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Reweighted Proximal Pruning for Large-Scale Language Representation | 1 INTRODUCTION . Pre-trained language representations such as GPT ( Radford et al. , 2018 ) , BERT ( Devlin et al. , 2019 ) and XLNet ( Yang et al. , 2019 ) , have shown substantial performance improvements using self-supervised training on large-scale corpora ( Dai & Le , 2015 ; Peters et al. , 2018 ; Radford et al. ,... | Models such as BERT are pretrained language models which provide significant improvement for different tasks, however they suffer from high huge size and complexity. This paper has proposed using proximal gradient descent to find sparse weights for BERT to reduce the number of parameters and make the model smaller. The... | SP:fd30a45391475363c65ab80a809f654676cbea71 |
Reweighted Proximal Pruning for Large-Scale Language Representation | 1 INTRODUCTION . Pre-trained language representations such as GPT ( Radford et al. , 2018 ) , BERT ( Devlin et al. , 2019 ) and XLNet ( Yang et al. , 2019 ) , have shown substantial performance improvements using self-supervised training on large-scale corpora ( Dai & Le , 2015 ; Peters et al. , 2018 ; Radford et al. ,... | The paper proposes a new approach to prune weights that is designed keeping large scale pre-trained language representations like BERT. Such a method is desirable for deploying such models on devices with limited memory like phones etc. Experiments on Squad and Glue datasets show that a pruned version of the model main... | SP:fd30a45391475363c65ab80a809f654676cbea71 |
Network Pruning for Low-Rank Binary Index | 1 INTRODUCTION . Numerous parameter pruning techniques have been introduced based on the observation that significant amounts of connectivity can be removed without sacrificing model accuracy . Current active research strives to enhance pruning rate at the cost of additional computations ( Guo et al. , 2016 ; Molchanov... | The paper addresses the problem of reducing the computational complexity of neural network pruning. Main idea is to compute a low-rank approximation of the binary index matrix used to represent the structure of the pruned network. In the considered setup, the binary index matrix is the (sparse) boolean matrix associate... | SP:8323e9c866137e2f5c7a692bbeb89cce8d2fd6df |
Network Pruning for Low-Rank Binary Index | 1 INTRODUCTION . Numerous parameter pruning techniques have been introduced based on the observation that significant amounts of connectivity can be removed without sacrificing model accuracy . Current active research strives to enhance pruning rate at the cost of additional computations ( Guo et al. , 2016 ; Molchanov... | This paper proposed a new network pruning method that generates a low-rank binary index matrix to compress index data, and a tile-based factorization technique to save memory. The binary index can achieve larger compression ratio than the CSR index, and the low-rank binary index can further reduce memory usage. The re... | SP:8323e9c866137e2f5c7a692bbeb89cce8d2fd6df |
Constant Time Graph Neural Networks | 1 INTRODUCTION . Machine learning on graph structures has various applications such as chemo-informatics ( Gilmer et al. , 2017 ) , question answering systems ( Schlichtkrull et al. , 2018 ) , and recommender systems ( Fan et al. , 2019 ) . Recently , a novel machine learning model for graph data called graph neural ne... | In this paper, the authors propose a constant-time approximation for graph convolution operation via theoretical analysis on the number of sampling from each neighbor. The authors prove that both node embedding and gradient can be approximated via constant number of samples among the neighbors. Extensive experiments ar... | SP:d68fbbd734099992d67e10a0b15483ae7359ec52 |
Constant Time Graph Neural Networks | 1 INTRODUCTION . Machine learning on graph structures has various applications such as chemo-informatics ( Gilmer et al. , 2017 ) , question answering systems ( Schlichtkrull et al. , 2018 ) , and recommender systems ( Fan et al. , 2019 ) . Recently , a novel machine learning model for graph data called graph neural ne... | In this paper, the authors provide a theoretical framework for characterizing the approximation guarantees provided by node sampling to estimate embeddings in various GNN architectures. In particular, they prove several PAC learning-style bounds on the embedding and gradient estimation when using node sampling approach... | SP:d68fbbd734099992d67e10a0b15483ae7359ec52 |
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization | 1 INTRODUCTION . The remarkable empirical success of deep neural networks is often attributed to the availability of large data sets for training . However , sample size does not provide a comprehensive rationale since complex models often outperform simple ones on a given data set , even when the model size exceeds th... | This paper studies the inductive bias in deep neural networks. The authors train several FF image recognition networks and many CNN variants on a single image, and observe that most networks fall into one of two categories: either memorizing the output of the single training sample, or learning the identity function an... | SP:a199489c162c1b433e63d5aeee3d69ada321d32a |
Identity Crisis: Memorization and Generalization Under Extreme Overparameterization | 1 INTRODUCTION . The remarkable empirical success of deep neural networks is often attributed to the availability of large data sets for training . However , sample size does not provide a comprehensive rationale since complex models often outperform simple ones on a given data set , even when the model size exceeds th... | This paper studies the inductive bias of neural nets by considering the toy example of learning an identity map through a single data point (and hence the NNs are always overparametrized). The authors compare CNNs versus FCNs, and find that CNNs tend to “generalize” in terms of actually learning the concept of an ident... | SP:a199489c162c1b433e63d5aeee3d69ada321d32a |
PCMC-Net: Feature-based Pairwise Choice Markov Chains | 1 INTRODUCTION . Choice modeling aims at finding statistical models capturing the human behavior when faced with a set of alternatives . Classical examples include consumer purchasing decisions , choices of schooling or employment , and commuter choices for modes of transportation among available options . Traditional ... | This paper introduces a novel approximate inference method, called PCMC-Net, for models from the family of Pairwise Choice Markov Chains (PCMC). The method relies on training a neural network. Consequently, the authors claim that inference is amortized, but its computational complexity is still quadratic in the number ... | SP:e4f04edbe1885c93875d82d78d3e0cf2d0359393 |
PCMC-Net: Feature-based Pairwise Choice Markov Chains | 1 INTRODUCTION . Choice modeling aims at finding statistical models capturing the human behavior when faced with a set of alternatives . Classical examples include consumer purchasing decisions , choices of schooling or employment , and commuter choices for modes of transportation among available options . Traditional ... | This paper enables a feature-based parametrization and amortized inference of Pairwise Choice Markov Chains (PCMCs), a model for decisions in the face of a set of alternative choices (e.g. the rock-paper-scissors game). Previous approaches to fitting PCMCs have leveraged sequential least squares programming, making op... | SP:e4f04edbe1885c93875d82d78d3e0cf2d0359393 |
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems | 1 INTRODUCTION . First-order methods such as Stochastic Gradient Descent ( SGD ) are currently the standard choice for training deep neural networks . The merit of first-order methods is obvious : they only calculate the gradient and therefore are computationally efficient . In addition to better computational efficien... | The authors propose a scalable second order method for optimization using a quadratic loss. The method is inspired by the Neural Tangent kernel approach, which also allows them to provide global convergence rates for GD and batch SGD. The algorithm has a computational complexity that is linear in the number of paramete... | SP:7d6388235c53030aa92499c15b4543f82b9ff27c |
Gram-Gauss-Newton Method: Learning Overparameterized Neural Networks for Regression Problems | 1 INTRODUCTION . First-order methods such as Stochastic Gradient Descent ( SGD ) are currently the standard choice for training deep neural networks . The merit of first-order methods is obvious : they only calculate the gradient and therefore are computationally efficient . In addition to better computational efficien... | Authors propose minimizing neural network using kernel ridge regression. (Formula 9 and Algorithm 1). Main difference of this method is compared to Gauss-Newton, is that it uses JJ' as curvature, which has dimensions b-by-by (batch size b), instead of J'J as curvature, which has dimensions m-by-m (number of parameters ... | SP:7d6388235c53030aa92499c15b4543f82b9ff27c |
Switched linear projections and inactive state sensitivity for deep neural network interpretability | We introduce switched linear projections for expressing the activity of a neuron in a ReLU-based deep neural network in terms of a single linear projection in the input space . The method works by isolating the active subnetwork , a series of linear transformations , that completely determine the entire computation of ... | This manuscript introduces a novel method to explain activities of ReLU-based deep networks by constructing a linear subnetwork which only contains neurons activated by the input. The status of each neuron can be obtained given any input sample. Moreover, the author applies the notion of “neuron’s center”, which is a n... | SP:7d3b2759cf0b3dfc61a0de94781dc460b29b5b84 |
Switched linear projections and inactive state sensitivity for deep neural network interpretability | We introduce switched linear projections for expressing the activity of a neuron in a ReLU-based deep neural network in terms of a single linear projection in the input space . The method works by isolating the active subnetwork , a series of linear transformations , that completely determine the entire computation of ... | The work basically introduced a new way of looking at interpretability; instead of focusing on the source of activations in the network for a given input image, focus on the source of stability (non-active) neurons (in a ReLU network). The work starts by proving (although it is trivial) that in a ReLU (more generally a... | SP:7d3b2759cf0b3dfc61a0de94781dc460b29b5b84 |
Bio-Inspired Hashing for Unsupervised Similarity Search | The fruit fly Drosophila ’ s olfactory circuit has inspired a new locality sensitive hashing ( LSH ) algorithm , FlyHash . In contrast with classical LSH algorithms that produce low dimensional hash codes , FlyHash produces sparse high-dimensional hash codes and has also been shown to have superior empirical performanc... | This paper studies a new model of locally sensitive hashing (LSH) that is inspired by the fruit fly Drosophila's olfactory circuit. Instead of mapping each input to a low-dimensional space, such LSH methods (FlyHash) map given $d$-dimensional inputs to an $m$-dimensional space such that $m \gg d$. However, these mappin... | SP:97237fefef6ae3891e0d3558373cccf759002ab5 |
Bio-Inspired Hashing for Unsupervised Similarity Search | The fruit fly Drosophila ’ s olfactory circuit has inspired a new locality sensitive hashing ( LSH ) algorithm , FlyHash . In contrast with classical LSH algorithms that produce low dimensional hash codes , FlyHash produces sparse high-dimensional hash codes and has also been shown to have superior empirical performanc... | This paper introduces a variant of FlyHash for similarity search in vector space. The basic idea is motivated by the intuition: the original FlyHash method is data-independent, so can we improve FlyHash's locality sensitivity by learning from data. It does so by learning the weights of the projection layer and uses win... | SP:97237fefef6ae3891e0d3558373cccf759002ab5 |
Model-Agnostic Feature Selection with Additional Mutual Information | 1 INTRODUCTION . Model interpretation techniques aim to select features important for a response by reducing models ( sometimes locally ) to be human interpretable . However , the phrase model interpretation can be a bit of a misnomer . Any interpretation of a model must be imbued to the model by the population distrib... | This paper addresses supervised feature selection: given a D-dimensional input variable x = (x_1, ..., x_D), and a response variable y, the goal is to find a subset of "useful" features in x. Here, a feature x_j is useful if it is dependent on y even when conditioning on all other input variables (denoted by x_{-j}, wh... | SP:c71c1b8b8e1a7fe2225d8e232288de3c08f0356c |
Model-Agnostic Feature Selection with Additional Mutual Information | 1 INTRODUCTION . Model interpretation techniques aim to select features important for a response by reducing models ( sometimes locally ) to be human interpretable . However , the phrase model interpretation can be a bit of a misnomer . Any interpretation of a model must be imbued to the model by the population distrib... | This paper presents a method to provide some level of interpretation on the influence of input features on the response of a machine level model all the way down to the instance level. The proposed method is model agnostic. Quoting the authors, they advocate for methods that look at interpretability “as understanding t... | SP:c71c1b8b8e1a7fe2225d8e232288de3c08f0356c |
Estimating counterfactual treatment outcomes over time through adversarially balanced representations | 1 INTRODUCTION . As clinical decision-makers are often faced with the problem of choosing between treatment alternatives for patients , reliably estimating their effects is paramount . While clinical trials represent the gold standard for causal inference , they are expensive , have a few patients and narrow inclusion ... | The paper introduces Counterfactual Recurrent Network (CRN) that is able to estimate the effects of various treatments from longitudinal data. The claim is that the model can decide (i) treatment plan; (ii) optimal time of treatment; and (iii) when to stop treatment. The proposed method attempts to learn time-invariant... | SP:e1844adc4921e5ff485d07de51ca078b21321390 |
Estimating counterfactual treatment outcomes over time through adversarially balanced representations | 1 INTRODUCTION . As clinical decision-makers are often faced with the problem of choosing between treatment alternatives for patients , reliably estimating their effects is paramount . While clinical trials represent the gold standard for causal inference , they are expensive , have a few patients and narrow inclusion ... | This work addresses the problem of causal inference in time-dependent treatment regimes. To address the problem, the authors propose an extension of the balancing representation for causal inference framework that seeks render the current treatment independent from a representation of the history of treatment and confo... | SP:e1844adc4921e5ff485d07de51ca078b21321390 |
Confidence Scores Make Instance-dependent Label-noise Learning Possible | 1 INTRODUCTION . The recent success of deep neural networks has increased the need for high-quality labeled data . However , such a labelling process can be time-consuming and costly . A compromise is to resort to weakly-supervised annotations , using crowdsourcing platforms or trained classifiers that annotate the dat... | This paper focuses on instance-dependent label noise problem, which is a new and important area in learning with noisy labels. The authors propose confidence-scored instance-dependent noise (CSIDN) to overcome strong assumptions on noise models. They clearly define confidence scores and justify their availability. To s... | SP:b258e7150a73085f7bf32c927126a3601210bcec |
Confidence Scores Make Instance-dependent Label-noise Learning Possible | 1 INTRODUCTION . The recent success of deep neural networks has increased the need for high-quality labeled data . However , such a labelling process can be time-consuming and costly . A compromise is to resort to weakly-supervised annotations , using crowdsourcing platforms or trained classifiers that annotate the dat... | Learning with noise labels is a hot topic now due to the reason that deep learning algorithms often require large-scale supervised training samples and labelling a large amount of data is costly. However, almost all of the existing methods assume that the label noise is instance-independent. It either depends on the cl... | SP:b258e7150a73085f7bf32c927126a3601210bcec |
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches | 1 INTRODUCTION . Predicting time-to-event outcomes arises in a variety of applications . For example , in healthcare , we may be interested in predicting how much time a patient has to live . In criminology , we may be interested in predicting when a convicted criminal might reoffend . In e-commerce and on streaming pl... | The paper addresses the problem of survival analysis (predicting time until, e.g., death) using topic modeling. The point of introducing topic modeling here is to gain better insight into what helps predict survival times of unseen test subjects. This contrasts with much of the earlier work in survival analysis, which ... | SP:97bd209e33851a48ea9e5c3cab5c3888438e5189 |
Topic Models with Survival Supervision: Archetypal Analysis and Neural Approaches | 1 INTRODUCTION . Predicting time-to-event outcomes arises in a variety of applications . For example , in healthcare , we may be interested in predicting how much time a patient has to live . In criminology , we may be interested in predicting when a convicted criminal might reoffend . In e-commerce and on streaming pl... | The paper considers the problem of interpreting the predictions for survival analysis using topic models. The classical survival analysis problem assumes each datapoint is a subject (X,Y,\delta) where X is a feature vector and Y is a life time or a censoring time depending on whether the subject is dead (when delta=1) ... | SP:97bd209e33851a48ea9e5c3cab5c3888438e5189 |
EDUCE: Explaining model Decision through Unsupervised Concepts Extraction | 1 INTRODUCTION . While deep learning models are powerful tools to perform a large variety of tasks , their predictive process often remains obscure . Understanding their behavior becomes crucial . This is particularly true with text data , where predicting without justifications has limited applicability . There has be... | The paper introduces a new concept-based interpretability method that lies in the family of self-interpretable models (i.e. it's not a post-hoc method). Self-interpretability is achieved by a two-stage model: First, a concept-extractor finds the related pieces of consecutive words (excerpts) in a given text that are re... | SP:1f205607623dfcb3aa5e21f7d13d3182ba29bad5 |
EDUCE: Explaining model Decision through Unsupervised Concepts Extraction | 1 INTRODUCTION . While deep learning models are powerful tools to perform a large variety of tasks , their predictive process often remains obscure . Understanding their behavior becomes crucial . This is particularly true with text data , where predicting without justifications has limited applicability . There has be... | The authors proposed a self-explainable deep net architecture that could be used for text categorization. The main idea is to force the network to extract "excerpts", from the input text, each corresponds to a concept, which are also learned for interpretation. The classification is finally made based off of the learne... | SP:1f205607623dfcb3aa5e21f7d13d3182ba29bad5 |
Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations | 1 INTRODUCTION . Increasing adoption of machine learning in computer graphics has rapidly decreased the time-frame and skill set needed for convincing photo manipulation . Point-and-click solutions are readily available for plausible object insertion ( Portenier et al. , 2019 ) , removal ( Xiong et al. , 2019 ) , sky r... | The paper describes a pipeline for image compression which allows to reliably detect specific manipulation patterns in compressed images. The results show that it is possible to learn image compression that performs similarly to a modern image compression algorithm while in the same time is optimized to reveal specifi... | SP:d8e30dcadff63f56df4894cb5ba871cb2f8ee0ea |
Quantifying the Cost of Reliable Photo Authentication via High-Performance Learned Lossy Representations | 1 INTRODUCTION . Increasing adoption of machine learning in computer graphics has rapidly decreased the time-frame and skill set needed for convincing photo manipulation . Point-and-click solutions are readily available for plausible object insertion ( Portenier et al. , 2019 ) , removal ( Xiong et al. , 2019 ) , sky r... | This paper presents a learned image compression method that is able to be robust under a variety of tasks. The results aren't state of the art in terms of rate-distortion performance, but this paper has a very good analysis of the results, and has produced a very fast codec. In that sense, this is a very interesting pa... | SP:d8e30dcadff63f56df4894cb5ba871cb2f8ee0ea |
Self-Adversarial Learning with Comparative Discrimination for Text Generation | 1 INTRODUCTION . Generative Adversarial Networks ( Goodfellow et al. , 2014 ) ( GANs ) have achieved tremendous success for image generation and received much attention in computer vision . For text generation , however , the performance of GANs is severely limited due to reward sparsity and mode collapse : reward spar... | This paper introduces a Self-Adversarial Learning (SAL) mechanism in GAN based text generation, aiming at tackling the problem of mode collapse and sparse rewards problem. Specifically, motivated by “self-play” mechanism in RL community, instead of using a binary classifier as discriminator in original GAN, SAL employs... | SP:e3e9a73988c8a2fb968f9ac2739ccac95f5a01bf |
Self-Adversarial Learning with Comparative Discrimination for Text Generation | 1 INTRODUCTION . Generative Adversarial Networks ( Goodfellow et al. , 2014 ) ( GANs ) have achieved tremendous success for image generation and received much attention in computer vision . For text generation , however , the performance of GANs is severely limited due to reward sparsity and mode collapse : reward spar... | To alleviate the issues of reward sparsity and mode collapse in most text-generation GANs with a binary discriminator, this paper proposes a self-adversarial learning (SAL) framework with a novel comparative discriminator that takes pairs of text examples from real and generated examples and outputs better, worse, or i... | SP:e3e9a73988c8a2fb968f9ac2739ccac95f5a01bf |
Robust training with ensemble consensus | Since deep neural networks are over-parameterized , they can memorize noisy examples . We address such a memorization issue in the presence of label noise . From the fact that deep neural networks can not generalize to neighborhoods of memorized features , we hypothesize that noisy examples do not consistently incur sm... | In this paper, the authors proposed to identify noisy training examples using ensemble consensus. The authors argued and demonstrated through numeric studies that, to the contrary of some earlier work, training examples with low training loss are not necessarily mislabeled. Rather, the authors hypothesized that example... | SP:672b4b380be73c57e2e7fd3d9f7ea8af0d98f6d1 |
Robust training with ensemble consensus | Since deep neural networks are over-parameterized , they can memorize noisy examples . We address such a memorization issue in the presence of label noise . From the fact that deep neural networks can not generalize to neighborhoods of memorized features , we hypothesize that noisy examples do not consistently incur sm... | This paper proposes a general method for eliminating noisy labels in supervised learning based on the combination of two ideas: outputs of noisy examples are less robust under noise, and noisy labels are less likely to have a low loss. The authors then propose 3 concrete instantiations of the idea, and do a thorough em... | SP:672b4b380be73c57e2e7fd3d9f7ea8af0d98f6d1 |
Deep Evidential Uncertainty | 1 INTRODUCTION Recent advances in deep supervised learning have yielded super human level performance and precision . While these models empirically generalize well when placed into new test enviornments , they are often easily fooled by adversarial perturbations ( Goodfellow et al. , 2014 ) , and have difficulty under... | This paper proposed deep evidential regression, a method for training neural networks to not only estimate the output but also the associated evidence in support of that output. The main idea follows the evidential deep learning work proposed in (Sensoy et al., 2018) extending it from the classification regime to the r... | SP:3225cc5911de7539e93553a7794975ea4358671e |
Deep Evidential Uncertainty | 1 INTRODUCTION Recent advances in deep supervised learning have yielded super human level performance and precision . While these models empirically generalize well when placed into new test enviornments , they are often easily fooled by adversarial perturbations ( Goodfellow et al. , 2014 ) , and have difficulty under... | This paper proposes a novel approach to estimate the confidence of predictions in a regression setting. The approach starts from the standard modelling assuming iid samples from a Gaussian distribution with unknown mean and variances and places evidential priors (relying on the Dempster-Shafer Theory of Evidence [1] /s... | SP:3225cc5911de7539e93553a7794975ea4358671e |
Adaptive Loss Scaling for Mixed Precision Training | 1 INTRODUCTION . Training deep neural networks ( DNNs ) is well-known to be time and energy consuming , motivating the development of new methods and hardware to make training more efficient . One way to improve training efficiency is to use numerical representations that are more hardware-friendly . This is the reason... | The authors propose an adaptive loss scaling method during the backpropagation stage for the mix precision training to reduce the underflow. Compared with the previous work, which scales the loss by human design, and needs to be consistent in all layers. The authors state that they can decide the scale rate layer by la... | SP:4da7ae6cfcf4cab7581bab283e12354b60d3b2dd |
Adaptive Loss Scaling for Mixed Precision Training | 1 INTRODUCTION . Training deep neural networks ( DNNs ) is well-known to be time and energy consuming , motivating the development of new methods and hardware to make training more efficient . One way to improve training efficiency is to use numerical representations that are more hardware-friendly . This is the reason... | In this paper, the authors propose a method to train models in FP16 precision. The authors show that the key reason of training performance drop is the overflow or underflow of back propagation information. Instead of using a fixed value or dynamic value proposed by a previous work, this paper adopts a more elaborate w... | SP:4da7ae6cfcf4cab7581bab283e12354b60d3b2dd |
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization | 1 INTRODUCTION . Batch Normalization ( BN ) ( Ioffe & Szegedy , 2015 ) is one of the most popular techniques for training neural networks . It has been widely proven effective in many applications , and become the indispensable part of many state of the art deep models . Despite the success of BN , it ’ s still challen... | The paper extends recently proposed BatchRenormalization (BRN) technique which uses exponential moving average (EMA) statistics in forward and backward passes of BatchNorm (BN) instead of vanilla batch statistics. Motivation of the work is to stabilize training neural networks on small batch size setup. Authors propose... | SP:85a8e18145acb8bc9d79e188c173ac2c1d1007ed |
Towards Stabilizing Batch Statistics in Backward Propagation of Batch Normalization | 1 INTRODUCTION . Batch Normalization ( BN ) ( Ioffe & Szegedy , 2015 ) is one of the most popular techniques for training neural networks . It has been widely proven effective in many applications , and become the indispensable part of many state of the art deep models . Despite the success of BN , it ’ s still challen... | The paper proposes a new approach for batch-normalization. Standard approaches are sensitive to the batch size, because small batches will lead to unstable statistics. So when the mini-batch is small, the performance can drop significantly. The paper addresses this issue by analyzing extra statistics in the batch norma... | SP:85a8e18145acb8bc9d79e188c173ac2c1d1007ed |
Prune or quantize? Strategy for Pareto-optimally low-cost and accurate CNN | 1 INTRODUCTION . Reducing execution cost of deep learning inference is one of the most active research topics for applying superhuman recognition in embedded IoT devices and robots . A typical approach for employing memory- and computation-efficient components is separable convolution , which is a combination of depth-... | The paper proposes a new metric to evaluate both the amount of pruning and quantization. This metric is agnostic to the hardware architecture and is simply obtained by computing the Frobenius norm of some point-wise transformation of the quantized weights. They first show empirically that this Evaluation metric is corr... | SP:c7c30d05c86b1bb69f9098afe8bc2514e8eb22c0 |
Prune or quantize? Strategy for Pareto-optimally low-cost and accurate CNN | 1 INTRODUCTION . Reducing execution cost of deep learning inference is one of the most active research topics for applying superhuman recognition in embedded IoT devices and robots . A typical approach for employing memory- and computation-efficient components is separable convolution , which is a combination of depth-... | The authors propose a hardware-agnostic metric called effective signal norm (ESN) to measure the computational cost of convolutional neural networks. This metric aims to fairly measure the effects of pruning and quantization. What’s more, based on the metric, the authors demonstrate that models with fewer parameters ac... | SP:c7c30d05c86b1bb69f9098afe8bc2514e8eb22c0 |
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs | 1 INTRODUCTION . Deep Learning frameworks such as MXNet ( Chen et al. , 2015 ) , PyTorch ( Paszke et al. , 2017 ) , and TensorFlow ( TensorFlow Authors , 2016a ) represent neural network models as computation graphs . Efficiently executing such graphs requires optimizing discrete decisions about how to map the computat... | This paper proposes an ML-based method to optimize TensorFlow Graph execution. Specifically, it combines graph neural networks (GNNs) and BRKGA (a genetic algorithm) to search over the joint space of TF node-device placement and scheduling. The core claims on the advantages of this method are that (1) it co-searches pl... | SP:3044e58365db4d2ac46059803c3696add32881ba |
Reinforced Genetic Algorithm Learning for Optimizing Computation Graphs | 1 INTRODUCTION . Deep Learning frameworks such as MXNet ( Chen et al. , 2015 ) , PyTorch ( Paszke et al. , 2017 ) , and TensorFlow ( TensorFlow Authors , 2016a ) represent neural network models as computation graphs . Efficiently executing such graphs requires optimizing discrete decisions about how to map the computat... | In this paper, the authors proposed a framework to generating a task scheduling for a compiler to reduce the execution cost of neural networks. A computation graph is first fed into a GNN to produce a beta distribution, which is then fed into the BRKGA algorithm to yield the encoded solutions. The motivation is interes... | SP:3044e58365db4d2ac46059803c3696add32881ba |
The Detection of Distributional Discrepancy for Text Generation | 1 INTRODUCTION . Text generation by neural language models ( LM ) , such as LSTM ( Hochreiter & Schmidhuber , 1997 ) have given rise to much progress and are now used to dialogue generation ( Li et al. , 2017 ) , machine translation ( Wu et al. , 2016 ) and image caption ( Xu et al. , 2015 ) . However , the generated s... | This paper proposes an estimator to quantify the difference in distributions between real and generated text based on a classifier that discriminates between real vs generated text. The methodology is however not particularly well motivated and the experiments do not convince me that this proposed measure is superior ... | SP:cbb28b39e7a2f17e1b229130ba484e49b69ab695 |
The Detection of Distributional Discrepancy for Text Generation | 1 INTRODUCTION . Text generation by neural language models ( LM ) , such as LSTM ( Hochreiter & Schmidhuber , 1997 ) have given rise to much progress and are now used to dialogue generation ( Li et al. , 2017 ) , machine translation ( Wu et al. , 2016 ) and image caption ( Xu et al. , 2015 ) . However , the generated s... | This paper proposes two metrics to measure the discrepancy between generated text and real text, based on the discriminator score in GANs. Empirically, it shows that text generated by current text generation methods is still far from human-generated text, as measured by the proposed metric. The writing is a bit rough s... | SP:cbb28b39e7a2f17e1b229130ba484e49b69ab695 |
Augmenting Self-attention with Persistent Memory | 1 INTRODUCTION . Transformer networks ( Vaswani et al. , 2017 ) are sequence models that rely on the attention mechanism ( Bahdanau et al. , 2015 ) to capture long term dependencies . Since their introduction in the context of machine translation , they have been applied to many natural language processing tasks , such... | This paper proposes a simple modification to the ubiquitous Transformer model. Noticing that the feed-forward layer of a Transformer layer looks a bit like an attention over "persistent" memory vectors, the authors propose to explicitly incorporate this notion directly into the self-attention layer. This involves conca... | SP:43fde058475c6f68bfcad69c3bed3982344e5fa2 |
Augmenting Self-attention with Persistent Memory | 1 INTRODUCTION . Transformer networks ( Vaswani et al. , 2017 ) are sequence models that rely on the attention mechanism ( Bahdanau et al. , 2015 ) to capture long term dependencies . Since their introduction in the context of machine translation , they have been applied to many natural language processing tasks , such... | This paper considers an architecture change to the transformer in which they swap the feedforward subcomponent of the standard transformer with an "attention only" variant that includes persistent "memory" vectors. The model is evaluated against a suite of baselines on the tasks of character- and word-level language mo... | SP:43fde058475c6f68bfcad69c3bed3982344e5fa2 |
Gradient Surgery for Multi-Task Learning | 1 INTRODUCTION . While deep learning and deep reinforcement learning ( RL ) have shown considerable promise in enabling systems to perform complex tasks , the data requirements of current methods make it difficult to learn a breadth of capabilities particularly when all tasks are learned individually from scratch . A n... | This paper proposes as solution to manage the case where gradients are conflicting in gradient-based Multi-Task Learning (MTL), pointing to different directions. They propose a simple “gradient surgery” technique that alters the gradients by projecting a conflicting gradient on the normal vector of the other one, in or... | SP:664b4cc73449713aac9e5e0d40027993d7a17c3a |
Gradient Surgery for Multi-Task Learning | 1 INTRODUCTION . While deep learning and deep reinforcement learning ( RL ) have shown considerable promise in enabling systems to perform complex tasks , the data requirements of current methods make it difficult to learn a breadth of capabilities particularly when all tasks are learned individually from scratch . A n... | The paper presents a method to boost multi-task learning performance by editing gradient to remove conflicts between tasks. The main idea is to use cosine similarity to 1) determine if two task gradients conflict and 2) to project one conflicting gradient to the normal plane of the other, thereby removing the conflict ... | SP:664b4cc73449713aac9e5e0d40027993d7a17c3a |
Reducing Computation in Recurrent Networks by Selectively Updating State Neurons | 1 INTRODUCTION . Recurrent Neural Networks ( RNN ) are the state-of-the-art approach to many sequential learning problems including speech recognition ( Graves et al. , 2013 ) , machine translation ( Bahdanau et al. , 2015 ) , and sequence generation ( Graves , 2013 ; Xu et al. , 2015 ) . However , RNNs typically rely ... | This paper proposes selective activation RNN (SA-RNN), by using an update coordinator to determine which subset of the RNN’s hidden state dimensions should be updated at a given timestep. The proposed loss term is then a sum of the original objective (e.g. classification) and a weighted sum of the probability that each... | SP:a7eaf12be994bfd19da12b089262215f4f683e58 |
Reducing Computation in Recurrent Networks by Selectively Updating State Neurons | 1 INTRODUCTION . Recurrent Neural Networks ( RNN ) are the state-of-the-art approach to many sequential learning problems including speech recognition ( Graves et al. , 2013 ) , machine translation ( Bahdanau et al. , 2015 ) , and sequence generation ( Graves , 2013 ; Xu et al. , 2015 ) . However , RNNs typically rely ... | A main problem with RNN is to update all hidden dimensions in each time step. The authors proposed selective-activation RNN (SA-RNN), which modifies each state of RNN by adding an update coordinator which is modeled as a lightweight neural network. The coordinator, based on the incoming data, makes a discrete decision ... | SP:a7eaf12be994bfd19da12b089262215f4f683e58 |
Off-policy Bandits with Deficient Support | 1 INTRODUCTION . Many interactive systems ( e.g. , voice assistants , recommender systems , ad placement ) can be modeled as contextual bandit problems ( Langford & Zhang , 2008 ) . In particular , each user request provides a context ( e.g. , user profile , query ) for which the system selects an action ( e.g. , recom... | This work addresses the problem of off-policy evaluation in the presence of positivity violations, i.e. some actions are not observed in the logged policy. As the paper points out, positivity violations can lead to unboundedly bad estimates when employing IPS. The authors propose three methods to deal with this problem... | SP:03577eb6fb5a3b9ac15af04673f905565c57d425 |
Off-policy Bandits with Deficient Support | 1 INTRODUCTION . Many interactive systems ( e.g. , voice assistants , recommender systems , ad placement ) can be modeled as contextual bandit problems ( Langford & Zhang , 2008 ) . In particular , each user request provides a context ( e.g. , user profile , query ) for which the system selects an action ( e.g. , recom... | This paper talks about the problem of off-policy or batch learning in the contextual bandit setting without the complete support assumption. This problem setting is very realistic and encountered in most problems, especially in temporally extended settings, such as reinforcement learning. They compare three approaches ... | SP:03577eb6fb5a3b9ac15af04673f905565c57d425 |
Conditional Flow Variational Autoencoders for Structured Sequence Prediction | 1 INTRODUCTION . Anticipating future states of the environment is a key competence necessary for the success of autonomous agents . In complex real world environments , the future is highly uncertain . Therefore , structured predictions , one to many mappings of the likely future states of the world , are important . I... | The paper proposes a combination of conditional VAE wtih normalising flows priors and posterior regularisation strategies to capture the diversity of multi-modal trajectories of complex motion patterns. The paper argues that more flexible priors over the latent space can provide posteriors that more closely resemble th... | SP:ef685ddc158f7b74eec0f2139f14d7443b1192a7 |
Conditional Flow Variational Autoencoders for Structured Sequence Prediction | 1 INTRODUCTION . Anticipating future states of the environment is a key competence necessary for the success of autonomous agents . In complex real world environments , the future is highly uncertain . Therefore , structured predictions , one to many mappings of the likely future states of the world , are important . I... | The work proposes a method to improve conditional VAE with a learnable prior distribution using normalizing flow. The authors also design two regularization methods for the CF-VAE to improve training stability and avoid posterior collapse. The paper is clearly motivated and easy to follow. Experiment results on MNIST, ... | SP:ef685ddc158f7b74eec0f2139f14d7443b1192a7 |
Sequence-level Intrinsic Exploration Model for Partially Observable Domains | 1 INTRODUCTION . Under the reinforcement learning formalism , the learning behavior of an agent is driven by the reward that the agent collects from the environment ( Sutton and Barto , 1998 ) . However , many real-world problems have sparse rewards and most existing algorithms struggle with such sparsity . One inheren... | The authors tackle the exploration problem by introducing SIM (Sequence-level Intrinsic exploration Module). In most existing literature, intrinsic motivation bonuses are scored based on individual states or transitions, and not over multi-step trajectories. SIM predicts novelty bonuses based on the prediction error of... | SP:1370baa1ac8b5530a26058d36be82ecf0e97cc98 |
Sequence-level Intrinsic Exploration Model for Partially Observable Domains | 1 INTRODUCTION . Under the reinforcement learning formalism , the learning behavior of an agent is driven by the reward that the agent collects from the environment ( Sutton and Barto , 1998 ) . However , many real-world problems have sparse rewards and most existing algorithms struggle with such sparsity . One inheren... | This paper extends the prediction-error based model by Pathak et. al., 2019 by learning a forward (and inverse) dynamics model for predicting a state feature multiple steps into the future (say, K-steps) given an open loop sequence of K actions as opposed to 1 step into the future, with the caveat that instead of using... | SP:1370baa1ac8b5530a26058d36be82ecf0e97cc98 |
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators | 1 INTRODUCTION . Few-shot learning in the perspective of meta-learning aims to train models which can quickly solve novel tasks or adapt to new environments with limited number of examples . In case of few-shot classification , models are usually evaluated on a held-out dataset which does not have any common class with... | This paper aims to tackle few-shot classification with many different domains. The idea is to build a pool of embedding models, which are based on the same base network. The models are diversed by their own modulators. The high-level intuition is to let the model pool capture good domain-invariant features by the share... | SP:c6613fdb5a29233f4fb6b2eb86542ae07fc1d366 |
Domain-Agnostic Few-Shot Classification by Learning Disparate Modulators | 1 INTRODUCTION . Few-shot learning in the perspective of meta-learning aims to train models which can quickly solve novel tasks or adapt to new environments with limited number of examples . In case of few-shot classification , models are usually evaluated on a held-out dataset which does not have any common class with... | In this paper, the authors proposed to address the few-shot learning problem, especially for the cross-domain setting where a newly coming task originates from a different distribution (or in this work implemented by sampling from an unseen dataset). Basically, the authors constructed a model zoo based on source datase... | SP:c6613fdb5a29233f4fb6b2eb86542ae07fc1d366 |
Exploration in Reinforcement Learning with Deep Covering Options | 1 INTRODUCTION . Temporal abstraction , often formalized via the options framework ( Sutton et al. , 1999 ) , has the potential to greatly improve the performance of reinforcement learning ( RL ) agents by representing actions at different time scales . However , the question of which options an agent should construct ... | The paper proposes an algorithm to extend the recently proposed method of “covering options” from a tabular setting to continuous state spaces (or large discrete state spaces). The proposed algorithm approximately computes the second eigenfunction of the normalized laplacian of the state space, uses it to identify an u... | SP:461e76527806e28b022e6c4ed7872e6d8d7a3697 |
Exploration in Reinforcement Learning with Deep Covering Options | 1 INTRODUCTION . Temporal abstraction , often formalized via the options framework ( Sutton et al. , 1999 ) , has the potential to greatly improve the performance of reinforcement learning ( RL ) agents by representing actions at different time scales . However , the question of which options an agent should construct ... | The authors introduce deep covering options, an online mechanism to extend the covering options to large state spaces. They claim their method discovers options that are task agnostic. The method is evaluated in sparse reward domains and claims to gain improvement in exploration and performance as well. The authors ex... | SP:461e76527806e28b022e6c4ed7872e6d8d7a3697 |
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps | Generating visualizations and interpretations from high-dimensional data is a1 common problem in many fields . Two key approaches for tackling this problem2 are clustering and representation learning . There are very performant deep cluster-3 ing models on the one hand and interpretable representation learning techniqu... | This paper proposes VarPSOM, a method which utilizes variational autoencoders (VAEs) and clustering techniques based on self-organizing maps (SOMs) to learn clustering of image data (MNIST and Fashion MNIST in particular). An LSTM-based extension termed VarTPSOM is also evaluated on medical time series data. For the mo... | SP:faefbfe1f151c4b3e0db3ef30e3317f45dd82274 |
Variational pSOM: Deep Probabilistic Clustering with Self-Organizing Maps | Generating visualizations and interpretations from high-dimensional data is a1 common problem in many fields . Two key approaches for tackling this problem2 are clustering and representation learning . There are very performant deep cluster-3 ing models on the one hand and interpretable representation learning techniqu... | The paper proposes combining the latent space of a variational autoencoder with two losses that regularize the latent space. The first loss is the cluster hardening loss in Aljalbout et. al [https://arxiv.org/pdf/1801.07648.pdf]. This loss attempts to convert from a soft-assignments of points (in latent space) to clus... | SP:faefbfe1f151c4b3e0db3ef30e3317f45dd82274 |
Progressive Upsampling Audio Synthesis via Effective Adversarial Training | 1 INTRODUCTION . Synthesis of realistic sound is a long-studied research topic , with various real-world applications such as text-to-speech ( TTS ) ( Wang et al. , 2017 ; Ping et al. , 2018 ) , sound effect ( Raghuvanshi et al. , 2016 ) , and music generation ( Briot et al. , 2017 ; Dong et al. , 2018 ; Huang et al. ,... | The authors detail PUGAN, architectural changes to models for raw waveform generation with GANs. They do a good job of motivating the challenge of raw audio generation with GANs and of methods for progressive training. PUGAN incorporates U-Net modules in the generator ("Bandwidth expansion"), sinc convolution as bandli... | SP:a324d745f08a17a7b48caa2246a51f222107f31c |
Progressive Upsampling Audio Synthesis via Effective Adversarial Training | 1 INTRODUCTION . Synthesis of realistic sound is a long-studied research topic , with various real-world applications such as text-to-speech ( TTS ) ( Wang et al. , 2017 ; Ping et al. , 2018 ) , sound effect ( Raghuvanshi et al. , 2016 ) , and music generation ( Briot et al. , 2017 ; Dong et al. , 2018 ; Huang et al. ,... | The paper presents an approach based on generative adversarial models for the unconditional generation of audio. The authors take inspiration from WaveGAN, to which they add more sophisticated upsampling blocks (called the bandwidth extension module) instead of transposed convolutions. They also propose to add a sinc ... | SP:a324d745f08a17a7b48caa2246a51f222107f31c |
The Implicit Bias of Depth: How Incremental Learning Drives Generalization | 1 INTRODUCTION . Neural networks have led to a breakthrough in modern machine learning , allowing us to efficiently learn highly expressive models that still generalize to unseen data . The theoretical reasons for this success are still unclear , as the generalization capabilities of neural networks defy the classic st... | This paper deals with the theoretical study of the gradient dynamics in deep neural networks. More precisely, this paper define a notion of incremental learning for a particular learning dynamics and study how the depth of the network influence it. Then, the authors show two cases where it applies: matrix sensing, quad... | SP:a02b08206bf5b7026e1c35f23d2810cffa529d1f |
The Implicit Bias of Depth: How Incremental Learning Drives Generalization | 1 INTRODUCTION . Neural networks have led to a breakthrough in modern machine learning , allowing us to efficiently learn highly expressive models that still generalize to unseen data . The theoretical reasons for this success are still unclear , as the generalization capabilities of neural networks defy the classic st... | This paper studies the phenomenon of incremental learning in several deep models. It starts with analyzing the optimization dynamics of a toy model, and showing that it follows incremental learning, a notion defined clearly in the paper. In particular, it shows that depth affects the strength of incremental learning in... | SP:a02b08206bf5b7026e1c35f23d2810cffa529d1f |
Deep amortized clustering | 1 INTRODUCTION Clustering is a fundamental task in unsupervised machine learning to group similar data points into multiple clusters . Aside from its usefulness in many downstream tasks , clustering is an important tool for visualising and understanding the underlying structures of datasets , as well as a model for cat... | The paper presents an amortized clustering method, called DAC, which is a neural architecture that allows efficient data clustering using a few forward passes. The proposed method is essentially based on the idea behind set-input neural networks [1], which consists of modeling the interaction between instances within a... | SP:0706a81690b0a5309c00632dd76f8d62290c7e9e |
Deep amortized clustering | 1 INTRODUCTION Clustering is a fundamental task in unsupervised machine learning to group similar data points into multiple clusters . Aside from its usefulness in many downstream tasks , clustering is an important tool for visualising and understanding the underlying structures of datasets , as well as a model for cat... | In this paper, the authors proposed a new clustering method called deep amortized clustering (DAC). Inspired by Lee et al 2019, the authors exploited a transformer to gather the contextual information across different dataset points and then predict the cluster label for each data point. The main difference from Lee et... | SP:0706a81690b0a5309c00632dd76f8d62290c7e9e |
Visual Interpretability Alone Helps Adversarial Robustness | 1 INTRODUCTION . It has become widely known that convolutional neural networks ( CNNs ) are vulnerable to adversarial examples , namely , perturbed inputs with intention to mislead networks ’ prediction ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ; Papernot et al. , 2016a ; Carlini & Wagner , 2017 ; Chen et al. ... | In summary, this paper studies if interpretation robustness (i.e., similar examples should have similar interpretation) can help enhance the robustness of the model, especially in terms of adversarial attacks. The study direction itself is interesting and very useful for the interpretation and adversarial attack commun... | SP:e6fdf0645148e28af1a54c7f05e2594b2cf535cd |
Visual Interpretability Alone Helps Adversarial Robustness | 1 INTRODUCTION . It has become widely known that convolutional neural networks ( CNNs ) are vulnerable to adversarial examples , namely , perturbed inputs with intention to mislead networks ’ prediction ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ; Papernot et al. , 2016a ; Carlini & Wagner , 2017 ; Chen et al. ... | The present work considers adversarial attacks that also yield similar outputs for "interpretability methods", which are methods that output some vector corresponding to a given classification (usually the vector is e.g. an image or a similar object). It also shows that by regularizing nearby inputs to have similar int... | SP:e6fdf0645148e28af1a54c7f05e2594b2cf535cd |
Carpe Diem, Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) for randomly selected mini-batch samples is commonly used to train deep neural networks ( DNNs ) . However , many recent studies have pointed out that the performance of DNNs is heavily dependent on how well the mini-batch samples are selected ( Shrivastava et al. , ... | This paper proposes Recency Bias, an adaptive mini batch selection method for training deep neural networks. To select informative minibatches for training, the proposed method maintains a fixed size sliding window of past model predictions for each data sample. At a given iteration, samples which have highly inconsist... | SP:1c1b27e49b3df07bb7da0440a4ab0018d9d8440d |
Carpe Diem, Seize the Samples Uncertain "at the Moment" for Adaptive Batch Selection | 1 INTRODUCTION . Stochastic gradient descent ( SGD ) for randomly selected mini-batch samples is commonly used to train deep neural networks ( DNNs ) . However , many recent studies have pointed out that the performance of DNNs is heavily dependent on how well the mini-batch samples are selected ( Shrivastava et al. , ... | This paper explores a well motivated but very heuristic idea for selecting the next samples to train on for training deep learning models. This method relies on looking at the uncertainty of predictions of in the recent history of statements and preferring those instances that have a predictive uncertainty over the rec... | SP:1c1b27e49b3df07bb7da0440a4ab0018d9d8440d |
LabelFool: A Trick in the Label Space | 1 INTRODUCTION . Deep neural networks are powerful learning models that achieve state-of-the-art pattern recognition performance in classification tasks ( Krizhevsky et al. , 2012b ; LeCun et al. , 2010 ; He et al. , 2016 ) . Nevertheless , it is found that adding well-designed perturbations to original samples can mak... | This paper proposes a method to create adversarial perturbations whose target labels are similar to their ground truth. The target labels are selected using an existing perceptual similarity measure for images. Perturbations are generated using a DeepFool-like algorithm. Human evaluation supports that the pair of the ... | SP:9774f39a520317f2d547b5ab2690f59473d20f8e |
LabelFool: A Trick in the Label Space | 1 INTRODUCTION . Deep neural networks are powerful learning models that achieve state-of-the-art pattern recognition performance in classification tasks ( Krizhevsky et al. , 2012b ; LeCun et al. , 2010 ; He et al. , 2016 ) . Nevertheless , it is found that adding well-designed perturbations to original samples can mak... | This paper describes a technique for creating adversarial images where the added perturbations are not only imperceptible to machines, but also to human observers. The authors describe why this might be beneficial. The method works by finding labels that are not too far from the source image's ground-truth labels, and ... | SP:9774f39a520317f2d547b5ab2690f59473d20f8e |
Searching to Exploit Memorization Effect in Learning from Corrupted Labels | 1 INTRODUCTION . Learning with deep neural networks has enjoyed huge empirical success in recent years across a wide variety of tasks , from image processing to speech recognition , and from language modeling to recommender system ( Goodfellow et al. , 2016 ) . However , their success highly counts on the availability ... | This paper studies the problem of learning from corrupted labels via picking up clean instances from training dataset. The sample selection mainly based on function R(t), which controls how many instances are kept. This paper proposes a unique curvature of R(t) based on intuition and presents how R(t) can be learned vi... | SP:bd7f50f0b7150fbbf799760afdeeaeed76e93c8e |
Searching to Exploit Memorization Effect in Learning from Corrupted Labels | 1 INTRODUCTION . Learning with deep neural networks has enjoyed huge empirical success in recent years across a wide variety of tasks , from image processing to speech recognition , and from language modeling to recommender system ( Goodfellow et al. , 2016 ) . However , their success highly counts on the availability ... | This paper focuses on the topic of learning from noisy -- or as they call it "corrupted" -- labels. Specifically this focuses on an approach where data selection -- ideally of cleaner/less noisy examples -- can help the learn model overcome data noise, akin to the approaches this builds upon (i.e, the Co-Teaching and ... | SP:bd7f50f0b7150fbbf799760afdeeaeed76e93c8e |
Consistency Regularization for Generative Adversarial Networks | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have recently demonstrated impressive results on image-synthesis benchmarks ( Radford et al. , 2016 ; Zhang et al. , 2017 ; Miyato & Koyama , 2018 ; Zhang et al. , 2018 ; Brock et al. , 2018 ; Karras et al. , 2019 ) . In the original... | This paper proposes to use Consistency Regularization for training GANs, a technique known to work well in unsupervised learning. The technique consists in applying a transformation to real images and enforcing that the features of the discriminator between the transformed inputs and the original inputs are similar. Th... | SP:d3d5b63a44519237d64cc3087c26a5c910a6e17e |
Consistency Regularization for Generative Adversarial Networks | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) have recently demonstrated impressive results on image-synthesis benchmarks ( Radford et al. , 2016 ; Zhang et al. , 2017 ; Miyato & Koyama , 2018 ; Zhang et al. , 2018 ; Brock et al. , 2018 ; Karras et al. , 2019 ) . In the original... | The paper presents a new regularization technique termed consistency regularization for training GANs. The idea is the following: the authors propose to penalize the sensitivity of the last layer of the discriminator to augmented images. This idea is simple yet efficient: it is easy to implement, a regularization term ... | SP:d3d5b63a44519237d64cc3087c26a5c910a6e17e |
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring | 1 INTRODUCTION . Semi-supervised learning ( SSL ) provides a means of leveraging unlabeled data to improve a model ’ s performance when only limited labeled data is available . This can enable the use of large , powerful models when labeling data is expensive or inconvenient . Research on SSL has produced a diverse col... | This paper proposes two modifications for the MixMatch method [1] and achieves improved accuracy on a range of semi-supervised benchmarks. The first modification enforces the distribution of predicted labels to match the distribution of labeled data. The second modification is adding a learned data augmentation strateg... | SP:620dded5d2b04f0d178ebd00c303f9fb43afdb30 |
ReMixMatch: Semi-Supervised Learning with Distribution Matching and Augmentation Anchoring | 1 INTRODUCTION . Semi-supervised learning ( SSL ) provides a means of leveraging unlabeled data to improve a model ’ s performance when only limited labeled data is available . This can enable the use of large , powerful models when labeling data is expensive or inconvenient . Research on SSL has produced a diverse col... | This paper presents ReMixMatch an improved version of MixMatch. The main contributions are the distribution alignment and the augmentation anchoring. Distribution alignment rescales the predictions based on the difference between the model marginals and the ground truth running average estimation. Augmentation anchorin... | SP:620dded5d2b04f0d178ebd00c303f9fb43afdb30 |
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware | 1 INTRODUCTION . Deep neural networks have shown excellent performance on various computer vision and natural language processing tasks , such as classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; He et al. , 2016 ) , object detection ( Girshick , 2015 ; Redmon et al. , 2016 ; He et al. , 2017 ) ... | This paper proposes a linear symmetric quantizer for integer accelerators called LLSQ, which learns the quantization scaling factor using simulated gradient as update policy. Their main contribution is enabling inference on integer-only hardware by covering all parameters of all operators in convolutional networks, inc... | SP:8674a490809de44ceedfbfce7a48920a11390355 |
Linear Symmetric Quantization of Neural Networks for Low-precision Integer Hardware | 1 INTRODUCTION . Deep neural networks have shown excellent performance on various computer vision and natural language processing tasks , such as classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; He et al. , 2016 ) , object detection ( Girshick , 2015 ; Redmon et al. , 2016 ; He et al. , 2017 ) ... | This paper focuses on the quantization of ConvNets. This paper proposes a learned linear symmetric quantizer to reduce the precision of weight, bias, and activation. The proposed approach works as the following: for a pre-trained neural network, it computes the new weight and activation as a product of a quantized valu... | SP:8674a490809de44ceedfbfce7a48920a11390355 |
Contrastive Learning of Structured World Models | A structured understanding of our world in terms of objects , relations , and hierarchies is an important component of human cognition . Learning such a structured world model from raw sensory data remains a challenge . As a step towards this goal , we introduce Contrastively-trained Structured World Models ( C-SWMs ) ... | This paper aims to learn a structured latent space for images, which is made up of objects and their relations. The method works by (1) extracting object masks via a CNN, (2) turning those masks into feature vectors via an MLP, (3) estimating an action-conditioned delta for each feature via a GNN. Learning happens with... | SP:813bacb9aed3dba22dc9c379793d87506d53f362 |
Contrastive Learning of Structured World Models | A structured understanding of our world in terms of objects , relations , and hierarchies is an important component of human cognition . Learning such a structured world model from raw sensory data remains a challenge . As a step towards this goal , we introduce Contrastively-trained Structured World Models ( C-SWMs ) ... | This paper tackles the problem of learning an encoder and transition model of an environment, such that the representation learnt uses an object-centric representation which could favor compositionality and generalisation. This is trained using a contrastive max-margin loss, instead of a generative loss as previously e... | SP:813bacb9aed3dba22dc9c379793d87506d53f362 |
Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data | 1 INTRODUCTION . Ensuring a functional and near real-time system of surveillance for crop diseases and pests is of critical importance to sustaining the livelihoods of smallholder farmers in sub-Saharan Africa ( Mutembesa et al. , 2018 ) . Disease and pest surveillance systems have to be put in place to provide early w... | This paper presents a very interesting application of speech keyword spotting techniques; the aim is to listen to continuous streams of community radio in Uganda in order to spot keywords of interest related to agriculture to monitor food security concerns in rural areas. The lack of internet infrastructure results in ... | SP:836de33d05125c0f6f805a38d340f6ceae4f22d7 |
Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data | 1 INTRODUCTION . Ensuring a functional and near real-time system of surveillance for crop diseases and pests is of critical importance to sustaining the livelihoods of smallholder farmers in sub-Saharan Africa ( Mutembesa et al. , 2018 ) . Disease and pest surveillance systems have to be put in place to provide early w... | The paper describes an approach to analyze radio data with ML-based speech keyword techniques. The authors identify keywords related to agriculture and build a model that can automatically detect these keywords of interest. Their contribution is the proposed model relies on relatively simple neural networks (15-layer 1... | SP:836de33d05125c0f6f805a38d340f6ceae4f22d7 |
Hydra: Preserving Ensemble Diversity for Model Distillation | 1 INTRODUCTION . Deep neural networks have achieved impressive performance , however , they tend to make overconfident predictions and poorly quantify uncertainty ( Lakshminarayanan et al. , 2017 ) . It has been demonstrated that ensembles of models improve predictive performance and offer higher quality uncertainty qu... | This work introduces a new method for ensemble distillation. The problem of making better ensemble distillation methods seems relevant as ensembles are still one of the best ways to estimate uncertainty in practice (although see concerns below). The method itself is a simple extension of earlier “prior networks”: the o... | SP:7b714eb05f8e86b18444c8f39d89e566313988dc |
Hydra: Preserving Ensemble Diversity for Model Distillation | 1 INTRODUCTION . Deep neural networks have achieved impressive performance , however , they tend to make overconfident predictions and poorly quantify uncertainty ( Lakshminarayanan et al. , 2017 ) . It has been demonstrated that ensembles of models improve predictive performance and offer higher quality uncertainty qu... | The paper proposes to distill the predictions of an ensemble with a multi-headed network, with as many heads as members in the original ensemble. Distillation proceeds by minimizing the KL divergence between the predictions of each ensemble member with the corresponding head in the student network. Experiments illustra... | SP:7b714eb05f8e86b18444c8f39d89e566313988dc |
Option Discovery using Deep Skill Chaining | 1 INTRODUCTION . Hierarchical reinforcement learning ( Barto & Mahadevan , 2003 ) is a promising approach for solving long-horizon sequential decision making problems . Hierarchical methods lower the decision making burden on the agent through the use of problem specific action abstractions ( Konidaris , 2019 ) . While... | This paper studies the problem of learning suitable action abstractions (i.e., options or skills) that can be composed hierarchically to solve control tasks. The starting point for the paper is the (classic) observation that one skill should end where another can start. The paper then proposes a recursive algorithm for... | SP:551579ae4e3fe3b943e738e04b923d519bea84e8 |
Option Discovery using Deep Skill Chaining | 1 INTRODUCTION . Hierarchical reinforcement learning ( Barto & Mahadevan , 2003 ) is a promising approach for solving long-horizon sequential decision making problems . Hierarchical methods lower the decision making burden on the agent through the use of problem specific action abstractions ( Konidaris , 2019 ) . While... | The authors tackle the problem of skill discovery by skill chaining. In particular, the authors claim two key contributions over the state of the art in option discovery 1) learn initiation sets 2) do not need to specify the number of options and this is also learned. Skill discovery is formalized by skill chaining; wh... | SP:551579ae4e3fe3b943e738e04b923d519bea84e8 |
Neural Outlier Rejection for Self-Supervised Keypoint Learning | Identifying salient points in images is a crucial component for visual odometry , Structure-from-Motion or SLAM algorithms . Recently , several learned keypoint methods have demonstrated compelling performance on challenging benchmarks . However , generating consistent and accurate training data for interest-point dete... | The following work proposes several improvements over prior works in unsupervised/self-supervised keypoint-descriptor learning such as Christiansen et al. One improvement is the relaxation of the cell-boundaries for keypoint prediction -- specifically allowing keypoints anchored at the cell's center to be offset into n... | SP:3c415c075029fe70504aac9ad8fd3a7a8995458c |
Neural Outlier Rejection for Self-Supervised Keypoint Learning | Identifying salient points in images is a crucial component for visual odometry , Structure-from-Motion or SLAM algorithms . Recently , several learned keypoint methods have demonstrated compelling performance on challenging benchmarks . However , generating consistent and accurate training data for interest-point dete... | The paper is devoted to self-supervised learning of local features (both detectors and descriptors simultaneously). The problem is old yet not fully solved yet, because handcrafted SIFT is still winning the benchmarks. This work mostly follows and improves upon SuperPoint (DeTone et.al 2017) and the follow-up work Unsu... | SP:3c415c075029fe70504aac9ad8fd3a7a8995458c |
Attention Privileged Reinforcement Learning for Domain Transfer | 1 INTRODUCTION . Deep Reinforcement Learning ( RL ) has recently provided significant successes in a range of areas , including video games ( Mnih et al. , 2015 ) , board games ( Silver et al. , 2017 ) , simulated continuous control tasks ( Lillicrap et al. , 2015 ) , and robotic manipulation ( Haarnoja et al. , 2018 ;... | Building on top of the domain randomization principle (used to train policies robust to domain-variations) to learn policies which transfer well to new domains, the paper proposes an approach to improve and speed-up learning / training over randomized environments. The paper operates in a settings where the policy to b... | SP:ada3d3555f409cc84a060f81d2e4934459fa731f |
Attention Privileged Reinforcement Learning for Domain Transfer | 1 INTRODUCTION . Deep Reinforcement Learning ( RL ) has recently provided significant successes in a range of areas , including video games ( Mnih et al. , 2015 ) , board games ( Silver et al. , 2017 ) , simulated continuous control tasks ( Lillicrap et al. , 2015 ) , and robotic manipulation ( Haarnoja et al. , 2018 ;... | The topic addressed by the paper is domain adaptation and transfer learning in the text context of deep reinforcement learning, in particular the “sim2real” problem, where a policy is learned in simulation and should be transferred to a physical agent in a real-world scenario. The work builds on the existing “asymmetri... | SP:ada3d3555f409cc84a060f81d2e4934459fa731f |
Filling the Soap Bubbles: Efficient Black-Box Adversarial Certification with Non-Gaussian Smoothing | 1 INTRODUCTION . Deep neural networks have achieved state-of-the-art performance on many tasks such as image classification ( He et al. , 2016 ; Lu et al. , 2018 ) and language modeling ( Devlin et al. , 2019 ) . Nonetheless , modern deep learning models have been shown to be highly sensitive to small and adversarially... | This paper investigates the choice of noise distributions for smoothing an arbitrary classifier for defending against adversarial attacks. The paper focuses on the two major adversaries: \ell_2 adversaries and \ell_\infty adversaries. Theorem 1 quantifies the tradeoff between the choice of smoothing distribution which... | SP:7733bd3495e737a6664928d1d5b01b5485bcce89 |
Filling the Soap Bubbles: Efficient Black-Box Adversarial Certification with Non-Gaussian Smoothing | 1 INTRODUCTION . Deep neural networks have achieved state-of-the-art performance on many tasks such as image classification ( He et al. , 2016 ; Lu et al. , 2018 ) and language modeling ( Devlin et al. , 2019 ) . Nonetheless , modern deep learning models have been shown to be highly sensitive to small and adversarially... | This paper presents a new method for adversarial certification using non-Gaussian noise. A new framework for certification is proposed, which allows to use different distributions compared to previous work based on Gaussian noise. From this framework, a trade-off between accuracy and robustness is identified and new di... | SP:7733bd3495e737a6664928d1d5b01b5485bcce89 |
Dynamics-Aware Unsupervised Discovery of Skills | 1 INTRODUCTION . Deep reinforcement learning ( RL ) enables autonomous learning of diverse and complex tasks with rich sensory inputs , temporally extended goals , and challenging dynamics , such as discrete gameplaying domains ( Mnih et al. , 2013 ; Silver et al. , 2016 ) , and continuous control domains including loc... | This paper proposes a novel approach to learn a continuous set of skills (where a skill is associated with a latent vector and the skill policy network takes that vector as an extra input) by pure unsupervised exploration using as intrinsic reward a proxy for the mutual information between next states and the skill (g... | SP:cb8f98d674ac5fdafd3ff738a7d0027f6c4a19ad |
Dynamics-Aware Unsupervised Discovery of Skills | 1 INTRODUCTION . Deep reinforcement learning ( RL ) enables autonomous learning of diverse and complex tasks with rich sensory inputs , temporally extended goals , and challenging dynamics , such as discrete gameplaying domains ( Mnih et al. , 2013 ; Silver et al. , 2016 ) , and continuous control domains including loc... | This paper introduces an unsupervised learning algorithm Dynamics-Aware Discovery of Skills (DADS) for learning low-level “skills” that can be leveraged for model-predictive control. The skills are learned by maximizing the mutual information between the next state s’ and the current skill z conditioned on the current ... | SP:cb8f98d674ac5fdafd3ff738a7d0027f6c4a19ad |
A GOODNESS OF FIT MEASURE FOR GENERATIVE NETWORKS | 1 INTRODUCTION AND RELATED WORK . Generative adversarial networks ( Goodfellow et al. , 2014 ) are a specific type of generative model that has shown impressive performance lately . The main idea is that there are two networks that compete against each other : a generator network that generates images and a discriminat... | This paper defines a goodness of fit measure F for generative networks, that reflects how well a model can generate the training data. F allows to detect mode collapse: as long as it is strictly positive, mode collapse is observed as parts of the training data have not been memorized. It aims at providing an alternativ... | SP:34ae68bada17973bcb8f7aab9a70349b31dde1ad |
A GOODNESS OF FIT MEASURE FOR GENERATIVE NETWORKS | 1 INTRODUCTION AND RELATED WORK . Generative adversarial networks ( Goodfellow et al. , 2014 ) are a specific type of generative model that has shown impressive performance lately . The main idea is that there are two networks that compete against each other : a generator network that generates images and a discriminat... | This work proposed a new goodness of fit measure for generative network evaluations, which is based on how well the network can generate the training data. The measure is zero if the network could perfectly recover the training data, and would represent how far it is from generating the training set in the average mann... | SP:34ae68bada17973bcb8f7aab9a70349b31dde1ad |
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