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- ShortScience.org is a platform for post-publication discussion aiming to improve accessibility and reproducibility of research ideas.
- The website has 1583 public summaries, mostly in machine learning, written by the community and organized by paper, conference, and year.
- Reading summaries of papers is useful to obtain the perspective and insight of another reader, why they liked or disliked it, and their attempt to demystify complicated sections.
- Also, writing summaries is a good exercise to understand the content of a paper because you are forced to challenge your assumptions when explaining it.
- Finally, you can keep up to date with the flood of research by reading the latest summaries on our Twitter and Facebook pages.

Comparing Rewinding and Fine-tuning in Neural Network Pruning

Renda, Alex and Frankle, Jonathan and Carbin, Michael

International Conference on Learning Representations - 2020 via Local Bibsonomy

Keywords: dblp

Renda, Alex and Frankle, Jonathan and Carbin, Michael

International Conference on Learning Representations - 2020 via Local Bibsonomy

Keywords: dblp

[link]
This is an interestingly pragmatic paper that makes a super simple observation. Often, we may want a usable network with fewer parameters, to make our network more easily usable on small devices. It's been observed (by these same authors, in fact), that pruned networks can achieve comparable weights to their fully trained counterparts if you rewind and retrain from early in the training process, to compensate for the loss of the (not ultimately important) pruned weights. This observation has been dubbed the "Lottery Ticket Hypothesis", after the idea that there's some small effective subnetwork you can find if you sample enough networks. Given these two facts - the usefulness of pruning, and the success of weight rewinding - the authors explore the effectiveness of various ways to train after pruning. Current standard practice is to prune low-magnitude weights, and then continue training remaining weights from values they had at pruning time, keeping the final learning rate of the network constant. The authors find that: 1. Weight rewinding, where you rewind weights to *near* their starting value, and then retrain using the learning rates of early in training, outperforms fine tuning from the place weights were when you pruned but, also 2. Learning rate rewinding, where you keep weights as they are, but rewind learning rates to what they were early in training, are actually the most effective for a given amount of training time/search cost To me, this feels a little bit like burying the lede: the takeaway seems to be that when you prune, it's beneficial to make your network more "elastic" (in the metaphor-to-neuroscience sense) so it can more effectively learn to compensate for the removed neurons. So, what was really valuable in weight rewinding was the ability to "heat up" learning on a smaller set of weights, so they could adapt more quickly. And the fact that learning rate rewinding works better than weight rewinding suggests that there is value in the learned weights after all, that value is just outstripped by the benefit of rolling back to old learning rates. All in all, not a super radical conclusion, but a useful and practical one to have so clearly laid out in a paper. |

Variational Policy Search via Trajectory Optimization

Levine, Sergey and Koltun, Vladlen

Neural Information Processing Systems Conference - 2013 via Local Bibsonomy

Keywords: dblp

Levine, Sergey and Koltun, Vladlen

Neural Information Processing Systems Conference - 2013 via Local Bibsonomy

Keywords: dblp

[link]
The paper introduces a new approach of how classical policy search can be combined and improved with trajectory optimization methods serving as exploration strategy. An optimization criteria with the goal of finding optimal policy parameters is decomposed with a variational approach. The variational distribution is approximated as Gaussian distribution which allows a solution with the iterative LQR algorithm. The overall algorithm uses expectation maximization to iterate between minimizing the KL divergence of the variational decomposition and maximizing the lower bound with respect to the policy parameters. |

Convolutional Neural Networks for Sentence Classification

Kim, Yoon

arXiv e-Print archive - 2014 via Local Bibsonomy

Keywords: dblp

Kim, Yoon

arXiv e-Print archive - 2014 via Local Bibsonomy

Keywords: dblp

[link]
#### Introduction * The paper demonstrates how simple CNNs, built on top of word embeddings, can be used for sentence classification tasks. * [Link to the paper](https://arxiv.org/abs/1408.5882) * [Implementation](https://github.com/shagunsodhani/CNN-Sentence-Classifier) #### Architecture * Pad input sentences so that they are of the same length. * Map words in the padded sentence using word embeddings (which may be either initialized as zero vectors or initialized as word2vec embeddings) to obtain a matrix corresponding to the sentence. * Apply convolution layer with multiple filter widths and feature maps. * Apply max-over-time pooling operation over the feature map. * Concatenate the pooling results from different layers and feed to a fully-connected layer with softmax activation. * Softmax outputs probabilistic distribution over the labels. * Use dropout for regularisation. #### Hyperparameters * RELU activation for convolution layers * Filter window of 3, 4, 5 with 100 feature maps each. * Dropout - 0.5 * Gradient clipping at 3 * Batch size - 50 * Adadelta update rule. #### Variants * CNN-rand * Randomly initialized word vectors. * CNN-static * Uses pre-trained vectors from word2vec and does not update the word vectors. * CNN-non-static * Same as CNN-static but updates word vectors during training. * CNN-multichannel * Uses two set of word vectors (channels). * One set is updated and other is not updated. #### Datasets * Sentiment analysis datasets for Movie Reviews, Customer Reviews etc. * Classification data for questions. * Maximum number of classes for any dataset - 6 #### Strengths * Good results on benchmarks despite being a simple architecture. * Word vectors obtained by non-static channel have more meaningful representation. #### Weakness * Small data with few labels. * Results are not very detailed or exhaustive. |

Understanding deep learning requires rethinking generalization

Chiyuan Zhang and Samy Bengio and Moritz Hardt and Benjamin Recht and Oriol Vinyals

arXiv e-Print archive - 2016 via Local arXiv

Keywords: cs.LG

**First published:** 2016/11/10 (7 years ago)

**Abstract:** Despite their massive size, successful deep artificial neural networks can
exhibit a remarkably small difference between training and test performance.
Conventional wisdom attributes small generalization error either to properties
of the model family, or to the regularization techniques used during training.
Through extensive systematic experiments, we show how these traditional
approaches fail to explain why large neural networks generalize well in
practice. Specifically, our experiments establish that state-of-the-art
convolutional networks for image classification trained with stochastic
gradient methods easily fit a random labeling of the training data. This
phenomenon is qualitatively unaffected by explicit regularization, and occurs
even if we replace the true images by completely unstructured random noise. We
corroborate these experimental findings with a theoretical construction showing
that simple depth two neural networks already have perfect finite sample
expressivity as soon as the number of parameters exceeds the number of data
points as it usually does in practice.
We interpret our experimental findings by comparison with traditional models.
more
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Chiyuan Zhang and Samy Bengio and Moritz Hardt and Benjamin Recht and Oriol Vinyals

arXiv e-Print archive - 2016 via Local arXiv

Keywords: cs.LG

[link]
This paper deals with the question what / how exactly CNNs learn, considering the fact that they usually have more trainable parameters than data points on which they are trained. When the authors write "deep neural networks", they are talking about Inception V3, AlexNet and MLPs. ## Key contributions * Deep neural networks easily fit random labels (achieving a training error of 0 and a test error which is just randomly guessing labels as expected). $\Rightarrow$Those architectures can simply brute-force memorize the training data. * Deep neural networks fit random images (e.g. Gaussian noise) with 0 training error. The authors conclude that VC-dimension / Rademacher complexity, and uniform stability are bad explanations for generalization capabilities of neural networks * The authors give a construction for a 2-layer network with $p = 2n+d$ parameters - where $n$ is the number of samples and $d$ is the dimension of each sample - which can easily fit any labeling. (Finite sample expressivity). See section 4. ## What I learned * Any measure $m$ of the generalization capability of classifiers $H$ should take the percentage of corrupted labels ($p_c \in [0, 1]$, where $p_c =0$ is a perfect labeling and $p_c=1$ is totally random) into account: If $p_c = 1$, then $m()$ should be 0, too, as it is impossible to learn something meaningful with totally random labels. * We seem to have built models which work well on image data in general, but not "natural" / meaningful images as we thought. ## Funny > deep neural nets remain mysterious for many reasons > Note that this is not exactly simple as the kernel matrix requires 30GB to store in memory. Nonetheless, this system can be solved in under 3 minutes in on a commodity workstation with 24 cores and 256 GB of RAM with a conventional LAPACK call. ## See also * [Deep Nets Don't Learn Via Memorization](https://openreview.net/pdf?id=rJv6ZgHYg) |

Mask R-CNN

He, Kaiming and Gkioxari, Georgia and Dollár, Piotr and Girshick, Ross B.

arXiv e-Print archive - 2017 via Local Bibsonomy

Keywords: dblp

He, Kaiming and Gkioxari, Georgia and Dollár, Piotr and Girshick, Ross B.

arXiv e-Print archive - 2017 via Local Bibsonomy

Keywords: dblp

[link]
Mask RCNN takes off from where Faster RCNN left, with some augmentations aimed at bettering instance segmentation (which was out of scope for FRCNN). Instance segmentation was achieved remarkably well in *DeepMask* , *SharpMask* and later *Feature Pyramid Networks* (FPN). Faster RCNN was not designed for pixel-to-pixel alignment between network inputs and outputs. This is most evident in how RoIPool , the de facto core operation for attending to instances, performs coarse spatial quantization for feature extraction. Mask RCNN fixes that by introducing RoIAlign in place of RoIPool. #### Methodology Mask RCNN retains most of the architecture of Faster RCNN. It adds the a third branch for segmentation. The third branch takes the output from RoIAlign layer and predicts binary class masks for each class. ##### Major Changes and intutions **Mask prediction** Mask prediction segmentation predicts a binary mask for each RoI using fully convolution - and the stark difference being usage of *sigmoid* activation for predicting final mask instead of *softmax*, implies masks don't compete with each other. This *decouples* segmentation from classification. The class prediction branch is used for class prediction and for calculating loss, the mask of predicted loss is used calculating Lmask. Also, they show that a single class agnostic mask prediction works almost as effective as separate mask for each class, thereby supporting their method of decoupling classification from segmentation **RoIAlign** RoIPool first quantizes a floating-number RoI to the discrete granularity of the feature map, this quantized RoI is then subdivided into spatial bins which are themselves quantized, and finally feature values covered by each bin are aggregated (usually by max pooling). Instead of quantization of the RoI boundaries or bin bilinear interpolation is used to compute the exact values of the input features at four regularly sampled locations in each RoI bin, and aggregate the result (using max or average). **Backbone architecture** Faster RCNN uses a VGG like structure for extracting features from image, weights of which were shared among RPN and region detection layers. Herein, authors experiment with 2 backbone architectures - ResNet based VGG like in FRCNN and ResNet based [FPN](http://www.shortscience.org/paper?bibtexKey=journals/corr/LinDGHHB16) based. FPN uses convolution feature maps from previous layers and recombining them to produce pyramid of feature maps to be used for prediction instead of single-scale feature layer (final output of conv layer before connecting to fc layers was used in Faster RCNN) **Training Objective** The training objective looks like this ![](https://i.imgur.com/snUq73Q.png) Lmask is the addition from Faster RCNN. The method to calculate was mentioned above #### Observation Mask RCNN performs significantly better than COCO instance segmentation winners *without any bells and whiskers*. Detailed results are available in the paper |

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