit-learn random forest model, since it has that <code>feature_importances_</code> attribute. Though you might see similar results by checking the correlation between features and your target variable, <code>feature_importances_</code> uses a more sophisticated approach. From the <a ... | 2020-04-05 02:12:21.030000+00:00 | 2020-04-05 02:12:21.030000+00:00 | null | null | 61,037,114 | <p>I want to see the correlation between variables. So first, i used Correlation Matrix. It showed me the correlation between all variables. Then i create my <code>random forest regressor</code> model. In an article i found that it has function of <code>feature_importances_</code>. It tells the correlation between the ... | 2020-04-05 01:58:35.490000+00:00 | 2020-04-05 16:43:38.870000+00:00 | 2020-04-05 02:26:05.780000+00:00 | python|machine-learning|scikit-learn | ['https://scikit-learn.org/stable/modules/ensemble.html#feature-importance-evaluation', 'https://arxiv.org/pdf/1407.7502.pdf'] | 2 |
58,602,515 | <p>There is a paper about SAC with discrete action spaces. It says SAC for discrete action spaces doesn't need re-parametrization tricks like Gumbel softmax. Instead, SAC needs some modifications. please refer to the paper for more details.</p>
<p><a href="https://arxiv.org/abs/1910.07207" rel="nofollow noreferrer">Pa... | 2019-10-29 06:44:08.427000+00:00 | 2019-10-30 07:08:03.470000+00:00 | 2019-10-30 07:08:03.470000+00:00 | null | 56,226,133 | <p>I'm trying to implement the soft actor critic algorithm for discrete action space and I have trouble with the loss function.</p>
<p>Here is the link from SAC with continuous action space:
<a href="https://spinningup.openai.com/en/latest/algorithms/sac.html" rel="nofollow noreferrer">https://spinningup.openai.com/en/... | 2019-05-20 18:11:23.630000+00:00 | 2021-04-11 01:12:01.010000+00:00 | 2020-06-20 09:12:55.060000+00:00 | python|tensorflow|machine-learning|reinforcement-learning | ['https://arxiv.org/abs/1910.07207', 'https://github.com/p-christ/Deep-Reinforcement-Learning-Algorithms-with-PyTorch', 'https://github.com/ku2482/sac-discrete.pytorch'] | 3 |
61,294,337 | <p>Try this:</p>
<ol>
<li>Train the first model, which sets <code>trainable</code> to <code>False</code>. You don't have to train it to saturation, so I would start with your 5 epochs.</li>
<li>Go back and set <code>trainable</code> to <code>True</code> for all the <code>vgg19</code> parameters. Then, <a href="https:/... | 2020-04-18 18:40:42.910000+00:00 | 2020-04-19 16:58:38.890000+00:00 | 2020-04-19 16:58:38.890000+00:00 | null | 61,292,890 | <p>I have two models initialized like this</p>
<pre><code>vgg19 = keras.applications.vgg19.VGG19(
weights='imagenet',
include_top=False,
input_shape=(img_height, img_width, img_channels))
for layer in vgg19.layers:
layer.trainable = False
model = Sequential(layers=vgg19.layers)
model.add(Flatten())
model.add... | 2020-04-18 16:54:44.350000+00:00 | 2020-04-19 16:58:38.890000+00:00 | null | machine-learning|keras|neural-network|transfer-learning|vgg-net | ['https://keras.io/getting-started/faq/#how-can-i-freeze-keras-layers', 'https://arxiv.org/abs/1801.06146', 'https://docs.fast.ai/basic_train.html#Discriminative-layer-training'] | 3 |
23,161,886 | <p>According the Joaquín Pérez-Iglesias in <a href="https://arxiv.org/abs/0911.5046" rel="nofollow noreferrer">Integrating the Probabilistic Model BM25/BM25F into Lucene</a>, the score function R should be defined as followed :</p>
<p><img src="https://i.stack.imgur.com/sgOGC.png" alt="enter image description here"></... | 2014-04-18 20:35:15.557000+00:00 | 2017-06-13 15:31:38.190000+00:00 | 2017-06-13 15:31:38.190000+00:00 | null | 23,161,677 | <p>I am studying the Okapi BMS25 model. I understand everything but two confusion. While calculating document length (dl) and average document length (avdl). I found the document length is </p>
<p><img src="https://i.stack.imgur.com/3UYl4.jpg" alt="enter image description here"></p>
<p>So it is a summation of my keyw... | 2014-04-18 20:20:34.857000+00:00 | 2017-06-13 15:31:38.190000+00:00 | 2014-04-18 20:24:03.840000+00:00 | information-retrieval | ['https://arxiv.org/abs/0911.5046'] | 1 |
57,708,625 | <p>@j23 has given a very reasonable operational answer. By way of possible explanation of the behaviour you observed, the number you are printing is not a single <code>$RANDOM</code> value, but the product of two such values. Pairs of consecutive outputs of a pseudorandom number generator (PRNG) are not necessarily a... | 2019-08-29 10:53:47.187000+00:00 | 2019-08-29 10:53:47.187000+00:00 | null | null | 57,705,578 | <p>I wrote a script with bash in while loop. The code:</p>
<pre><code>number=0
while [ 1500 -gt $number ]
do
var="abcdefghijklmnopqrstuvxyz"
letter1="${var:$(( RANDOM % ${#var} )):1}"
letter2="${var:$(( RANDOM % ${#var} )):1}"
a=$RANDOM
b=$RANDOM
c=$(( $a * $b))
echo "$letter1$letter2 $c" >> a.txt
... | 2019-08-29 07:53:50.393000+00:00 | 2019-08-30 03:50:34.093000+00:00 | 2019-08-29 10:15:47.567000+00:00 | linux|bash|random|debian | [] | 0 |
36,675,792 | <p>I will first go through some general links and resources and then try to describe the general idea of the algorithm.</p>
<p><strong>SEEDS implementations:</strong></p>
<p>You obviously already saw the documentation <a href="http://docs.opencv.org/3.0-beta/modules/ximgproc/doc/superpixels.html" rel="noreferrer">her... | 2016-04-17 11:17:55.450000+00:00 | 2016-04-17 11:17:55.450000+00:00 | null | null | 36,672,019 | <p>I am interested in superpixels extracted via energy-driven sampling (SEEDS) which is a method of image segmentation using superpixels. This is also what OpenCV uses to create superpixels. I am having troubles finding documentation behind the SEEDS algorithm. OpenCV gives a very general description which can be fo... | 2016-04-17 02:22:16.217000+00:00 | 2016-04-17 11:17:55.450000+00:00 | null | opencv|image-segmentation|superpixels | ['http://docs.opencv.org/3.0-beta/modules/ximgproc/doc/superpixels.html', 'https://github.com/Itseez/opencv_contrib/blob/6cd8e9f556c8c55c05178dec05d5277ae00020d9/modules/ximgproc/samples/seeds.cpp', 'https://github.com/davidstutz/superpixels-revisited/tree/master/lib_seeds', 'https://github.com/davidstutz/seeds-revised... | 7 |
59,618,044 | <p>You did not provide full code of algorithm.
Check <a href="https://arxiv.org/abs/1509.02971" rel="nofollow noreferrer">DDPG paper</a> for networks architecture and hyperparameters, because its proven in paper that same algorithm configuration works very well for different problems and environments.
Make sure you use... | 2020-01-06 19:44:23.100000+00:00 | 2020-01-06 19:44:23.100000+00:00 | null | null | 59,470,798 | <p>I'm trying to implement DDPG with tensorflow 2.
The problem is that it doesn't learn: even after adding some noise and some expolitation vs exploration factor the agent seems to stuck everytime in a generic direction, only changing its intensity.</p>
<p>This is my Actor neural network:</p>
<pre><code> d1 = self... | 2019-12-24 15:25:28.733000+00:00 | 2020-01-06 19:44:23.100000+00:00 | null | tensorflow|neural-network|reinforcement-learning | ['https://arxiv.org/abs/1509.02971'] | 1 |
54,587,814 | <p>You would like to combine the pretrained model labels with your own labels, or in other words you are augmenting the pretrained model with new classes. The practical approach is to use the basis of transfer learning.</p>
<p>But let me tell you this, this is still a hot research topic. It is easier to retrain with y... | 2019-02-08 07:31:55.783000+00:00 | 2019-02-08 08:02:14.117000+00:00 | 2019-02-08 08:02:14.117000+00:00 | null | 54,548,361 | <p>I have two Pre-Trained and saved Inception Models.</p>
<p>Model 1 = Inception Model with Imagenet Classes - It can predict an image in 1000 classes.</p>
<p>Model 2 = Inception Model with my own classiciation (20 Classes) - It can predict an image in 20 classes. Performed transfer learning and saved the model.</p>
... | 2019-02-06 07:12:47.770000+00:00 | 2022-01-09 19:11:50.310000+00:00 | 2019-02-07 00:36:51.703000+00:00 | python|keras | ['https://arxiv.org/abs/1606.09282', 'https://github.com/lizhitwo/LearningWithoutForgetting', 'https://github.com/tensorflow/hub/blob/master/examples/image_retraining/retrain.py', 'https://www.tensorflow.org/tutorials/images/hub_with_keras', 'https://github.com/tensorflow/models/tree/master/research/inception'] | 5 |
61,218,755 | <p>Let me talk about random integer generating algorithms that are "optimal" in terms of the number of random bits it uses on average. In the rest of this post, we will assume we have a "true" random generator that can produce unbiased and independent random bits.</p>
<p>In 1976, D. E. Knuth and A. ... | 2020-04-14 23:09:50.483000+00:00 | 2022-01-12 13:43:19.307000+00:00 | 2022-01-12 13:43:19.307000+00:00 | null | 18,101,973 | <p>If I have a true random number generator (TRNG) which can give me either a 0 or a 1 each time I call it, then it is trivial to then generate any number in a range with a length equal to a power of 2. For example, if I wanted to generate a random number between 0 and 63, I would simply poll the TRNG 5 times, for a ma... | 2013-08-07 11:16:38.410000+00:00 | 2022-01-12 13:43:19.307000+00:00 | null | algorithm|random|statistics | ['https://arxiv.org/abs/1304.1916', 'http://mathforum.org/library/drmath/view/65653.html', 'https://arxiv.org/abs/1012.4290', 'https://www.pcg-random.org/posts/bounded-rands.html'] | 4 |
73,809,965 | <p>The answer is much harder than one might think: <a href="https://arxiv.org/pdf/1201.2374.pdf" rel="nofollow noreferrer">this paper</a> contains a 50-page proof of an algorithm that does this.</p> | 2022-09-22 06:00:58.077000+00:00 | 2022-09-22 06:00:58.077000+00:00 | null | null | 73,788,683 | <p>There have been similar questions, like <a href="https://stackoverflow.com/q/27649735/2281318">this</a> or <a href="https://stackoverflow.com/questions/39850826/how-can-i-convert-pcfg-in-cnf-for-this-grammar">this</a>, but mine is different:</p>
<p>Let's say, I have a probabilistic context-free grammar (PCFG)</p>
<p... | 2022-09-20 14:51:13.297000+00:00 | 2022-09-22 06:00:58.077000+00:00 | 2022-09-21 08:27:46.903000+00:00 | nlp|context-free-grammar|probability-distribution | ['https://arxiv.org/pdf/1201.2374.pdf'] | 1 |
60,204,849 | <p>This is not an easy problem.</p>
<p>You suggest replacing the words on the data preprocessing level. To do so, you would need word alignment that would tell you what source words match your target word. There are tools for that like <a href="https://github.com/clab/fast_align" rel="nofollow noreferrer">FastAlign</a... | 2020-02-13 09:48:13.833000+00:00 | 2020-02-13 09:48:13.833000+00:00 | null | null | 60,202,019 | <p>I'm working on Machine learning AI translation system, and I want to make it more adaptable my code now when the word is new will place <em>UNK</em> which stands for <em>UNKNOWN</em> and leave it, but I want to copy the same word and past it back instead of printing <em>UNK</em>, so if a new word comes it should pa... | 2020-02-13 06:52:39.317000+00:00 | 2020-02-13 09:48:13.833000+00:00 | 2020-02-13 09:38:42.347000+00:00 | python|tensorflow|keras|nlp|machine-translation | ['https://github.com/clab/fast_align', 'https://arxiv.org/abs/1603.06393', 'https://github.com/glample/fastBPE', 'https://github.com/google/sentencepiece'] | 4 |
47,697,583 | <p>Looks like you're facing the problem of exploding gradients with ReLu activation function (that what <code>NaN</code> means -- very big activations). There are several techniques to deal with this issue, e.g. <a href="https://arxiv.org/abs/1502.03167" rel="nofollow noreferrer">batch normalization</a> (changes the ne... | 2017-12-07 14:44:40.883000+00:00 | 2017-12-07 14:44:40.883000+00:00 | null | null | 47,685,341 | <p>The neural network I trained is the critic network for deep reinforcement learning. The problem is when one of the layer's activation is set to be relu or elu, the output would be nan after some training step, while the output is normal if the activation is tanh. And the code is as follows(based on tensorflow):</p>
... | 2017-12-06 23:41:56.423000+00:00 | 2017-12-07 16:36:30.227000+00:00 | 2017-12-07 16:36:30.227000+00:00 | machine-learning|tensorflow|neural-network|deep-learning|reinforcement-learning | ['https://arxiv.org/abs/1502.03167', 'http://proceedings.mlr.press/v9/glorot10a/glorot10a.pdf', 'https://www.tensorflow.org/api_docs/python/tf/variance_scaling_initializer'] | 3 |
43,142,186 | <p>Why don't you want to join the prediction at feature level? </p>
<p>If you really want to stick to your idea of not joining any layers of the network, you can apply a CRF or SVM on top of the overall prediction pipeline to learn cross-correlations between the predictions. For any other method you will need to combi... | 2017-03-31 13:39:48.350000+00:00 | 2017-03-31 15:36:59.993000+00:00 | 2017-03-31 15:36:59.993000+00:00 | null | 43,128,935 | <p>I have trained a network on two different modals of the same image. I pass the data together in one layer but after that, it is pretty much two networks in parallel, they don't share a layer and the two tasks have different set of labels, therefore I have two different loss and accuracy layers (I use caffe btw). I w... | 2017-03-30 21:46:51.827000+00:00 | 2018-09-19 06:53:53.693000+00:00 | 2018-09-19 06:53:53.693000+00:00 | neural-network|deep-learning|conv-neural-network | ['https://arxiv.org/abs/1612.07695', 'https://github.com/MarvinTeichmann/MultiNet#multinet'] | 2 |
68,837,545 | <p>The variances of several of your latent variables are very small. For example, Dlbi appears to be effectively zero. That's the source of the issue here.</p>
<p>There are two things you can to try to remedy this.</p>
<p>First, it may work better to identify the model by fixing the latent variable variances to 1, rath... | 2021-08-18 18:21:04.677000+00:00 | 2021-08-18 18:21:04.677000+00:00 | null | null | 68,837,355 | <p>Title basically explains it but I'm trying to build a bifactor model with psychopathy as one factor and subtypes as the other. I believe that I have everything constrained properly but that might be the issue.</p>
<p><strong>Current code:</strong></p>
<pre class="lang-r prettyprint-override"><code>BifactorModel <... | 2021-08-18 18:05:39.517000+00:00 | 2021-08-18 18:21:04.677000+00:00 | 2021-08-18 18:13:26.287000+00:00 | r|statistics|r-lavaan|factor-analysis | ['https://psyarxiv.com/q356f/', 'https://www.sciencedirect.com/science/article/pii/S0001879120300555'] | 2 |
46,810,226 | <p>The document <a href="https://github.com/commaai/research/blob/3429b061cf2a15dc37661552775aa983206f7561/DriveSim.md" rel="nofollow noreferrer">DriveSim.md</a> in the repository links to a paper titled <a href="https://arxiv.org/pdf/1608.01230.pdf" rel="nofollow noreferrer">Learning a Driving Simulator</a>. In the pa... | 2017-10-18 12:29:17.730000+00:00 | 2017-10-18 12:29:17.730000+00:00 | null | null | 46,787,099 | <p>In comma.ai's <a href="https://github.com/commaai/research" rel="nofollow noreferrer">self-driving car software</a> they use a client/server architecture. Two processes are started separately, <code>server.py</code> and <code>train_steering_model.py.</code> </p>
<p><code>server.py</code> sends data to <code>train_s... | 2017-10-17 09:39:40.777000+00:00 | 2017-10-18 15:43:06.183000+00:00 | 2017-10-18 15:43:06.183000+00:00 | machine-learning|tensorflow|client-server|keras | ['https://github.com/commaai/research/blob/3429b061cf2a15dc37661552775aa983206f7561/DriveSim.md', 'https://arxiv.org/pdf/1608.01230.pdf', 'https://github.com/commaai/research/blob/master/Logs.md', 'https://en.wikipedia.org/wiki/CAN_bus'] | 4 |
17,254,565 | <blockquote>
<p>Is it a bug?</p>
</blockquote>
<p>No, a bug is something that violates the specification. The <a href="http://docs.oracle.com/javase/6/docs/api/java/lang/Math.html#pow(double,%20double)" rel="noreferrer">specification</a> states:</p>
<blockquote>
<p>Returns the value of the first argument raised to the ... | 2013-06-22 19:33:14.970000+00:00 | 2013-06-22 19:33:14.970000+00:00 | 2020-06-20 09:12:55.060000+00:00 | null | 17,254,533 | <p><code>Math.pow(0.0, 0.0)</code> in Java returns 1 which is wrong. 0^0 is undefined. The same problem exists also in the windows calculator (I am using Win7). Why is that?</p>
<p>Mathematica declares it as an error as well as my Casio scientific calculator, why not java or the Win calculator... Is it a bug? </p> | 2013-06-22 19:29:07.417000+00:00 | 2013-06-22 19:47:28.097000+00:00 | 2013-06-22 19:43:12.600000+00:00 | java|math|pow | ['http://docs.oracle.com/javase/6/docs/api/java/lang/Math.html#pow(double,%20double)', 'https://en.wikipedia.org/wiki/Exponentiation#Zero_to_the_power_of_zero', 'http://arxiv.org/abs/math/9205211'] | 3 |
44,476,646 | <p>The paper <a href="https://arxiv.org/abs/1403.2056" rel="nofollow noreferrer">Engineering Parallel String Sorting</a> has, surprisingly, benchmarks of a large number of single-threaded algorithms on an URL dataset (see page 29). Rantala's variant of multikey quicksort with caching comes out ahead; you can test mult... | 2017-06-10 18:22:27.283000+00:00 | 2017-06-10 18:22:27.283000+00:00 | null | null | 16,245,946 | <p>I have a set of strings. 90% of them are URLs start with <code>"http://www."</code>. I want to sort them alphabetically.</p>
<p>Currently I use C++ std::sort(). but std::sort is a variant of quick-sort based on comparison, and comparing two strings with long common prefix is not effecient. However (I think) a radix... | 2013-04-26 22:08:45.513000+00:00 | 2017-06-10 18:22:27.283000+00:00 | null | string|algorithm|sorting|data-structures | ['https://arxiv.org/abs/1403.2056', 'https://github.com/rantala/string-sorting', 'https://link.springer.com/chapter/10.1007%2F978-3-540-89097-3_3'] | 3 |
68,626,258 | <p>The addition of noise affects the backward pass through that layer. For example, for quantization layers, people typically use straight-through estimator (which is basically just gradient clipping if I remember correctly).</p>
<p>Please note that when clipping gradients they should be done when passing through noise... | 2021-08-02 18:13:53+00:00 | 2021-08-02 18:22:28.163000+00:00 | 2021-08-02 18:22:28.163000+00:00 | null | 68,620,396 | <p>accroding to paper by OpenAI(<a href="https://arxiv.org/pdf/1706.02275.pdf" rel="nofollow noreferrer">https://arxiv.org/pdf/1706.02275.pdf</a>),we should add noise to policy to ensure exploration. While in an example code, there is a method to add noise:</p>
<p><code>u = torch.rand_like(model_out)</code></p>
<p><cod... | 2021-08-02 10:54:53.303000+00:00 | 2021-08-02 18:22:28.163000+00:00 | null | pytorch|reinforcement-learning|multi-agent | ['https://arxiv.org/pdf/1607.00133.pdf', 'https://discuss.pytorch.org/t/how-to-add-noise-to-mnist-dataset-when-using-pytorch/59745/17'] | 2 |
63,267,234 | <p>This is almost surely a duplicate but I am in-between tasks and cannot search now...</p>
<p>"It's still complicated" but Rcpp 1.0.5 now also ship this <a href="https://arxiv.org/abs/1911.06416" rel="nofollow noreferrer">arXiv paper I wrote on this</a> as this <a href="https://cloud.r-project.org/web/packag... | 2020-08-05 14:21:48.940000+00:00 | 2020-08-05 14:21:48.940000+00:00 | null | null | 63,266,717 | <p>I can't seem to find anything on the internet which show you how to build and compile a C++ library such that it can be used in R via the Rcpp package. I am missing some steps where the library is somehow linked to R tools</p>
<p>For instance how do you get the boost library working with R or any other such library?... | 2020-08-05 13:54:23.270000+00:00 | 2020-08-05 14:21:48.940000+00:00 | null | c++|r|boost|rcpp | ['https://arxiv.org/abs/1911.06416', 'https://cloud.r-project.org/web/packages/Rcpp/vignettes/Rcpp-libraries.pdf'] | 2 |
62,840,563 | <p>I have to disagree with the other answer that quotes Barr et al (2013) to "keep it maximal". This has been proven to be bad advice on so many occasions that the authors of lme4 had introduce code to check for singular fit. Not only that but Doug Bates (primary author of lme4 and probably the worlds leading... | 2020-07-10 18:45:59.247000+00:00 | 2020-07-10 18:45:59.247000+00:00 | null | null | 55,124,966 | <p>I have a question regarding the correct model setup in R using lmer.</p>
<p>This is a repeated measures experiment</p>
<p>Each subject (20 in total) completed 4 different task for each stimulation condition (anode, cathode, and sham). The dependent variable is reaction times (rt)</p>
<p>I used this model but I am... | 2019-03-12 15:17:29.430000+00:00 | 2022-01-16 21:30:10.213000+00:00 | 2022-01-16 21:30:10.213000+00:00 | r|statistics|regression|lme4|mixed-models | ['https://arxiv.org/pdf/1506.04967.pdf', 'https://stats.stackexchange.com/questions/378939/dealing-with-singular-fit-in-mixed-models/379068#379068', 'https://stats.stackexchange.com/questions/449095/how-to-simplify-a-singular-random-structure-when-reported-correlations-are-not-n/449122#449122'] | 3 |
44,602,084 | <p>A reduction from 0,1 knapsack to subset-sum is described in Theorem 2 of the paper <a href="https://arxiv.org/abs/1208.4225" rel="nofollow noreferrer">"Reducing a Target Interval to a Few Exact Queries"</a>. It proceeds in three steps. First, reduce knapsack to a decision problem that tests whether there is a subs... | 2017-06-17 07:45:03.333000+00:00 | 2017-06-17 07:45:03.333000+00:00 | null | null | 44,167,855 | <p>I know how to reduce subset sum to 0,1 knapsack. But is it possible to reduce knapsack to subset sum? How?</p> | 2017-05-24 20:24:42.180000+00:00 | 2020-07-08 17:30:08.993000+00:00 | null | knapsack-problem|reduction|np-complete|subset-sum | ['https://arxiv.org/abs/1208.4225'] | 1 |
62,038,806 | <p>Every programing language that takes concurrency seriously needs a memory model - and here is why.</p>
<blockquote>
<p>The memory model is the crux of the concurrency semantics of shared-memory systems. It defines the possible values that a read operation is allowed to return for any given set of write operations... | 2020-05-27 08:35:51.610000+00:00 | 2020-05-27 08:35:51.610000+00:00 | null | null | 62,036,945 | <p>Java's multithreaded code is finally mapped to the operating system thread for execution. </p>
<p>Is the operating system thread not thread safe? </p>
<p>Why use the Java memory model to ensure thread safety?Why define the Java memory model? </p>
<p>I hope someone can answer this question, I have looked up a... | 2020-05-27 06:42:51.037000+00:00 | 2020-05-27 13:43:10.213000+00:00 | null | java|multithreading|jvm|memory-model|java-memory-model | ['https://arxiv.org/pdf/1803.04432.pdf'] | 1 |
48,084,719 | <p>There are at least 5 reasons which might cause such behavior:</p>
<ol>
<li><p><strong>Outliers:</strong> imagine that you have 10 exactly the same images and 9 out of them belong to class <strong>A</strong> and one belongs to class <strong>B</strong>. In this case, a model will start to assign a high probability of... | 2018-01-03 20:33:59.797000+00:00 | 2018-01-03 20:33:59.797000+00:00 | null | null | 48,025,267 | <p>Is it practically possible to have decreasing loss and decreasing accuracy at each epoch when training a CNN model?
I am getting the below result while training.</p>
<p><a href="https://i.stack.imgur.com/LL9k4.png" rel="noreferrer"><img src="https://i.stack.imgur.com/LL9k4.png" alt="enter image description here"></... | 2017-12-29 16:10:48.313000+00:00 | 2019-08-25 10:48:02.777000+00:00 | 2019-08-25 10:48:02.777000+00:00 | machine-learning|neural-network|deep-learning|keras|conv-neural-network | ['https://keras.io/callbacks/#reducelronplateau', 'https://arxiv.org/abs/1512.00567'] | 2 |
2,352,873 | <p><em><strong>About an O(n) time, O(1) space algorithm for out-shuffle</strong></em></p>
<hr />
<p>Doing an out-shuffle in O(n) time and O(1) space is possible, but it is <em><strong>tough</strong></em>. Not sure why people think it is easy and are suggesting you try something else.</p>
<p>The following paper has an O... | 2010-02-28 22:07:37.763000+00:00 | 2021-02-05 06:40:35.520000+00:00 | 2021-02-05 06:40:35.520000+00:00 | null | 2,352,542 | <p>I am preparing for a software job interview, and I am having trouble with in-place array modifications.</p>
<p>For example, in the out-shuffle problem you interleave two halves of an array so that <code>1 2 3 4 5 6 7 8</code> would become <code>1 5 2 6 3 7 4 8</code>. <a href="http://www.careercup.com/question?id=30... | 2010-02-28 20:29:38.973000+00:00 | 2021-02-05 07:31:20.587000+00:00 | 2021-02-05 06:42:07.533000+00:00 | algorithm|arrays|in-place | ['http://arxiv.org/PS_cache/arxiv/pdf/0805/0805.1598v1.pdf'] | 1 |
69,530,100 | <p>The Federated Learning for Image Classification tutorial would be a good start to learn TFF: <a href="https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification" rel="noreferrer">https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification</a> The "P... | 2021-10-11 17:13:13.587000+00:00 | 2021-10-11 17:13:13.587000+00:00 | null | null | 69,499,432 | <p>I am a newbie in federated learning and just getting to know TensorFlow Federated TFF framework. I have some questions in my mind I would be really appreciated it if anybody can clarify them:</p>
<ol>
<li>Does Federated Averaging algorithm the only aggregation algorithm supported in TFF? and how it differs from Fede... | 2021-10-08 17:03:26.013000+00:00 | 2021-10-11 17:13:13.587000+00:00 | null | tensorflow|tensorflow-federated|federated-learning | ['https://www.tensorflow.org/federated/tutorials/federated_learning_for_image_classification', 'https://arxiv.org/abs/1912.04977', 'https://www.tensorflow.org/federated/tutorials/building_your_own_federated_learning_algorithm'] | 3 |
34,654,016 | <p>The depth of the network is not the only thing that affects its number of parameters. The number of parameters per layer has a huge effect. This means that for each convolutional layer, the size of the filter and the number of filters (features learned) would make a huge difference. Have a look at the paper of the V... | 2016-01-07 11:29:23.583000+00:00 | 2016-01-07 11:35:44.940000+00:00 | 2016-01-07 11:35:44.940000+00:00 | null | 34,650,307 | <p>I trained two convolutional neural nets in Keras. The first is net is as below</p>
<pre><code>def VGG1(weights_path):
model = Sequential()
model.add(Convolution2D(nb_filters, nb_conv, nb_conv,
border_mode='valid',
input_shape=(1, img_rows, img_cols)))
mode... | 2016-01-07 08:21:26.943000+00:00 | 2016-01-13 03:35:33.307000+00:00 | 2016-01-13 03:35:33.307000+00:00 | python|machine-learning|conv-neural-network|keras | ['http://arxiv.org/pdf/1409.1556.pdf', 'http://arxiv.org/pdf/1512.03385v1.pdf'] | 2 |
46,888,556 | <p>Yes, you could do it based on <a href="https://arxiv.org/pdf/1703.06870.pdf" rel="nofollow noreferrer">this paper</a></p>
<p>There is an implementation <a href="https://github.com/CharlesShang/FastMaskRCNN" rel="nofollow noreferrer">here</a> but note that this is not related to the object detection API.</p> | 2017-10-23 11:45:06.567000+00:00 | 2017-10-23 11:45:06.567000+00:00 | null | null | 46,784,295 | <p>in TensorFlow object detection API, is there any possibility to get other shape of recognized object than rectangle? </p>
<p>For example when I'm teaching my computer to recognize a forest, mostly it should be more useful to get some kind of polygon instead of rectangle. </p>
<p>Other thing is for example river, b... | 2017-10-17 07:03:07.817000+00:00 | 2019-03-11 11:41:56.523000+00:00 | null | python|tensorflow|object-detection|object-detection-api | ['https://arxiv.org/pdf/1703.06870.pdf', 'https://github.com/CharlesShang/FastMaskRCNN'] | 2 |
46,784,633 | <p>The simple answer is no. This framework based on <a href="https://arxiv.org/abs/1506.01497" rel="nofollow noreferrer">Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks</a> and <a href="https://arxiv.org/abs/1512.02325" rel="nofollow noreferrer">SSD: Single Shot MultiBox Detector</a> whic... | 2017-10-17 07:23:45.300000+00:00 | 2017-10-17 07:23:45.300000+00:00 | null | null | 46,784,295 | <p>in TensorFlow object detection API, is there any possibility to get other shape of recognized object than rectangle? </p>
<p>For example when I'm teaching my computer to recognize a forest, mostly it should be more useful to get some kind of polygon instead of rectangle. </p>
<p>Other thing is for example river, b... | 2017-10-17 07:03:07.817000+00:00 | 2019-03-11 11:41:56.523000+00:00 | null | python|tensorflow|object-detection|object-detection-api | ['https://arxiv.org/abs/1506.01497', 'https://arxiv.org/abs/1512.02325'] | 2 |
57,845,877 | <p>It highly depends on your dataset and is part of the data scientist's job to find which model is more suitable for a particular task in terms of selected performance metric, training cost, model complexity etc.</p>
<p>When you work on the problem you will probably test all of the above models and compare them. Whic... | 2019-09-08 21:28:37.183000+00:00 | 2020-02-21 02:07:23.390000+00:00 | 2020-02-21 02:07:23.390000+00:00 | null | 57,845,439 | <p>I'm trying to train a model for a sentence classification task. The input is a sentence (a vector of integers) and the output is a label (0 or 1). I've seen some articles here and there about using Bert and GPT2 for text classification tasks. However, I'm not sure which one should I pick to start with. Which of thes... | 2019-09-08 20:14:09.827000+00:00 | 2021-07-08 00:48:27.637000+00:00 | null | tensorflow|nlp|language-model|bert-language-model | ['https://www.deeplearning.ai/machine-learning-yearning/', 'https://arxiv.org/abs/1801.06146'] | 2 |
34,696,626 | <p>Caffeine does not implement LRU as its cache eviction policy. Instead, Caffeine uses a policy called <a href="http://arxiv.org/pdf/1512.00727.pdf" rel="noreferrer">TinyLFU</a>. The Caffeine documentation includes a page on <a href="https://github.com/ben-manes/caffeine/wiki/Efficiency" rel="noreferrer">Efficiency<... | 2016-01-09 17:26:56.323000+00:00 | 2016-01-10 23:38:37.203000+00:00 | 2017-05-23 11:46:16.967000+00:00 | null | 34,694,920 | <p>I'm trying to use Caffeine as LRU cache, so entries that were added first will be evicted first.
Ran this code:</p>
<pre><code>final Cache<Object, Object> map = Caffeine.newBuilder()
.maximumSize(10)
.initialCapacity(10)
.build();
for (long i=0; i<20;i++) {
map.... | 2016-01-09 14:51:15.053000+00:00 | 2016-01-14 05:24:38.227000+00:00 | 2016-01-14 05:24:38.227000+00:00 | java|lru|caffeine | ['http://arxiv.org/pdf/1512.00727.pdf', 'https://github.com/ben-manes/caffeine/wiki/Efficiency', 'http://docs.oracle.com/javase/7/docs/api/java/util/LinkedHashMap.html', 'http://docs.oracle.com/javase/7/docs/api/java/util/LinkedHashMap.html#removeEldestEntry(java.util.Map.Entry)', 'https://code.google.com/p/guava-libra... | 6 |
13,221,327 | <p>It's possible to train tesseract to recognize handwriting. Here are the instructions: <a href="https://tesseract-ocr.github.io/tessdoc/Training-Tesseract" rel="nofollow noreferrer">https://tesseract-ocr.github.io/tessdoc/Training-Tesseract</a></p>
<p>But don't expect very good results. Academics have typically got... | 2012-11-04 18:03:56.083000+00:00 | 2021-05-06 01:28:11.220000+00:00 | 2021-05-06 01:28:11.220000+00:00 | null | 12,310,287 | <p>I have a dictionary of words in a text file, separated by newlines. And I want to recognize the handwriting using Tesseract, and output the nearest matching line in the text file.</p>
<p>This is the first time I'll be using Tesseract, and it's already in my project workspace, I just need the training data.</p>
<p>... | 2012-09-07 00:39:20.670000+00:00 | 2021-05-06 01:28:11.220000+00:00 | 2012-09-07 03:41:52.583000+00:00 | android|tesseract|handwriting | ['https://tesseract-ocr.github.io/tessdoc/Training-Tesseract', 'https://arxiv.org/pdf/1003.5893.pdf', 'https://arxiv.org/pdf/1003.5898.pdf'] | 3 |
61,700,910 | <p>Learning rates of the client and server optimizer can be very important in determining the final accuracy of the model.</p>
<p>Very high client learning rates can result in high <em>training</em> accuracy, since the <em>client local</em> training process is overfitting to the <em>client local</em> data (the two cla... | 2020-05-09 17:28:22.087000+00:00 | 2020-05-09 17:28:22.087000+00:00 | null | null | 61,219,479 | <p>I create a Non-IID data set where I divide 60000 examples(10 classes and every class has 6000 examples) to 200 fragments, and every fragment has 300 examples. There are 100 clients and I allocate 2 fragments randomly to every client. This is the situation of some clients.
<a href="https://i.stack.imgur.com/P8Uhu.p... | 2020-04-15 00:35:21.863000+00:00 | 2020-05-09 17:28:22.087000+00:00 | null | python|tensorflow|imbalanced-data|tensorflow-federated | ['https://arxiv.org/abs/2003.00295'] | 1 |
55,455,537 | <p>You can learn more in the publication by <a href="https://arxiv.org/abs/1301.3781" rel="nofollow noreferrer">Tomas Mikolov</a> where he introduces vector embedding called Word2Vec.</p>
<p>Idea behind embedding is to compress sparse vector into a low dimensional dense vector. Lets consider a dictionary of 100k words... | 2019-04-01 12:49:28.187000+00:00 | 2019-04-02 06:32:13.187000+00:00 | 2019-04-02 06:32:13.187000+00:00 | null | 55,453,716 | <p>In this <a href="https://keras.io/layers/embeddings/" rel="nofollow noreferrer">documentation</a> it says that it "Turns positive integers (indexes) into dense vectors of fixed size. eg. [[4], [20]] -> [[0.25, 0.1], [0.6, -0.2]]"</p>
<p>Could anyone please make it clearer? </p>
<p>How does the function convert int... | 2019-04-01 11:08:17.763000+00:00 | 2019-04-02 06:53:35.247000+00:00 | 2019-04-02 06:53:35.247000+00:00 | python|tensorflow|keras | ['https://arxiv.org/abs/1301.3781'] | 1 |
52,613,961 | <p>You can use Autoencoder on Textual data as explained <a href="https://machinelearningmastery.com/encoder-decoder-models-text-summarization-keras/" rel="nofollow noreferrer">here</a>.</p>
<p>Autoencoder usually worked better on image data but recent approaches changed the autoencoder in a way it is also good on the ... | 2018-10-02 18:05:00.933000+00:00 | 2018-10-02 19:18:26.457000+00:00 | 2018-10-02 19:18:26.457000+00:00 | null | 33,794,615 | <p>I am doing my project based on health care.I am going to train my autoencoders with the symptoms and the diseases i.e my input is in textual form. Will that work? (I am using Rstudio).Please anyone help me with this</p> | 2015-11-19 03:14:28.260000+00:00 | 2018-10-02 19:18:26.457000+00:00 | null | autoencoder | ['https://machinelearningmastery.com/encoder-decoder-models-text-summarization-keras/', 'https://arxiv.org/abs/1705.02033'] | 2 |
65,784,745 | <p>The <strong>time-distributed dense layer</strong> as name suggested just an ordinary dense layer that apply to every temporal slice of an input, you can think it as special form of <strong>RNN cell</strong>, i.e without recurrent hidden state.</p>
<p>So you can using any layer that is time-distributed as your output... | 2021-01-19 02:32:08.730000+00:00 | 2021-01-19 02:32:08.730000+00:00 | null | null | 65,783,297 | <p>I am trying to get a LSTM autoencoder to recreate its inputs. So far I have:</p>
<pre><code>class getSequence(nn.Module):
def forward(self, x):
out, _ = x
return out
class getLast(nn.Module):
def forward(self, x):
out, states = x
states = states[len(states) - 1]
retu... | 2021-01-18 23:06:26.273000+00:00 | 2021-01-19 02:32:08.730000+00:00 | null | deep-learning|pytorch|recurrent-neural-network|autoencoder | ['https://arxiv.org/abs/1502.04681', 'https://i.stack.imgur.com/Mffem.png'] | 2 |
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