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On the Neural Tangent Kernel of Equilibrium Models | 1 INTRODUCTION Recent works empirically observe that as the depth of a weight-tied input-injected network increases , its output tends to converge to a fixed point . Motivated by this phenomenon , DEQ models were proposed to effectively represent an “ infinite depth ” network by root-finding . A natural question to ask... | This paper studies the neural tangent kernel (NTK) of fully-connected neural networks with input injection (defined in the first set of display in Section 3.1), and the infinite depth limit of the NTK. The calculations are further carried out for the convolution neural networks with input injection (defined at the begi... | SP:3bd9aeb125e33dd92db4510d3c2c26e782576ab8 |
On the Neural Tangent Kernel of Equilibrium Models | 1 INTRODUCTION Recent works empirically observe that as the depth of a weight-tied input-injected network increases , its output tends to converge to a fixed point . Motivated by this phenomenon , DEQ models were proposed to effectively represent an “ infinite depth ” network by root-finding . A natural question to ask... | The paper shows the deep equilibrium model has non-degenerate neural tangent kernel in the infinite depth setting. The neural tangent kernel can be computed by a similar root-finding problem as that in the deep equilibrium problem itself. Some experiments have been performed to compare the performance of deep equilibri... | SP:3bd9aeb125e33dd92db4510d3c2c26e782576ab8 |
Equivariant Normalizing Flows for Point Processes and Sets | A point process describes how random sets of exchangeable points are generated . The points usually influence the positions of each other via attractive and repulsive forces . To model this behavior , it is enough to transform the samples from the uniform process with a sufficiently complex equivariant function . Howev... | This paper provides a novel method of learning density models for sets of points by modelling the sets as samples from a point process, approximated with normalizing flows. A point process gives a probability to a set of points, not assuming that the points are independent of one another. The authors describe the CONF... | SP:e7de4f8c7d34e0371297b60ef316a6bdd343b2c3 |
Equivariant Normalizing Flows for Point Processes and Sets | A point process describes how random sets of exchangeable points are generated . The points usually influence the positions of each other via attractive and repulsive forces . To model this behavior , it is enough to transform the samples from the uniform process with a sufficiently complex equivariant function . Howev... | This paper proposes a method based on continuous normalizing flows that can model random sets of exchangeable points. The advantages of the method are as follows: (1) it can handle a complex density function of sets and (2) it is designed so that model learning can be performed based on tractable likelihood while consi... | SP:e7de4f8c7d34e0371297b60ef316a6bdd343b2c3 |
Equivariant Normalizing Flows for Point Processes and Sets | A point process describes how random sets of exchangeable points are generated . The points usually influence the positions of each other via attractive and repulsive forces . To model this behavior , it is enough to transform the samples from the uniform process with a sufficiently complex equivariant function . Howev... | This paper proposes a method for modeling exchangeable sets of data, or point processes. Specifically, the paper is interested in applying normalizing flow methods to these point processes. The paper proposes a method using continuous normalizing flows, and compares the performance of their proposed method, Confet, to ... | SP:e7de4f8c7d34e0371297b60ef316a6bdd343b2c3 |
Integrating Categorical Semantics into Unsupervised Domain Translation | 1 INTRODUCTION . Domain translation has sparked a lot of interest in the computer vision community following the work of Isola et al . ( 2016 ) on image-to-image translation . This was done by learning a conditional GAN ( Mirza & Osindero , 2014 ) , in a supervised manner , using paired samples from the source and targ... | This paper presents unsupervised domain translation (UDT), considering two scenarios: Semantic Preserving Unsupervised Domain Translation (SPUDT) and is Style-Heterogeneous Domain Translation (SHDT). This study uses MNIST and SVHN datasets for demonstrating SPUDT and Sketches and Reals samples from the DomainNet datase... | SP:17a9ff5184de391ff498d72f69ec884c2d5927a8 |
Integrating Categorical Semantics into Unsupervised Domain Translation | 1 INTRODUCTION . Domain translation has sparked a lot of interest in the computer vision community following the work of Isola et al . ( 2016 ) on image-to-image translation . This was done by learning a conditional GAN ( Mirza & Osindero , 2014 ) , in a supervised manner , using paired samples from the source and targ... | The paper addresses the domain translation problem and proposes a novel approach to translate images between domains in an unsupervised manner, by integrating unsupervised learning of domain-invariant semantic features between the two domains. The paper is well-written with a clear standing point and motivation, along ... | SP:17a9ff5184de391ff498d72f69ec884c2d5927a8 |
Integrating Categorical Semantics into Unsupervised Domain Translation | 1 INTRODUCTION . Domain translation has sparked a lot of interest in the computer vision community following the work of Isola et al . ( 2016 ) on image-to-image translation . This was done by learning a conditional GAN ( Mirza & Osindero , 2014 ) , in a supervised manner , using paired samples from the source and targ... | The authors use Domain Invariant Categorical Semantics to improve unsupervised domain translation (UDT). They learn these semantics in an unsupervised manner. They show how this can improve results on Semantic Preserving Unsupervised Domain Translation and Style Heterogeneous Domain Translation by doing experiments on ... | SP:17a9ff5184de391ff498d72f69ec884c2d5927a8 |
Multi-Source Unsupervised Hyperparameter Optimization | 1 INTRODUCTION . Hyperparameter optimization ( HPO ) has been a pivotal part of machine learning ( ML ) and contributed to achieving a good performance in a wide range of tasks ( Feurer & Hutter , 2019 ) . It is widely acknowledged that the performance of deep neural networks depends greatly on the configuration of the... | In the situation where a given objective is computed with samples from a distribution, e.g. loss on validation data in hyperparameter optimization, this paper proposes a method to construct a surrogate objective using objectives computed on sets of samples each of which is from a different distribution. Basic idea is t... | SP:76c71593430e9f257e9c2feddd5539c83f9b507f |
Multi-Source Unsupervised Hyperparameter Optimization | 1 INTRODUCTION . Hyperparameter optimization ( HPO ) has been a pivotal part of machine learning ( ML ) and contributed to achieving a good performance in a wide range of tasks ( Feurer & Hutter , 2019 ) . It is widely acknowledged that the performance of deep neural networks depends greatly on the configuration of the... | The paper introduces multi-source unsupervised hyperparameter optimization (MSU-HPO), a novel BO framework where a range of related tasks are available but labels cannot be accessed for the target task. As ground truth on the target task is unavailable, the work introduces two estimators to approximate the target task ... | SP:76c71593430e9f257e9c2feddd5539c83f9b507f |
Multi-Source Unsupervised Hyperparameter Optimization | 1 INTRODUCTION . Hyperparameter optimization ( HPO ) has been a pivotal part of machine learning ( ML ) and contributed to achieving a good performance in a wide range of tasks ( Feurer & Hutter , 2019 ) . It is widely acknowledged that the performance of deep neural networks depends greatly on the configuration of the... | The authors describe a method for training and tuning a machine learning model for a prediction task where no labels are available, and where thus no model can be fit in the standard supervised manner. Instead labels are estimated based on related tasks that do have labels. After this a predictor can be trained on thos... | SP:76c71593430e9f257e9c2feddd5539c83f9b507f |
Knowledge Distillation as Semiparametric Inference | A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model . Surprisingly , this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data . To explain... | This paper gives generalization guarantees for vanilla knowledge distillation, where it identifies a large variance issue due to the teacher’s complexity in this guarantee, and then proposes alternative approaches to eliminate this issue. The framework it uses is inspired by semi-parametric methods. The main resulting ... | SP:d0cc938ab1814b400f9b056c172b056d6358ec21 |
Knowledge Distillation as Semiparametric Inference | A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model . Surprisingly , this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data . To explain... | The knowledge distillation (KD) approach is a two-step procedure: first train the teacher model on the labeled data and then train the student model using the predicted class probabilities from the teacher model. A key theoretical question about KD is whether and how much this two-step approach can improve on the one-s... | SP:d0cc938ab1814b400f9b056c172b056d6358ec21 |
Knowledge Distillation as Semiparametric Inference | A popular approach to model compression is to train an inexpensive student model to mimic the class probabilities of a highly accurate but cumbersome teacher model . Surprisingly , this two-step knowledge distillation process often leads to higher accuracy than training the student directly on labeled data . To explain... | This paper formulates knowledge distillation as a semi-parametric inference problem. Then, the paper adapts techniques from semi-parametric inference to analyze the error of a student model and improve the performance of knowledge distillation. The presentation is overall of good quality and it is relatively easy for m... | SP:d0cc938ab1814b400f9b056c172b056d6358ec21 |
An Unsupervised Deep Learning Approach for Real-World Image Denoising | 1 INTRODUCTION . Noise always exists during the process of image acquisition and its removing is important for image recovery and vision tasks , e.g. , segmentation and recognition . Specifically , the noisy image y is modeled as y = x+ n , where x denotes the clean image , n denotes the corrupted noise and image denoi... | The paper proposes an unsupervised approach for denoising for which works on a single noisy image. The central idea is to use a neural network to map the noisy image to a latent image space where the noisy distribution follows Gaussian (like Variance Stabliziation Transform) and then use a off-the-shelf Gaussian denois... | SP:68981cc1326c08d1ccad134c5b86a4631f31591e |
An Unsupervised Deep Learning Approach for Real-World Image Denoising | 1 INTRODUCTION . Noise always exists during the process of image acquisition and its removing is important for image recovery and vision tasks , e.g. , segmentation and recognition . Specifically , the noisy image y is modeled as y = x+ n , where x denotes the clean image , n denotes the corrupted noise and image denoi... | This paper proposes a method to perform denoising using a single image. A UNet encoder is used to map the image to a space where a denoiser such as BM3D is applied. ADMM is used to update the weights by minimizing a cost function where one of the terms fits the noise image (using an additional UNet decoder trained join... | SP:68981cc1326c08d1ccad134c5b86a4631f31591e |
An Unsupervised Deep Learning Approach for Real-World Image Denoising | 1 INTRODUCTION . Noise always exists during the process of image acquisition and its removing is important for image recovery and vision tasks , e.g. , segmentation and recognition . Specifically , the noisy image y is modeled as y = x+ n , where x denotes the clean image , n denotes the corrupted noise and image denoi... | The key idea of this paper is to transform a real-world noisy image into a latent image space using an encoder neural network(NN), where the noise is hopefully white Gaussian in the latent image space so that existing Gaussian denoisers can be used in the optimization steps. The proposed methods are reported to have im... | SP:68981cc1326c08d1ccad134c5b86a4631f31591e |
Meta-Learning with Neural Tangent Kernels | 1 INTRODUCTION . Meta-learning ( Schmidhuber , 1987 ) has made tremendous progresses in the last few years . It aims to learn abstract knowledge from many related tasks so that fast adaption to new and unseen tasks becomes possible . For example , in few-shot learning , meta-learning corresponds to learning a meta-mode... | In this paper, the authors view MAML from the lens of Reproducing Hilbert Kernel Hilbert Spaces (RKHS) by applying tools from the theory of Neural Tangent Kernels (NTKs). Based on these insights, they develop two meta-learning algorithms that avoid gradient-based inner-loop adaptation. Their algorithms are theoreticall... | SP:32ef828a66d08474bd68e7fb4f664b38d9f65715 |
Meta-Learning with Neural Tangent Kernels | 1 INTRODUCTION . Meta-learning ( Schmidhuber , 1987 ) has made tremendous progresses in the last few years . It aims to learn abstract knowledge from many related tasks so that fast adaption to new and unseen tasks becomes possible . For example , in few-shot learning , meta-learning corresponds to learning a meta-mode... | The authors propose two meta-learning algorithms in the reproducing kernel Hilbert space (RKHS) induced by the recently proposed Neural Tangent Kernels (NTK). The authors show how their algorithms obviate an explicit inner loop or task-adaptation step in the meta-learning training phase. In first algorithm, no explicit... | SP:32ef828a66d08474bd68e7fb4f664b38d9f65715 |
Meta-Learning with Neural Tangent Kernels | 1 INTRODUCTION . Meta-learning ( Schmidhuber , 1987 ) has made tremendous progresses in the last few years . It aims to learn abstract knowledge from many related tasks so that fast adaption to new and unseen tasks becomes possible . For example , in few-shot learning , meta-learning corresponds to learning a meta-mode... | This paper mainly deals with the computational issues of Model Agnostic Meta-Learning (MAML). Specifically, it proposes two meta-learning algorithms where the hypothesis class (i.e. the mapping function set) is defined in RKHS induced by NTK. Extensive experimental studies on many tasks (i.e. regression, few-shot image... | SP:32ef828a66d08474bd68e7fb4f664b38d9f65715 |
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks | 1 INTRODUCTION . With the rapid progress of deep neural network ( DNN ) techniques , innovative applications of deep learning in various domains , such as computer vision and natural language processing ( NLP ) , are getting more mature and powerful ( Huang et al. , 2017 ; Vaswani et al. , 2017 ; Szegedy et al. , 2015 ... | The paper introduces a new floating point format FFP8 that can adaptively choose the exponent bias as well as the existence of the sign bit. FFP8 is more flexible than other 8-bit floating point formats with fixed exponent biases. The authors show how FF8 can be used to cover the dynamic range of NN weights and activat... | SP:ff8234ea22341194724acd6110af9041d7b71a32 |
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks | 1 INTRODUCTION . With the rapid progress of deep neural network ( DNN ) techniques , innovative applications of deep learning in various domains , such as computer vision and natural language processing ( NLP ) , are getting more mature and powerful ( Huang et al. , 2017 ; Vaswani et al. , 2017 ; Szegedy et al. , 2015 ... | This paper explores 8-bit floating point formats for the inference of deep neural networks. The quantization is applied on the weight and activation tensors, but computation engine remains in FP32. To cover the different ranges of weight and activation tensors, the authors propose to use exponent bias. The authors did... | SP:ff8234ea22341194724acd6110af9041d7b71a32 |
All-You-Can-Fit 8-Bit Flexible Floating-Point Format for Accurate and Memory-Efficient Inference of Deep Neural Networks | 1 INTRODUCTION . With the rapid progress of deep neural network ( DNN ) techniques , innovative applications of deep learning in various domains , such as computer vision and natural language processing ( NLP ) , are getting more mature and powerful ( Huang et al. , 2017 ; Vaswani et al. , 2017 ; Szegedy et al. , 2015 ... | The paper proposes a new flexible floating point format (FFP8) on 8 bits, to help alleviate the high memory demand of deep networks inference, while preserving high accuracy. There is a large body of literature on reducing the data format, typically from 32 bits to 16, 8 and even below. There is previous work on using ... | SP:ff8234ea22341194724acd6110af9041d7b71a32 |
The Surprising Power of Graph Neural Networks with Random Node Initialization | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Scarselli et al. , 2009 ; Gori et al. , 2005 ) are neural architectures designed for learning functions over graph-structured data , and naturally encode desirable properties such as permutation invariance ( resp. , equivariance ) relative to graph nodes , and node-leve... | The paper studies the how random initialization of node states can improve the expressivity of message passing graph neural networks. Theoretically the paper shows that RNI makes MPNNs universal approximators for invariant functions over graphs. To supplement this claim, the authors evaluate GNNs with RNI and higher-or... | SP:7befb2bd43cebe3235a5040100023f589932ad8d |
The Surprising Power of Graph Neural Networks with Random Node Initialization | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Scarselli et al. , 2009 ; Gori et al. , 2005 ) are neural architectures designed for learning functions over graph-structured data , and naturally encode desirable properties such as permutation invariance ( resp. , equivariance ) relative to graph nodes , and node-leve... | This paper studies the power of message passing neural networks (MPNNs) with random node initialization (RNI). Although the power of standard MPNNs is limited to 1-WL, the main result of the paper is to prove that RNI makes MPNNs universal. The paper also introduces two graph classification datasets where each graph is... | SP:7befb2bd43cebe3235a5040100023f589932ad8d |
The Surprising Power of Graph Neural Networks with Random Node Initialization | 1 INTRODUCTION . Graph neural networks ( GNNs ) ( Scarselli et al. , 2009 ; Gori et al. , 2005 ) are neural architectures designed for learning functions over graph-structured data , and naturally encode desirable properties such as permutation invariance ( resp. , equivariance ) relative to graph nodes , and node-leve... | The paper study the effects of adding random features (RF) to graph neural networks (GNN). First, it is shown that, quite surprisingly, adding random features makes GNN universal approximators of invariant functions. Next, a novel dataset is defined that is aimed at evaluating the performance of models that have high e... | SP:7befb2bd43cebe3235a5040100023f589932ad8d |
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic | 1 INTRODUCTION . Many machine learning problems boil down to finding a minimizer ✓⇤ 2 Rd of a risk function taking the form F ( ✓ ) = E [ f ( ✓ , Z ) ] , ( 1 ) where f denotes a loss function , ✓ is the model parameter , and the random data point Z = ( X , y ) contains a feature vector X and its label y . In the case o... | This paper proposes a sign-based test to determine if a stochastic process is in its stationary state or not. Unlike Pflug test, this test uses two independent trajectories to build its test. It divides each trajectory into w parts and averages the gradients insides each part. Then measure the similarity of each averag... | SP:3bec5e8e3ab7e3d9db7b34ba9268be3844f7acc2 |
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic | 1 INTRODUCTION . Many machine learning problems boil down to finding a minimizer ✓⇤ 2 Rd of a risk function taking the form F ( ✓ ) = E [ f ( ✓ , Z ) ] , ( 1 ) where f denotes a loss function , ✓ is the model parameter , and the random data point Z = ( X , y ) contains a feature vector X and its label y . In the case o... | The paper focuses on estimating when stochastic gradient dynamics have reached a stationary phase by considering the inner product of pairwise stochastic trajectories referred to as threads. The chosen approach avoids strongly correlated estimates which leads to better mixing and more reliable identification of a stati... | SP:3bec5e8e3ab7e3d9db7b34ba9268be3844f7acc2 |
Robust Learning Rate Selection for Stochastic Optimization via Splitting Diagnostic | 1 INTRODUCTION . Many machine learning problems boil down to finding a minimizer ✓⇤ 2 Rd of a risk function taking the form F ( ✓ ) = E [ f ( ✓ , Z ) ] , ( 1 ) where f denotes a loss function , ✓ is the model parameter , and the random data point Z = ( X , y ) contains a feature vector X and its label y . In the case o... | The paper introduces SplitSGD method that detects the stationary phase in the stochastic optimization process and shrinks the learning rate. The SplitSGD is based on the observation that before reaching the stationary phase, two random batches of data will likely to have the gradient aligned as the noise between differ... | SP:3bec5e8e3ab7e3d9db7b34ba9268be3844f7acc2 |
Time Series Counterfactual Inference with Hidden Confounders | 1 INTRODUCTION . Decision makers want to know how to produce desired outcomes and act accordingly , which requires causal understanding of cause and effect . In this paper , we consider applications in healthcare , where time series data on past features and outcomes are now widely available . Causality in time series ... | This paper proposed to solve an interesting problem: how do we perform counterfactual inference for time series data? The paper follows a study of the problem in the static setting: in the first step, the paper fit an augmented time series $u_t$ as additional confounders, and then perform inference based on the augment... | SP:338bac3f8bfe06a38ef55fc3b56d326b848e133e |
Time Series Counterfactual Inference with Hidden Confounders | 1 INTRODUCTION . Decision makers want to know how to produce desired outcomes and act accordingly , which requires causal understanding of cause and effect . In this paper , we consider applications in healthcare , where time series data on past features and outcomes are now widely available . Causality in time series ... | In this manuscript, the authors propose a novel way of performing counterfactual inference in time-series in the presence of hidden confounders. For this, they employ neural ODEs as a latent time-series model, which they augment with additional latent variables. They test their approach on synthetic and real-world data... | SP:338bac3f8bfe06a38ef55fc3b56d326b848e133e |
Time Series Counterfactual Inference with Hidden Confounders | 1 INTRODUCTION . Decision makers want to know how to produce desired outcomes and act accordingly , which requires causal understanding of cause and effect . In this paper , we consider applications in healthcare , where time series data on past features and outcomes are now widely available . Causality in time series ... | The authors propose a new method, called augmented counterfactual ordinary differential equations (ACODs), to do counterfactual inference on time series data in healthcare. This is done by modelling interventions in continuous time with differential equations augmented by auxiliary confounding variables to reduce bias.... | SP:338bac3f8bfe06a38ef55fc3b56d326b848e133e |
Measuring Progress in Deep Reinforcement Learning Sample Efficiency | 1 INTRODUCTION . Recent successes of deep reinforcement learning ( DRL ) in Go ( Silver et al. , 2016 ; 2017 ; 2018 ) and complex real-time strategy games ( Berner et al. , 2019 ; Vinyals et al. , 2019 ) indicate the vast potential for automating complex economically relevant tasks like assembling goods in non-standard... | This paper conducts a meta-analysis of the trend in sample efficiency in deep RL. The authors argue that this is an informative measure of the progress in the field, in addition to the usual metrics of reward for given tasks, as it is an important consideration when applying deep RL to real world problems. They measure... | SP:4715cccdb9f75396851b845cde5521d43a4029fc |
Measuring Progress in Deep Reinforcement Learning Sample Efficiency | 1 INTRODUCTION . Recent successes of deep reinforcement learning ( DRL ) in Go ( Silver et al. , 2016 ; 2017 ; 2018 ) and complex real-time strategy games ( Berner et al. , 2019 ; Vinyals et al. , 2019 ) indicate the vast potential for automating complex economically relevant tasks like assembling goods in non-standard... | The paper is trying to make extensive and systematic investigation in deep RL papers to measure the recent progress in the broad literatures. The authors look into published state-of-the-art results in Atari, state-based continuous control and pixel-based continuous control. Inferring from papers' published training cu... | SP:4715cccdb9f75396851b845cde5521d43a4029fc |
Measuring Progress in Deep Reinforcement Learning Sample Efficiency | 1 INTRODUCTION . Recent successes of deep reinforcement learning ( DRL ) in Go ( Silver et al. , 2016 ; 2017 ; 2018 ) and complex real-time strategy games ( Berner et al. , 2019 ; Vinyals et al. , 2019 ) indicate the vast potential for automating complex economically relevant tasks like assembling goods in non-standard... | The paper proposes to retrospectively benchmark the sample-efficiency on widely used simulated deep RL benchmarks such as Atari and DMControl across the years. The paper shows some interesting trends with respect to how both the algorithmic improvements as well as the use of increased number of frames have driven the p... | SP:4715cccdb9f75396851b845cde5521d43a4029fc |
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule | 1 INTRODUCTION . One of the fascinating properties of deep neural networks ( DNNs ) is their ability to generalize well , i.e. , deliver high accuracy on the unseen test dataset . It is well-known that the learning rate ( LR ) schedules play an important role in the generalization performance ( Keskar et al. , 2016 ; W... | This paper did an empirical study on the learning rate (LR) schedule for deep neural networks (DNNs) training. The authors argue that the density of wide minima is lower than sharp minima and then show that this makes keeping high LR necessary. Finally, they propose a new LR schedule that maintains high LR enough long.... | SP:6c53796041fb5bfa1516d9172ce23628c39f3ce3 |
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule | 1 INTRODUCTION . One of the fascinating properties of deep neural networks ( DNNs ) is their ability to generalize well , i.e. , deliver high accuracy on the unseen test dataset . It is well-known that the learning rate ( LR ) schedules play an important role in the generalization performance ( Keskar et al. , 2016 ; W... | Learning rate schedule plays an important role in DL, which has a large influence over the final performance. Though there have been lots of schedules, achieving SOTA performance still requires careful hand-tuned schedule that may be case by case. Compared with previous learning rate schedules, authors first conjecture... | SP:6c53796041fb5bfa1516d9172ce23628c39f3ce3 |
Wide-minima Density Hypothesis and the Explore-Exploit Learning Rate Schedule | 1 INTRODUCTION . One of the fascinating properties of deep neural networks ( DNNs ) is their ability to generalize well , i.e. , deliver high accuracy on the unseen test dataset . It is well-known that the learning rate ( LR ) schedules play an important role in the generalization performance ( Keskar et al. , 2016 ; W... | This work studies the problem of how to define learning rate schedules when training deep models so that the models better generalize. To this end, the paper proposes and evaluates a learning rate schedule that consists of two stages (knee schedule). A first stage of exploration adoptes a high learning rate. This ini... | SP:6c53796041fb5bfa1516d9172ce23628c39f3ce3 |
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | 1 INTRODUCTION . The goal of task-oriented dialog systems is to assist the user in completing a certain task by performing an action or retrieving relevant information ( Tur & Mori , 2011 ) . They are often built on top of a structured ontology grounded in a knowledge base , a database , or a set of API calls . This in... | This paper proposes a pre-training approach to improve the performance in conversational semantic parsing. The idea is to use the training data to learn how to generate contextual representations by combining the now commonly used masked language modelling pretraining objective (MLM) with two additional objectives, na... | SP:bd9472600b9e7e4b407b0b2572179bc8cab7f272 |
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | 1 INTRODUCTION . The goal of task-oriented dialog systems is to assist the user in completing a certain task by performing an action or retrieving relevant information ( Tur & Mori , 2011 ) . They are often built on top of a structured ontology grounded in a knowledge base , a database , or a set of API calls . This in... | In this paper, the authors proposed a pre-training strategy for Conversational Semantic Parsing (CSP) tasks. The pre-training is run on top of any existing LM (i.e., in this work RoBERTA has been used), and uses three additional loss functions to inject the CSP inductive bias into the LM: Column Contextual Semantics (C... | SP:bd9472600b9e7e4b407b0b2572179bc8cab7f272 |
SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing | 1 INTRODUCTION . The goal of task-oriented dialog systems is to assist the user in completing a certain task by performing an action or retrieving relevant information ( Tur & Mori , 2011 ) . They are often built on top of a structured ontology grounded in a knowledge base , a database , or a set of API calls . This in... | The paper proposes to pretrain contextual semantic parsing models on synthesized data with two new training objectives: Column Contextual Semantics (CCS) and Turn Contextual Switch (TCS). The CCS objective predicts correct database operations based on corresponding columns in tables. The TCS aims to predict the labels ... | SP:bd9472600b9e7e4b407b0b2572179bc8cab7f272 |
Adaptive norms for deep learning with regularized Newton methods | 1 INTRODUCTION . We consider finite-sum optimization problems of the form min w∈Rd [ L ( w ) : = n∑ i=1 ` ( f ( w , xi , yi ) ) ] , ( 1 ) which typically arise in neural network training , e.g . for empirical risk minimization over a set of data points ( xi , yi ) ∈ Rin × Rout , i = 1 , . . . , n. Here , ` : Rout × Rou... | This paper analyzes adaptive methods like Adam and AMSProp, and shows that they can be re-interpreted as first order trust region methods with an ellipsoidal trust region (Lemma 1). The authors then propose a second order trust region method with similar ellipsoidal trust regions induced by the RMSProp matrices (Eq 7)... | SP:07bcf06ee98f73a12e1b49851490648bc71699aa |
Adaptive norms for deep learning with regularized Newton methods | 1 INTRODUCTION . We consider finite-sum optimization problems of the form min w∈Rd [ L ( w ) : = n∑ i=1 ` ( f ( w , xi , yi ) ) ] , ( 1 ) which typically arise in neural network training , e.g . for empirical risk minimization over a set of data points ( xi , yi ) ∈ Rin × Rout , i = 1 , . . . , n. Here , ` : Rout × Rou... | The paper proposes novel stochastic ellipsoidal trust-region methods inspired by adaptive gradient methods and studies the application of them with adaptive diagonal preconditioners. Theoretical convergence analysis is provided for TR with RMSProp ellipsoid, and numerical results demonstrates the superiority of ellips... | SP:07bcf06ee98f73a12e1b49851490648bc71699aa |
Adaptive norms for deep learning with regularized Newton methods | 1 INTRODUCTION . We consider finite-sum optimization problems of the form min w∈Rd [ L ( w ) : = n∑ i=1 ` ( f ( w , xi , yi ) ) ] , ( 1 ) which typically arise in neural network training , e.g . for empirical risk minimization over a set of data points ( xi , yi ) ∈ Rin × Rout , i = 1 , . . . , n. Here , ` : Rout × Rou... | Authors propose a new perspective on adaptive gradient methods. Main contribution is a trust region based algorithm they call "Stochastic Ellipsoidal Trust Region Method" thats flexible to include both full, and diagonal matrix as the preconditioning matrix. Authors also mention that the preconditioners are generally ... | SP:07bcf06ee98f73a12e1b49851490648bc71699aa |
Efficient Robust Training via Backward Smoothing | 1 INTRODUCTION . Deep neural networks are well known to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ) , i.e. , a small perturbation on the original input can lead to misclassification or erroneous prediction . Many defense methods have been developed to mitigate the disturbance of adversarial examples... | The paper proposes Backward Smoothing to close the gap in terms of robustness between standard multi-step and fast (one or two steps) adversarial training (AT). In particular, at high level, given a network $f$, a point $x$ and its logits $f(x)$ it suggests to first sample a random perturbation $\psi$ in the logits spa... | SP:396b87ea41744461ca381150153efdf374c2222f |
Efficient Robust Training via Backward Smoothing | 1 INTRODUCTION . Deep neural networks are well known to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ) , i.e. , a small perturbation on the original input can lead to misclassification or erroneous prediction . Many defense methods have been developed to mitigate the disturbance of adversarial examples... | The paper proposes a new algorithm for performing fast adversarial training. The proposed algorithm consists in solving the inner maximization problem in the following way: first, one takes a step of projected gradient descent (PGD) wrt an auxiliary loss (motivated by the idea of backward smoothing), and then one takes... | SP:396b87ea41744461ca381150153efdf374c2222f |
Efficient Robust Training via Backward Smoothing | 1 INTRODUCTION . Deep neural networks are well known to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ) , i.e. , a small perturbation on the original input can lead to misclassification or erroneous prediction . Many defense methods have been developed to mitigate the disturbance of adversarial examples... | This paper seeks to reduce the training time of TRADES adversarial training. It tries to understand fast adversarial training methods (single-step adversary methods) by viewing the random initialization of the adversarial perturbation in the PGD steps as randomized smoothing, making the inner maximization (the adversar... | SP:396b87ea41744461ca381150153efdf374c2222f |
IsarStep: a Benchmark for High-level Mathematical Reasoning | A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models . In this paper , we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models . We build a non-synthetic dataset from the largest ... | This paper presents a non-synthetic dataset generated from the Isabelle AFP, the largest mechanised proof repository for the task of filling in a missing intermediate proposition given surrounding proofs. Together with the dataset the paper presents a hierarchical transformer model (HAT). Top-10 accuracy, which is the ... | SP:cd7e181c1e4bcb07e01ddd3a1e608538fe38fa6e |
IsarStep: a Benchmark for High-level Mathematical Reasoning | A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models . In this paper , we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models . We build a non-synthetic dataset from the largest ... | The authors propose a new benchmark task to evaluate the high-level reasoning capabilities of machine learning models (specifically sequence-to-sequence models) in the context of proof assistants. The task consists of predicting the intermediate proposition from its surrounding ones, namely its previous and its subsequ... | SP:cd7e181c1e4bcb07e01ddd3a1e608538fe38fa6e |
IsarStep: a Benchmark for High-level Mathematical Reasoning | A well-defined benchmark is essential for measuring and accelerating research progress of machine learning models . In this paper , we present a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models . We build a non-synthetic dataset from the largest ... | This paper proposes a benchmark for high-level mathematical reasoning and study the reasoning capabilities of neural sequence-to-sequence models. This is a non-synthetic dataset from the largest repository of proofs written by human experts in a theorem prover, which has a broad coverage of undergraduate and research-l... | SP:cd7e181c1e4bcb07e01ddd3a1e608538fe38fa6e |
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) have been used extensively for segmentation problems in computer vision He et al . ( 2017 ) ; He et al . ( 2016 ) ; Chen et al . ( 2017 ) ; Xie & Tu ( 2015 ) . CNNs provide a framework for learning descriptors that are able to discriminate different textured or se... | The paper suggests replacing convolutional layers with ST-DNNs. ST-DNNs, in contrast to conv. layers, can natively support non-rectangular neighborhoods (regions). Similarly to conv. layers, ST-DNNs can be stacked to increase expressivity. ST-DNNs themselves correspond to solutions to the Poisson PDE. The paper describ... | SP:243a41483b648f364496a705cba45968c5c02968 |
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) have been used extensively for segmentation problems in computer vision He et al . ( 2017 ) ; He et al . ( 2016 ) ; Chen et al . ( 2017 ) ; Xie & Tu ( 2015 ) . CNNs provide a framework for learning descriptors that are able to discriminate different textured or se... | The paper proposed a new "shape-tailored" convolutional layer for improving the accuracy of semantic segmentation. The shape-tailored layer is inspired by the Poisson partial differential equation which aggregate features from neighboring pixels through the linear combinations of partial derivatives of the output of PD... | SP:243a41483b648f364496a705cba45968c5c02968 |
Shape-Tailored Deep Neural Networks Using PDEs for Segmentation | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) have been used extensively for segmentation problems in computer vision He et al . ( 2017 ) ; He et al . ( 2016 ) ; Chen et al . ( 2017 ) ; Xie & Tu ( 2015 ) . CNNs provide a framework for learning descriptors that are able to discriminate different textured or se... | This papers presents shape-tailored deep neural networks (ST-DNN) and apply to the task of texture segmentation. ST-DNN are motivated by the prior work on shape-tailored descriptors (or smoothing) that aggregate image statistics within regions of the interest, and defined as a solutions to the Poisson PDE which balance... | SP:243a41483b648f364496a705cba45968c5c02968 |
Memory Optimization for Deep Networks | Deep learning is slowly , but steadily , hitting a memory bottleneck . While the tensor computation in top-of-the-line GPUs increased by 32× over the last five years , the total available memory only grew by 2.5× . This prevents researchers from exploring larger architectures , as training large networks requires more ... | The paper provides a framework (MoNET) to perform automatic memory optimization targeting deep neural networks. Their technique jointly optimizes the checkpointing schedule and the choice of individual ops to find the implementation with the least possible runtime overhead under certain memory constraints. They formula... | SP:9d73791f8959a5303d9ee3cdab9aa6d9b89f8ea7 |
Memory Optimization for Deep Networks | Deep learning is slowly , but steadily , hitting a memory bottleneck . While the tensor computation in top-of-the-line GPUs increased by 32× over the last five years , the total available memory only grew by 2.5× . This prevents researchers from exploring larger architectures , as training large networks requires more ... | The authors present MONeT, an automatic approach to jointly optimize operator cost and checkpoint scheduling for deep learning on a fixed memory budget. The paper thoroughly defines the problem, relevant previous work, and the MONeT framework. Given a fixed GPU memory budget, MONeT solves an integer program in order ... | SP:9d73791f8959a5303d9ee3cdab9aa6d9b89f8ea7 |
Memory Optimization for Deep Networks | Deep learning is slowly , but steadily , hitting a memory bottleneck . While the tensor computation in top-of-the-line GPUs increased by 32× over the last five years , the total available memory only grew by 2.5× . This prevents researchers from exploring larger architectures , as training large networks requires more ... | Training deep learning models is becoming increasingly challenging due to a memory bottleneck that limits the size of the feature maps that can be stored. The paper presents an automatic framework (MONET) that minimizes the memory footprint for deep networks. The novelty of MONET is that it jointly optimizes over: (a) ... | SP:9d73791f8959a5303d9ee3cdab9aa6d9b89f8ea7 |
Generalized Universal Approximation for Certified Networks | To certify safety and robustness of neural networks , researchers have successfully applied abstract interpretation , primarily using interval bound propagation . To understand the power of interval bounds , we present the abstract universal approximation ( AUA ) theorem , a generalization of the recent result by Baade... | This paper proposes to extend the techniques of Baader et al. [2020], demonstrating that interval analysis provable ReLU networks are universal approximators, to a larger class of activation functions, which they call squashable functions. Furthermore, they claim that their proof of this theorem is simpler due to us... | SP:8887faab7d4c3fc72d031bca7281683261833706 |
Generalized Universal Approximation for Certified Networks | To certify safety and robustness of neural networks , researchers have successfully applied abstract interpretation , primarily using interval bound propagation . To understand the power of interval bounds , we present the abstract universal approximation ( AUA ) theorem , a generalization of the recent result by Baade... | The paper shows an "augmented" universal approximation (UA) result for neural networks that the authors call Abstract UA (AUA for short) and the motivation comes from understanding expressivity and certifiability of NN. Their result holds for NN with a wide variety of activation units and this is the main point of the ... | SP:8887faab7d4c3fc72d031bca7281683261833706 |
Generalized Universal Approximation for Certified Networks | To certify safety and robustness of neural networks , researchers have successfully applied abstract interpretation , primarily using interval bound propagation . To understand the power of interval bounds , we present the abstract universal approximation ( AUA ) theorem , a generalization of the recent result by Baade... | This paper studies the universal approximation of robust networks called the abstract universal approximation. While the traditional universal approximation aims to approximate the single output corresponding to each input value, abstract universal approximation studies the output interval generated by the input interv... | SP:8887faab7d4c3fc72d031bca7281683261833706 |
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms | 1 INTRODUCTION . Actor-critic algorithms have enjoyed great success both theoretically ( Williams , 1992 ; Sutton et al. , 2000 ; Konda , 2002 ; Schulman et al. , 2015a ) and empirically ( Mnih et al. , 2016 ; Silver et al. , 2016 ; Schulman et al. , 2017 ; OpenAI , 2018 ) . There is , however , a longstanding gap betw... | The authors examine the commonly used paradigm of not discounting in the policy gradient objective. They propose two hypotheses relating to discounting. (1) discounting the critic improves representation learning. (2) undiscounted policy gradient is similar to discounting + an auxiliary loss. These hypotheses are studi... | SP:55498a52c714b215caa9902e24f67daedf1f6bfd |
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms | 1 INTRODUCTION . Actor-critic algorithms have enjoyed great success both theoretically ( Williams , 1992 ; Sutton et al. , 2000 ; Konda , 2002 ; Schulman et al. , 2015a ) and empirically ( Mnih et al. , 2016 ; Silver et al. , 2016 ; Schulman et al. , 2017 ; OpenAI , 2018 ) . There is , however , a longstanding gap betw... | In this paper, the authors focus on the discounting mismatch in the Actor-Critic algorithm. From comprehensive experiments, the authors claim that this mismatch is either a bias-variance representation tradeoff or an auxiliary task for the actor update. Since the discounting mismatch problem is a well-known gap between... | SP:55498a52c714b215caa9902e24f67daedf1f6bfd |
A Deeper Look at Discounting Mismatch in Actor-Critic Algorithms | 1 INTRODUCTION . Actor-critic algorithms have enjoyed great success both theoretically ( Williams , 1992 ; Sutton et al. , 2000 ; Konda , 2002 ; Schulman et al. , 2015a ) and empirically ( Mnih et al. , 2016 ; Silver et al. , 2016 ; Schulman et al. , 2017 ; OpenAI , 2018 ) . There is , however , a longstanding gap betw... | The paper proposes an empirical study of the discount factor as a regularization parameter in the actor-critic architectures. Specifically, the paper considers the case in which the actor and the critic employ different values of the discount factor. Two scenarios are considered. First, the paper analyzes the case in w... | SP:55498a52c714b215caa9902e24f67daedf1f6bfd |
Grounding Language to Autonomously-Acquired Skills via Goal Generation | We are interested in the autonomous acquisition of repertoires of skills . Languageconditioned reinforcement learning ( LC-RL ) approaches are great tools in this quest , as they allow to express abstract goals as sets of constraints on the states . However , most LC-RL agents are not autonomous and can not learn witho... | This paper proposes DECSTR, a goal-driven RL framework where the goal is represented as a binary vector that encodes the semantic relationships between objects. The state is assumed to contain disentangled features for each of the objects (and other features relating to the agent’s end-effectors). The architecture is b... | SP:b0203f1c10cc5af3a8273ab1517267704ee46743 |
Grounding Language to Autonomously-Acquired Skills via Goal Generation | We are interested in the autonomous acquisition of repertoires of skills . Languageconditioned reinforcement learning ( LC-RL ) approaches are great tools in this quest , as they allow to express abstract goals as sets of constraints on the states . However , most LC-RL agents are not autonomous and can not learn witho... | This work proposed DECSTR, a procedure for encouraging intrinsic motivation via an intermediate semantic state-space representation. The authors propose an intermediate semantic state space that the intrinsically motivated agent learns to explore. For the environment provided (a 3-block system), the agent fully explore... | SP:b0203f1c10cc5af3a8273ab1517267704ee46743 |
Grounding Language to Autonomously-Acquired Skills via Goal Generation | We are interested in the autonomous acquisition of repertoires of skills . Languageconditioned reinforcement learning ( LC-RL ) approaches are great tools in this quest , as they allow to express abstract goals as sets of constraints on the states . However , most LC-RL agents are not autonomous and can not learn witho... | The DECSTR system's intrinsic motivations may be applicable to other application domains, depending on how objects and relations are enumerated. This potential is not explored beyond the toy environment presented. The learning methods (especially inductive biases) are hand-crafted based on human-level knowledge about s... | SP:b0203f1c10cc5af3a8273ab1517267704ee46743 |
On interaction between augmentations and corruptions in natural corruption robustness | 1 Introduction . Robustness to distribution shift , i.e . when the train and test distributions differ , is an important feature of practical machine learning models . Among many forms of distribution shift , one particularly relevant category for computer vision are image corruptions . For example , test data may come... | The paper studies the importance of similarity between augmentations and corruptions for improving performance on those corruptions. To measure the distance between the augmentation and corruption distributions, the paper proposes a new metric, Minimal Sample Distance (MSD), which is the perceptual similarity between a... | SP:38b5e8808025e698744d9544e91d334ee6566fa2 |
On interaction between augmentations and corruptions in natural corruption robustness | 1 Introduction . Robustness to distribution shift , i.e . when the train and test distributions differ , is an important feature of practical machine learning models . Among many forms of distribution shift , one particularly relevant category for computer vision are image corruptions . For example , test data may come... | The paper introduces the Minimal Sample Distance (MSD): a measure of the minimal distance, in a trained network representation space, between samples modified with an augmentation and the average of all samples modified by a corruption. It uses this metric to claim that there exists a high correlation between the corru... | SP:38b5e8808025e698744d9544e91d334ee6566fa2 |
On interaction between augmentations and corruptions in natural corruption robustness | 1 Introduction . Robustness to distribution shift , i.e . when the train and test distributions differ , is an important feature of practical machine learning models . Among many forms of distribution shift , one particularly relevant category for computer vision are image corruptions . For example , test data may come... | This paper proposes ImageNet-\bar{C} which uses a smaller number of carefully chosen corruptions, compared to ImageNet-C. The authors try to argue that previous work is overfitting to ImageNet-C. They claim "overfitting indeed occurs." Additionally, they propose "Minimum Sample Distance," showing that they can predict ... | SP:38b5e8808025e698744d9544e91d334ee6566fa2 |
Deep Data Flow Analysis | 1 INTRODUCTION . Compiler implementation is a complex and expensive activity ( Cooper & Torczon , 2012 ) . For this reason , there has been significant interest in using machine learning to automate various compiler tasks ( Allamanis et al. , 2018 ) . Most works have restricted their attention to selecting compiler heu... | The authors present a language-independent, compiler-agnostic graph representation of programs (ProGraML) designed for machine learning. Their representation is noteworthy for including the program's control, data, and call dependencies, and more generally for including enough information for standard dataflow analyse... | SP:54b8e490c922c62392b4b475ceec8b8d3ec65c7b |
Deep Data Flow Analysis | 1 INTRODUCTION . Compiler implementation is a complex and expensive activity ( Cooper & Torczon , 2012 ) . For this reason , there has been significant interest in using machine learning to automate various compiler tasks ( Allamanis et al. , 2018 ) . Most works have restricted their attention to selecting compiler heu... | A methodology for learning representations of programs using graph neural networks applied to graphs extracted from a compiler intermediate representation is presented. The graph representation captures both control flow as well as data dependencies and also represents calls to/returns from functions. A large new datas... | SP:54b8e490c922c62392b4b475ceec8b8d3ec65c7b |
Deep Data Flow Analysis | 1 INTRODUCTION . Compiler implementation is a complex and expensive activity ( Cooper & Torczon , 2012 ) . For this reason , there has been significant interest in using machine learning to automate various compiler tasks ( Allamanis et al. , 2018 ) . Most works have restricted their attention to selecting compiler heu... | The paper proposes a new program representation to find semantic code embeddings (ProGraML). The authors use these embeddings to perform a number of traditional dataflow analysis tasks that you can find in modern day compilers. When trained under a supervised learning setting, Deep Data Flow (DDF) analysis learns to pe... | SP:54b8e490c922c62392b4b475ceec8b8d3ec65c7b |
Optimal Transport Graph Neural Networks | Current graph neural network ( GNN ) architectures naively average or sum node embeddings into an aggregated graph representation—potentially losing structural or semantic information . We here introduce OT-GNN , a model that computes graph embeddings using parametric prototypes that highlight key facets of different g... | The paper introduces a novel approach to aggregate information of graph neural network node embeddings in order to support graph-level machine learning, like graph classification or graph regression. The aggregation is performed by comparing the node embeddings of a graph to learned, prototypical node embeddings via th... | SP:d4a4b5024e975d9321a8cd123c64f4220e38b990 |
Optimal Transport Graph Neural Networks | Current graph neural network ( GNN ) architectures naively average or sum node embeddings into an aggregated graph representation—potentially losing structural or semantic information . We here introduce OT-GNN , a model that computes graph embeddings using parametric prototypes that highlight key facets of different g... | This paper combines OT with parametric graph neural network. It replace the inner product between the graph embedding and the first layer weights of MLP by the Wasserstein distance between the node embeddings and some point clouds. Then the GNN, point clouds and the downstream MLP are trained in an end-to-end way. A r... | SP:d4a4b5024e975d9321a8cd123c64f4220e38b990 |
Optimal Transport Graph Neural Networks | Current graph neural network ( GNN ) architectures naively average or sum node embeddings into an aggregated graph representation—potentially losing structural or semantic information . We here introduce OT-GNN , a model that computes graph embeddings using parametric prototypes that highlight key facets of different g... | The paper proposes OT-GNN, which incorporates optimal transport distance to message passing of GNN. The message passing is aggregated by using a Wasserstein discrepancy for a point cloud. The contrastive regularization is utilized to overcome extreme clustering of nodes of the same class. Also, in theory, the author sh... | SP:d4a4b5024e975d9321a8cd123c64f4220e38b990 |
Online Adversarial Purification based on Self-supervised Learning | 1 INTRODUCTION . Deep neural networks have achieved remarkable results in many machine learning applications . However , these networks are known to be vulnerable to adversarial attacks , i.e . strategies which aim to find adversarial examples that are close or even perceptually indistinguishable from their natural cou... | Online defenses of adversarial examples is an old topic: Given an input x (potentially adversarially perturbed) at test time, we want to sanitize x to get x', on which the trained classifier $g \circ f$ gives the correct answer. This paper proposes a new architecture for online defenses via self supervision. There are ... | SP:c8a94e6bc17a9e9c3b5345b436130668207d29bd |
Online Adversarial Purification based on Self-supervised Learning | 1 INTRODUCTION . Deep neural networks have achieved remarkable results in many machine learning applications . However , these networks are known to be vulnerable to adversarial attacks , i.e . strategies which aim to find adversarial examples that are close or even perceptually indistinguishable from their natural cou... | This paper studies adversarial defense by combing purification and self-supervised loss. During inference, the authors propose an online-purification method based on (clipped) iterative gradient ascent. The loss used by purification is from some pre-defined self-supervised tasks. During training, joint loss of softmax ... | SP:c8a94e6bc17a9e9c3b5345b436130668207d29bd |
Online Adversarial Purification based on Self-supervised Learning | 1 INTRODUCTION . Deep neural networks have achieved remarkable results in many machine learning applications . However , these networks are known to be vulnerable to adversarial attacks , i.e . strategies which aim to find adversarial examples that are close or even perceptually indistinguishable from their natural cou... | The paper introduces a defence for adversarial attack based on minimising a self-supervised loss on the test examples. Authors work under the assumption that minimising the self-supervised loss would be equivalent to minimising the supervised loss (to which they don't have access at test time). Authors evaluate their m... | SP:c8a94e6bc17a9e9c3b5345b436130668207d29bd |
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning | 1 INTRODUCTION . The proliferation of deep learning ( DL ) has been fueled , in part , by a rapid growth in the size and complexity of deep neural networks ( DNN ) ( Dean et al. , 2012 ; Ying et al. , 2018 ) . This has spurred the rapid development of hardware ( Wang et al. , 2016 ; Jouppi et al. , 2017 ) and software ... | This paper proposes a new algorithm called EGRL to improve computation graph running time by optimizing placement of the graph's components on memory. Specifically, the authors demonstrate the algorithm on the Intel Neural Networks Processor for Inference (NNP-I), which allows them to map neural network components on o... | SP:4bea250d51f4de4be23762958f555db511609842 |
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning | 1 INTRODUCTION . The proliferation of deep learning ( DL ) has been fueled , in part , by a rapid growth in the size and complexity of deep neural networks ( DNN ) ( Dean et al. , 2012 ; Ying et al. , 2018 ) . This has spurred the rapid development of hardware ( Wang et al. , 2016 ; Jouppi et al. , 2017 ) and software ... | The paper proposes Evolutionary Graph Reinforcement Learning to solve the memory placement problem. Main ideas are using GNN as the network architecture for reinforcement learning agents that look for more informed priors for evolutionary algorithms. Overall novelty of the paper comes from the neat combination of RL, E... | SP:4bea250d51f4de4be23762958f555db511609842 |
Optimizing Memory Placement using Evolutionary Graph Reinforcement Learning | 1 INTRODUCTION . The proliferation of deep learning ( DL ) has been fueled , in part , by a rapid growth in the size and complexity of deep neural networks ( DNN ) ( Dean et al. , 2012 ; Ying et al. , 2018 ) . This has spurred the rapid development of hardware ( Wang et al. , 2016 ; Jouppi et al. , 2017 ) and software ... | Optimizing the execution of deep neural networks has tremendous impact on the cost and performance in many industries due to the proliferation of "Deep Learning". There has recently been an interesting line of work of using learning to optimize policies related to placement and scheduling of the neural network computat... | SP:4bea250d51f4de4be23762958f555db511609842 |
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models | 1 INTRODUCTION . Modern multilingual methods , such as multilingual language models ( Devlin et al. , 2018 ; Lample & Conneau , 2019 ; Conneau et al. , 2019 ) and multilingual neural machine translation ( NMT ) ( Firat et al. , 2016 ; Johnson et al. , 2017 ; Aharoni et al. , 2019 ; Arivazhagan et al. , 2019 ) , have be... | The paper studies the behaviour of gradient similarities across languages in multilingual NMT models. They find gradient similarities mirror language similarity. Hence, they look at method to gradient-based methods for multilingual NMT. They apply PCGrad to the multilingual NMT task and also extend this method to addre... | SP:7d2d23d569debc0e7857f6b61b54c7d632bccefc |
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models | 1 INTRODUCTION . Modern multilingual methods , such as multilingual language models ( Devlin et al. , 2018 ; Lample & Conneau , 2019 ; Conneau et al. , 2019 ) and multilingual neural machine translation ( NMT ) ( Firat et al. , 2016 ; Johnson et al. , 2017 ; Aharoni et al. , 2019 ; Arivazhagan et al. , 2019 ) , have be... | This paper conducts comprehensive analyses and a method to the multi-task training in multilingual models. By analyzing the gradient similarity of two tasks in multilingual NMT, this paper reveals that gradient similarities reflect language proximities, correlate with model quality, and also evolve with layers and trai... | SP:7d2d23d569debc0e7857f6b61b54c7d632bccefc |
Gradient Vaccine: Investigating and Improving Multi-task Optimization in Massively Multilingual Models | 1 INTRODUCTION . Modern multilingual methods , such as multilingual language models ( Devlin et al. , 2018 ; Lample & Conneau , 2019 ; Conneau et al. , 2019 ) and multilingual neural machine translation ( NMT ) ( Firat et al. , 2016 ; Johnson et al. , 2017 ; Aharoni et al. , 2019 ; Arivazhagan et al. , 2019 ) , have be... | Taking multilingual NMT (MNMT) into account, this work, investigates better model optimization alternative, that is in part can be attributed as a multi-task optimization problem. MNMT's are quite beneficial from different perspectives (improving low-resource languages, efficiency, etc). However, their inherently multi... | SP:7d2d23d569debc0e7857f6b61b54c7d632bccefc |
MixSize: Training Convnets With Mixed Image Sizes for Improved Accuracy, Speed and Scale Resiliency | 1 INTRODUCTION Convolutional neural networks are successfully used to solve various tasks across multiple domains such as visual ( Krizhevsky et al. , 2012 ; Ren et al. , 2015 ) , audio ( van den Oord et al. , 2016 ) , language ( Gehring et al. , 2017 ) and speech ( Abdel-Hamid et al. , 2014 ) . While scale-invariance ... | The paper proposes the use of mixed image sizes during training. They argue empirically that such an approach improves generalization for both fixed image size (e.g. 224 in ImageNet) as well as for variable image size. The proposed training algorithm maintains the same computational budget at each step by either changi... | SP:a49dca8ae6f8da27c7e929ca096a71a8833cb989 |
MixSize: Training Convnets With Mixed Image Sizes for Improved Accuracy, Speed and Scale Resiliency | 1 INTRODUCTION Convolutional neural networks are successfully used to solve various tasks across multiple domains such as visual ( Krizhevsky et al. , 2012 ; Ren et al. , 2015 ) , audio ( van den Oord et al. , 2016 ) , language ( Gehring et al. , 2017 ) and speech ( Abdel-Hamid et al. , 2014 ) . While scale-invariance ... | This paper proposes to increase training costs to compensate for the reduced costs from multi-scale CNN training by either increasing batch size (and therefore lowering the number of iterations per epoch) or increasing the number of augmented versions (duplicates) of the same samples within a batch. The former allows f... | SP:a49dca8ae6f8da27c7e929ca096a71a8833cb989 |
MixSize: Training Convnets With Mixed Image Sizes for Improved Accuracy, Speed and Scale Resiliency | 1 INTRODUCTION Convolutional neural networks are successfully used to solve various tasks across multiple domains such as visual ( Krizhevsky et al. , 2012 ; Ren et al. , 2015 ) , audio ( van den Oord et al. , 2016 ) , language ( Gehring et al. , 2017 ) and speech ( Abdel-Hamid et al. , 2014 ) . While scale-invariance ... | This paper presents a mixed-size CNN training scheme, using several different input image sizes for one single model training. The authors assume the training budget, represented as S_i^2*B_i*D_i (i.e., spatial sample size, the number of batched distinct samples and the duplicates for each distinct sample), to be a fix... | SP:a49dca8ae6f8da27c7e929ca096a71a8833cb989 |
Data-aware Low-Rank Compression for Large NLP Models | 1 INTRODUCTION . The representations learned by large-scale Natural Language Processing ( NLP ) models such as BERT have been widely used in various tasks ( Devlin et al. , 2018 ) . The pre-trained models of BERT and its variations are used as feature extractors for the downstream tasks such as question answering and n... | This paper studies a technique to increase the inference speed and decrease model sizes of pretrained NLP models such as BERT. Since most operations in BERT consist of matrix multiplications, the authors conduct empirical experiments to show that while matrices themselves are not low-rank, the learned representation of... | SP:861821cf96296ddff89505ab191fb9d7bee45f6d |
Data-aware Low-Rank Compression for Large NLP Models | 1 INTRODUCTION . The representations learned by large-scale Natural Language Processing ( NLP ) models such as BERT have been widely used in various tasks ( Devlin et al. , 2018 ) . The pre-trained models of BERT and its variations are used as feature extractors for the downstream tasks such as question answering and n... | This work introduces a low-rank based compression method, called DRONE, to accelerate the inference of large NLP models. Instead of decomposing weight matrices in a model, DRONE proposes to exploit the low-rank decomposition by considering the input vectors, which can be in a low-rank space. To compress the whole model... | SP:861821cf96296ddff89505ab191fb9d7bee45f6d |
Data-aware Low-Rank Compression for Large NLP Models | 1 INTRODUCTION . The representations learned by large-scale Natural Language Processing ( NLP ) models such as BERT have been widely used in various tasks ( Devlin et al. , 2018 ) . The pre-trained models of BERT and its variations are used as feature extractors for the downstream tasks such as question answering and n... | The goal of this paper is to accelerate large-scale NLP models. This paper reduces the computational complexity by exploiting the data distribution. They claim that exploiting the data distribution enables us to perform low-rank approximation on feed-forward networks. Furthermore, they use that idea to reduce the compl... | SP:861821cf96296ddff89505ab191fb9d7bee45f6d |
Neighbor Class Consistency on Unsupervised Domain Adaptation | 1 INTRODUCTION . Recent advances in deep neural network have dominated many computer vision tasks , such as image recognition He et al . ( 2016 ) , object detectionGirshick ( 2015 ) , and semantic segmentationLong et al . ( 2015 ) . However , collection and manual annotation need no trivial human effort , especially fo... | This paper tackles Unsupervised Domain Adaptation. The authors focus on the intrinsic discriminative feature for target samples. The proposed method, Neighborhood Class Consistency among target samples and augmented ones, is proposed as a set of multiple losses to calculate the consistency from several aspects. The exp... | SP:0da26728efc61af9be3b15caa90317fd587c5e21 |
Neighbor Class Consistency on Unsupervised Domain Adaptation | 1 INTRODUCTION . Recent advances in deep neural network have dominated many computer vision tasks , such as image recognition He et al . ( 2016 ) , object detectionGirshick ( 2015 ) , and semantic segmentationLong et al . ( 2015 ) . However , collection and manual annotation need no trivial human effort , especially fo... | This paper proposed neighbor class consistency regularization together with an entropy-based weighting factor to tackle the problem of unsupervised domain adaptation. Another self class consistency regularization was further introduced to help training. The difference between "neighbor class" and "self class" is the po... | SP:0da26728efc61af9be3b15caa90317fd587c5e21 |
Neighbor Class Consistency on Unsupervised Domain Adaptation | 1 INTRODUCTION . Recent advances in deep neural network have dominated many computer vision tasks , such as image recognition He et al . ( 2016 ) , object detectionGirshick ( 2015 ) , and semantic segmentationLong et al . ( 2015 ) . However , collection and manual annotation need no trivial human effort , especially fo... | This paper addresses the unsupervised domain adaption (UDA) problem. Particularly, the paper proposes to impose neighbor class consistency on target features to preserve intrinsic discriminative nature of target data and presents an entropy-based weighting scheme to improve robustness against the potential noisy neighb... | SP:0da26728efc61af9be3b15caa90317fd587c5e21 |
CROSS-SUPERVISED OBJECT DETECTION | 1 INTRODUCTION . Deep architectures have achieved great success in many computer vision tasks including object recognition and the closely related problem of object detection . Modern detectors , such as the Faster RCNN ( Ren et al. , 2015 ) , YOLO ( Redmon et al. , 2016 ) , and RetinaNet ( Lin et al. , 2017 ) , use th... | The paper proposes a new task cross-supervised object detection, which trains object detectors on the combination of base class images with instance-level annotations and novel class image with only image-level annotations. A network with a recognition head which is trained by image-level annotations and a detection he... | SP:c394e67f3796de6bb8a6bddc2681dfa574a1ec51 |
CROSS-SUPERVISED OBJECT DETECTION | 1 INTRODUCTION . Deep architectures have achieved great success in many computer vision tasks including object recognition and the closely related problem of object detection . Modern detectors , such as the Faster RCNN ( Ren et al. , 2015 ) , YOLO ( Redmon et al. , 2016 ) , and RetinaNet ( Lin et al. , 2017 ) , use th... | This paper introduces a new method for training an object detector on a dataset that consists of some object categories with instance-level bounding box annotations, as well as some other object categories with only image-level labels. The topic is interesting, important, and potentially very useful for real applicatio... | SP:c394e67f3796de6bb8a6bddc2681dfa574a1ec51 |
CROSS-SUPERVISED OBJECT DETECTION | 1 INTRODUCTION . Deep architectures have achieved great success in many computer vision tasks including object recognition and the closely related problem of object detection . Modern detectors , such as the Faster RCNN ( Ren et al. , 2015 ) , YOLO ( Redmon et al. , 2016 ) , and RetinaNet ( Lin et al. , 2017 ) , use th... | This paper defines cross-supervised object detection which learns a detector from both image-level and instance-level annotations. It proposes a unified framework along with a spatial correlation module for the task. The spatial correlation module is used for transfer mapping information from base categories to novel c... | SP:c394e67f3796de6bb8a6bddc2681dfa574a1ec51 |
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS | 1 INTRODUCTION . Long Short-term Memory ( LSTM ) ( Hochreiter & Schmidhuber , 1997 ) and Gated Recurrent Unit ( GRU ) ( Chung et al. , 2014 ) are widely used and investigated for tasks that involve sequential data . They are generally believed to be capable of capturing long-range dependencies while being able to allev... | This paper proposes to linearize GRU and LSTM cells (as error terms should be negligible when inputs are small in magnitude). Putting these linearized, or, really, affine, RNN cells together into a single-layer sequence processor, thanks to the affine-ness, we can decompose the score that is obtained by taking dot prod... | SP:bb8dbe6d6edaa4fa6ee844e75bbeaf3ac4e96a38 |
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS | 1 INTRODUCTION . Long Short-term Memory ( LSTM ) ( Hochreiter & Schmidhuber , 1997 ) and Gated Recurrent Unit ( GRU ) ( Chung et al. , 2014 ) are widely used and investigated for tasks that involve sequential data . They are generally believed to be capable of capturing long-range dependencies while being able to allev... | This paper provides a reliable interpretation of modern RNN models, through unrolling GRU and LSTM cells. The approximate state representations include a token-level term that only depends on the current input token and a sentence-level term that depends on all inputs until the current token. The deriving process is cl... | SP:bb8dbe6d6edaa4fa6ee844e75bbeaf3ac4e96a38 |
SEQUENCE-LEVEL FEATURES: HOW GRU AND LSTM CELLS CAPTURE N-GRAMS | 1 INTRODUCTION . Long Short-term Memory ( LSTM ) ( Hochreiter & Schmidhuber , 1997 ) and Gated Recurrent Unit ( GRU ) ( Chung et al. , 2014 ) are widely used and investigated for tasks that involve sequential data . They are generally believed to be capable of capturing long-range dependencies while being able to allev... | This paper attempts to add a contribution on understanding how gated recurrent neural networks like GRUs and LSTMs can learn the representation of n-grams. The authors expand the sigmoid function and the hyperbolic tangent function using Taylor series to obtain approximated closed-form mathematical expression of hidden... | SP:bb8dbe6d6edaa4fa6ee844e75bbeaf3ac4e96a38 |
Semantically-Adaptive Upsampling for Layout-to-Image Translation | 1 INTRODUCTION . The layout-to-image translation task aims to translate input layouts to realistic images ( see Fig . 1 ( a ) ) , which have many real-world applications and draw much attention from the community ( Park et al. , 2019 ; Liu et al. , 2019 ; Jiang et al. , 2020 ; Tang et al. , 2020 ) . For example , Park ... | This paper proposes a semantically-adaptive upsampling approach for layout-to-image translation. It uses the semantic label map to predict spatially-adaptive upsampling kernels for feature map upsampling. Compared with traditional upsampling methods, it has a larger receptive field to focus on not only nearby pixels, ... | SP:9cc61518a96c3aebe0869a87fc84ea2e13c752e5 |
Semantically-Adaptive Upsampling for Layout-to-Image Translation | 1 INTRODUCTION . The layout-to-image translation task aims to translate input layouts to realistic images ( see Fig . 1 ( a ) ) , which have many real-world applications and draw much attention from the community ( Park et al. , 2019 ; Liu et al. , 2019 ; Jiang et al. , 2020 ; Tang et al. , 2020 ) . For example , Park ... | This paper proposes the Semantic-Adaptive UpSampling method to do feature upsample in layout-to-image translation task. The SA-UpSample module exploit the semantic information to learn adaptive upsample kernels for different input features. The proposed method has the advantage of global view, semantically-adaptive and... | SP:9cc61518a96c3aebe0869a87fc84ea2e13c752e5 |
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