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The Risks of Invariant Risk Minimization | 1 INTRODUCTION . Prediction algorithms are evaluated by their performance on unseen test data . In classical machine learning , it is common to assume that such data are drawn i.i.d . from the same distribution as the data set on which the learning algorithm was trained—in the real world , however , this is often not t... | This paper studies a theoretical aspect of IRM and how will it fail. Main contribution is pointing out that IRM is ineffective when the number of environments $E$ is smaller than the dimension of environmental feature $d_e$. A simple but universal model assumption is built, where environmental feature $z_e$ and causal ... | SP:c4d8b135b7625ac0b52bdc4b9753c0db61b4d777 |
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs | 1 Introduction . Commonly used losses such as cross-entropy used in deep neural network ( DNN ) models can be expressed as a sum over the per-sample losses incurred by the current estimate of the model . This allows the direct use of mature optimization routines , and is sufficient for many use cases . But in applicati... | This paper addresses the classical topic of directly optimizing non-decomposable loss functions. Since these metrics can be computed via linear programs, it is sufficient to compute gradient through the LP solver. To that end, the authors propose to use a particular method for solving linear programs. In experiments, t... | SP:76ae86cb0be31feacf85f95c6c90fdd19aee85a3 |
Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs | 1 Introduction . Commonly used losses such as cross-entropy used in deep neural network ( DNN ) models can be expressed as a sum over the per-sample losses incurred by the current estimate of the model . This allows the direct use of mature optimization routines , and is sufficient for many use cases . But in applicati... | This paper approximates several nondecomposable functions (AUC and F1-score) as linear programmings and uses them as loss functions for network training. In the linear programmings, the constraints are indeterministic at each mini-batch, the number of constraints increases quadratically to the number of training sample... | SP:76ae86cb0be31feacf85f95c6c90fdd19aee85a3 |
Dynamic Backdoor Attacks Against Deep Neural Networks | 1 INTRODUCTION . Recent research has shown that deep neural network ( DNN ) models are vulnerable to various security and privacy attacks ( Papernot et al. , 2016 ; 2017 ; Shokri et al. , 2017 ; Salem et al. , 2019 ; 2020 ; Tramèr et al. , 2016 ; Oh et al. , 2018 ) . One such attack that receives a large amount of att... | This paper proposed a class of methods for dynamic backdoor attack. The main idea is to generate different backdoor patterns and locations in backdoor attack. The threat model is the attacker has full access to the training data and the model training procedure. Both single-target and multi-target class-conditional tri... | SP:6bfdc3596045227aaed04a50cd934e5d4bc1e9ad |
Dynamic Backdoor Attacks Against Deep Neural Networks | 1 INTRODUCTION . Recent research has shown that deep neural network ( DNN ) models are vulnerable to various security and privacy attacks ( Papernot et al. , 2016 ; 2017 ; Shokri et al. , 2017 ; Salem et al. , 2019 ; 2020 ; Tramèr et al. , 2016 ; Oh et al. , 2018 ) . One such attack that receives a large amount of att... | This paper outlines improved backdoor attacks for deep neural networks. Backdoor attacks are training-time attacks whereby an adveresary trains a network in such a way that it functions as a classifier on honestly generated images, but it has the added caveat of also being able to misclassify on images corrupted via a ... | SP:6bfdc3596045227aaed04a50cd934e5d4bc1e9ad |
Information Laundering for Model Privacy | 1 INTRODUCTION . An emerging number of applications involve the following user-scenario . Alice developed a model that takes a specific query as input and calculates a response as output . The model is a stochastic black-box that may represent a novel type of ensemble models , a known deep neural network architecture w... | This paper aims to tackle the problem of model stealing/extraction, as in stealing a model that is deployed remotely, through API access. The threat model that they are aiming to protect against is not well-defined. The proposed method is information theoretic. They propose adding two modules (kernels) before and after... | SP:ac5c618268cfa8b094aa4c7906c3bc6427035da5 |
Information Laundering for Model Privacy | 1 INTRODUCTION . An emerging number of applications involve the following user-scenario . Alice developed a model that takes a specific query as input and calculates a response as output . The model is a stochastic black-box that may represent a novel type of ensemble models , a known deep neural network architecture w... | This paper studies model privacy and its privacy and utility tradeoff. In particular, the authors proposed information laundered model where the input and output of the true model are perturbed. The objective is to minimize the KL divergence between the true model and laundered model with mutual information between in... | SP:ac5c618268cfa8b094aa4c7906c3bc6427035da5 |
Generative Time-series Modeling with Fourier Flows | 1 INTRODUCTION . Lack of access to data is a key hindrance to the development of machine learning solutions in application domains where data sharing may lead to privacy breaches ( Walonoski et al . ( 2018 ) ) . Areas where this problem is most conspicuous include medicine , where access to ( highly-sensitive ) clinica... | The paper presents Fourier Flows (FF), which is a time series generative model in the frequency domain. It shows that the Jacobian of the DFT is equal to 1, which means that DFT does not add too much overhead. The results on the real-world datasets are encouraging and expected because the predictive results mainly rely... | SP:15f1f17659b0f5d0b5a71f32a3c1ca918d54cc3d |
Generative Time-series Modeling with Fourier Flows | 1 INTRODUCTION . Lack of access to data is a key hindrance to the development of machine learning solutions in application domains where data sharing may lead to privacy breaches ( Walonoski et al . ( 2018 ) ) . Areas where this problem is most conspicuous include medicine , where access to ( highly-sensitive ) clinica... | The paper introduces a new convolutional flow architecture that uses the DFT to convert the generated time series to the frequency domain. Convolutions are performed by multiplication in the frequency domain through a spectral affine layer that transform the even or odd part of the signal using a data dependent filter.... | SP:15f1f17659b0f5d0b5a71f32a3c1ca918d54cc3d |
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings | 1 INTRODUCTION . Natural organisms benefit from the fact that their sensory inputs and action outputs are all organized in the same space , that is , the physical universe . This consistency makes it easy to apply the same predictive functions across diverse settings . Deep multi-task learning ( Deep MTL ) has shown a ... | .** Authors present a methodology for performing multi-task learning from data with disjoint and heterogeneous input domains. Particularly, they introduce an embedding of the inputs, in order to project each pair of input-output observations in a common continuous manifold where the exploration is significantly easier.... | SP:b97073d441c824891124ad65fd95937fe9e53533 |
The Traveling Observer Model: Multi-task Learning Through Spatial Variable Embeddings | 1 INTRODUCTION . Natural organisms benefit from the fact that their sensory inputs and action outputs are all organized in the same space , that is , the physical universe . This consistency makes it easy to apply the same predictive functions across diverse settings . Deep multi-task learning ( Deep MTL ) has shown a ... | This paper presents the traveling observer model (TOM), a general framework to learn multiple heterogenous supervized (input,output) tasks, which are indexed by a continuous "variable embedding" that is automatically learned by the system. The authors show on simple problems that the learned task embeddings can recover... | SP:b97073d441c824891124ad65fd95937fe9e53533 |
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights | 1 INTRODUCTION . Normalization techniques , such as batch normalization ( BN ) ( Ioffe & Szegedy , 2015 ) , layer normalization ( LN ) ( Ba et al. , 2016 ) , instance normalization ( IN ) ( Ulyanov et al. , 2016 ) , and group normalization ( GN ) ( Wu & He , 2018 ) , have become standard tools for training deep neural ... | This paper shows that momentum-based gradient descent optimizers reduce the effective step size in training scale-invariant models including deep neural networks normalized by batch normalization, layer normaliztion, instance normalization and group normalization. The authors then propose a solution that projects the u... | SP:4e2ac2684ed817d9221154e922dabd54ff51fc90 |
AdamP: Slowing Down the Slowdown for Momentum Optimizers on Scale-invariant Weights | 1 INTRODUCTION . Normalization techniques , such as batch normalization ( BN ) ( Ioffe & Szegedy , 2015 ) , layer normalization ( LN ) ( Ba et al. , 2016 ) , instance normalization ( IN ) ( Ulyanov et al. , 2016 ) , and group normalization ( GN ) ( Wu & He , 2018 ) , have become standard tools for training deep neural ... | This paper points out that momentum in GD optimizers results in a far more rapid reduction in effective step sizes for scale-invariant weights. To solve the problem, two algorithms called SGDP and AdamP are proposed, which project the updates to tangent space of the parameter. Experiments on several tasks including im... | SP:4e2ac2684ed817d9221154e922dabd54ff51fc90 |
Adversarial Attacks on Binary Image Recognition Systems | 1 INTRODUCTION . In this paper we study adversarial attacks on models designed to classify binary ( i.e . black and white ) images . Models for binary image classification are heavily used across a variety of applications that include receipt processing , passport recognition , check processing , and license plate reco... | Adversarial attacks for binary image classification are unique from traditional attacks on color images due to its limited available space for perturbation. This paper proposes an algorithm that efficiently searches for valid attacks (both targeted and untargeted) which cause minimum flipped pixels. The proposed metho... | SP:a77bf951ae279e9c2cd14f7f1d8066e7be62db15 |
Adversarial Attacks on Binary Image Recognition Systems | 1 INTRODUCTION . In this paper we study adversarial attacks on models designed to classify binary ( i.e . black and white ) images . Models for binary image classification are heavily used across a variety of applications that include receipt processing , passport recognition , check processing , and license plate reco... | The main question this paper aims to answer is how vulnerable binary image classification systems are. This is an important question because of the application of such binary image classifications for check processing, invoice processing, and license plate registration. One also would think that such systems are less ... | SP:a77bf951ae279e9c2cd14f7f1d8066e7be62db15 |
Continual learning with neural activation importance | 1 INTRODUCTION . Continual learning , or sequential learning , is a concept of online learning along multiple sequential tasks . The aim of continual learning is learning a set of related tasks that are observed irregularly or separately online . Therefore each task does not necessarily contain overlapped classes with ... | This paper proposes a method that tackles the problem of catastrophic forgetting in continual neural networks by assigning importance to neuron activations while tasks are executed in sequence. Similar to previous research (Jung et al., 2020), the proposed method measures neuron importance using average activation valu... | SP:e0bef945605bdba26787c47c6c8ecaa896610b54 |
Continual learning with neural activation importance | 1 INTRODUCTION . Continual learning , or sequential learning , is a concept of online learning along multiple sequential tasks . The aim of continual learning is learning a set of related tasks that are observed irregularly or separately online . Therefore each task does not necessarily contain overlapped classes with ... | This paper introduces a regularization approach for stable continual learning of sequential tasks. The proposed method computes neuron importance based on the activation values of nodes with their respective standard deviation. It further suggests a weight re-initialization scheme to achieve better performance. Experim... | SP:e0bef945605bdba26787c47c6c8ecaa896610b54 |
Temporal and Object Quantification Nets | 1 INTRODUCTION . When watching a soccer match ( Fig . 1 ) , we see more than just players and a ball moving around . Rather , we see events and actions in terms of high-level concepts , including relations between agents and objects . For example , people can easily recognize when one player has control of the ball , o... | This paper proposes TOQ-Nets which is a structured neural network that learns to describe complex activities over entities and time. The model leverages relational reasoning layers which are the Neural Logic Machines (NLM) to capture the spatial information. To further capture the temporal information, this paper propo... | SP:11763efc3e362e7b3a565f2c85e9eb1a03afda8f |
Temporal and Object Quantification Nets | 1 INTRODUCTION . When watching a soccer match ( Fig . 1 ) , we see more than just players and a ball moving around . Rather , we see events and actions in terms of high-level concepts , including relations between agents and objects . For example , people can easily recognize when one player has control of the ball , o... | This paper overall presents a model that defines the multi-person activities using logic expressions and uses neural logic models to generate recognitions and predictions over events. Specifically, the model follows the neural logic machines (Dong et al.) to define the operations in the networks. Authors demonstrate th... | SP:11763efc3e362e7b3a565f2c85e9eb1a03afda8f |
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization | 1 INTRODUCTION . Modeling dynamical systems in order to forecast the future is of critical importance in a wide range of fields including , e.g. , fluid dynamics , epidemiology , economics , and neuroscience [ 2 ; 21 ; 45 ; 22 ; 14 ] . Many dynamical systems are described by systems of non-linear differential equations... | The paper demonstrates that incorporating equivariance (i.e. symmetries) into model for predicting fluid dynamics improves its performance, especially when the test distribution is transformed by those symmetry groups. Leveraging the recent literature on equivariant CNNs, the paper proposes a CNN model that is equivari... | SP:d39fbf20324d392c4b0cbbf7fc40451d38285e6f |
Incorporating Symmetry into Deep Dynamics Models for Improved Generalization | 1 INTRODUCTION . Modeling dynamical systems in order to forecast the future is of critical importance in a wide range of fields including , e.g. , fluid dynamics , epidemiology , economics , and neuroscience [ 2 ; 21 ; 45 ; 22 ; 14 ] . Many dynamical systems are described by systems of non-linear differential equations... | This paper studies improving the modeling of physical dynamics with equivariant neural networks. In particular, this paper focuses on a new type of data governed by physical models. Several special symmetry groups are considered to better characterize the system, including uniform motion equivariance, resolution-indepe... | SP:d39fbf20324d392c4b0cbbf7fc40451d38285e6f |
Adam$^+$: A Stochastic Method with Adaptive Variance Reduction | 1 INTRODUCTION . Adaptive gradient methods ( Duchi et al. , 2011 ; McMahan & Streeter , 2010 ; Tieleman & Hinton , 2012 ; Kingma & Ba , 2014 ; Reddi et al. , 2019 ) are one of the most important variants of Stochastic Gradient Descent ( SGD ) in modern machine learning applications . Contrary to SGD , adaptive gradient... | This paper proposes a new optimizer called Adam+, with two main distinctions from standard Adam template: 1) the first order moment estimate is computed using the gradient evaluated at an extrapolated iterate. 2) the step size is scaled with the square root of the norm of the first order moment, rather than the exponen... | SP:522aebc6b5d57da5550911d44f91572975118008 |
Adam$^+$: A Stochastic Method with Adaptive Variance Reduction | 1 INTRODUCTION . Adaptive gradient methods ( Duchi et al. , 2011 ; McMahan & Streeter , 2010 ; Tieleman & Hinton , 2012 ; Kingma & Ba , 2014 ; Reddi et al. , 2019 ) are one of the most important variants of Stochastic Gradient Descent ( SGD ) in modern machine learning applications . Contrary to SGD , adaptive gradient... | This paper proposes the Adam+ algorithm that maintains an exponential moving average of the first moment and normalizes it by its $p$-th moment for some $p \in (1/2, 1)$. When $p = 2/3$, with appropriate hyperparameters, Adam+ achieves the state-of-the-art complexity $O(1/\epsilon^{3.5})$ to obtain an approximate first... | SP:522aebc6b5d57da5550911d44f91572975118008 |
Deep Goal-Oriented Clustering | 1 INTRODUCTION . Much of the advances in supervised learning in the past decade are due to the development of deep neural networks ( DNN ) , a class of hierarchical function approximators that are capable of learning complex input-output relationships . Prime examples of such advances include image recognition ( Krizhe... | This paper introduces Deep Goal Oriented Clustering, an approach for joint clustering and classification. The approach shares a latent embedding for the data between the two tasks. The latent embedding is parameterized by a mixture of Gaussians. The approach gives a probabilistic, VAE-based formulation and derives the ... | SP:57ac36954da5fb78b9f816c3aac6fd19d9c70e4f |
Deep Goal-Oriented Clustering | 1 INTRODUCTION . Much of the advances in supervised learning in the past decade are due to the development of deep neural networks ( DNN ) , a class of hierarchical function approximators that are capable of learning complex input-output relationships . Prime examples of such advances include image recognition ( Krizhe... | In traditional clustering algorithms, incorporation of “side-information”, or additional features only available during training time, typically assume some prior knowledge of the ground-truth clusters, or constraints on those clusters. However, this need not be the case, as training samples may contain arbitrary infor... | SP:57ac36954da5fb78b9f816c3aac6fd19d9c70e4f |
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers | 1 INTRODUCTION . Although neural networks ( NNs ) have achieved great success on various complicated tasks , they remain susceptible to adversarial examples ( Szegedy et al. , 2013 ) : imperceptible perturbations of test samples might unexpectedly change the NN predictions . Therefore , it is crucial to conduct formal ... | The authors demonstrate that using a modification of the LiRPA method during the branch-and-bound process for solving the neural network verification problem can lead to significant speed-ups. The experimental results are strong. The authors convincingly show that the their method outperforms the existing state-of-the-... | SP:c0e79bbc34b390168a3bc07c2c1b410736100783 |
Fast and Complete: Enabling Complete Neural Network Verification with Rapid and Massively Parallel Incomplete Verifiers | 1 INTRODUCTION . Although neural networks ( NNs ) have achieved great success on various complicated tasks , they remain susceptible to adversarial examples ( Szegedy et al. , 2013 ) : imperceptible perturbations of test samples might unexpectedly change the NN predictions . Therefore , it is crucial to conduct formal ... | This paper describes a branch-and-bound (BaB) process for neural network verification that uses linear relaxation based perturbation analysis (LiRPA). It gives a way to tighten the bounds obtained via LiRPA. Overall, this results is a complete verification procedure, which is an order of magnitude faster than existing ... | SP:c0e79bbc34b390168a3bc07c2c1b410736100783 |
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction | 1 INTRODUCTION . Transformer ( Vaswani et al. , 2017 ) is the state-of-the-art for sequential modeling which achieves superior performances in multiple domains , including natural language understanding ( Devlin et al. , 2019 ) , image generation ( Parmar et al. , 2018 ) and time-series forecasting ( Li et al. , 2019 )... | This paper proposed a modification to the classical transformer architecture and demonstrated significant performance gain on multiple benchmark tasks in both natural language processing and computer vision. Specifically, the authors propose to introduce a convolution-based attention map prediction module, so the depen... | SP:5c679af56d9947de1b793665b4c4eb952a672f0a |
Predictive Attention Transformer: Improving Transformer with Attention Map Prediction | 1 INTRODUCTION . Transformer ( Vaswani et al. , 2017 ) is the state-of-the-art for sequential modeling which achieves superior performances in multiple domains , including natural language understanding ( Devlin et al. , 2019 ) , image generation ( Parmar et al. , 2018 ) and time-series forecasting ( Li et al. , 2019 )... | This paper proposes a novel approach to improve self-attention through by bridging the attention maps from different layers via a chain of convolution-based prediction modules. In particular, it proposes to augment the existing works on Transformer through supplementary prediction modules by CNN-based attention predic... | SP:5c679af56d9947de1b793665b4c4eb952a672f0a |
Lie Algebra Convolutional Neural Networks with Automatic Symmetry Extraction | Many machine learning ( ML ) tasks involve data from unfamiliar domains , which may or may not have hidden symmetries . While much of the work on equivariant neural networks focuses on equivariant architectures , the ability of the architecture to discover symmetries in a given dataset is less studied . Convolutional N... | The paper presents a new approach for building group-equivariant neural networks. The authors propose *L-conv*, a layer which is equivariant to transformations from a group $G$ in the neighborhood of identity. They show that a network with a sufficient number of such layers is $G$-equivariant as a whole. Additionally, ... | SP:c157b650393ac987f2816812d1b720e41a8eb39c |
Lie Algebra Convolutional Neural Networks with Automatic Symmetry Extraction | Many machine learning ( ML ) tasks involve data from unfamiliar domains , which may or may not have hidden symmetries . While much of the work on equivariant neural networks focuses on equivariant architectures , the ability of the architecture to discover symmetries in a given dataset is less studied . Convolutional N... | The paper meticulously builds a theoretical framework for Lie-algebra convolutional layers and then goes on to show how CNNs, GCNs and FC layers are a special case of L-convs. The paper also demonstrates how the underlying generators can be learnt from data and provide convincing supporting experimental results. The pr... | SP:c157b650393ac987f2816812d1b720e41a8eb39c |
Temperature check: theory and practice for training models with softmax-cross-entropy losses | 1 INTRODUCTION . Deep learning has led to breakthroughs across a slew of classification tasks ( LeCun et al. , 1989 ; Krizhevsky et al. , 2012 ; Zagoruyko and Komodakis , 2017 ) . Crucial components of this success have been the use of the softmax function to model predicted class-probabilities combined with the cross-... | This paper studies how temperature scaling affects training dynamics of neural networks (with softmax layer and cross-entropy loss). The theoretical analysis shows that neural networks trained with smaller inverse temperatures (beta) exit the linear regime faster, which implies better performance. Experiments on image ... | SP:9002e8827008fb50594757c4aab871b9cb29963b |
Temperature check: theory and practice for training models with softmax-cross-entropy losses | 1 INTRODUCTION . Deep learning has led to breakthroughs across a slew of classification tasks ( LeCun et al. , 1989 ; Krizhevsky et al. , 2012 ; Zagoruyko and Komodakis , 2017 ) . Crucial components of this success have been the use of the softmax function to model predicted class-probabilities combined with the cross-... | This paper investigates how the inverse temperature parameter $\beta$ in the softmax-cross-entropy loss impacts the learning and the generalization. In the theory part, this paper introduces the concepts of early learning timescale and nonlinear timescale, and shows how the learning dynamics depend on the parameter $\b... | SP:9002e8827008fb50594757c4aab871b9cb29963b |
On Statistical Bias In Active Learning: How and When to Fix It | 1 INTRODUCTION . In modern machine learning , unlabelled data can be plentiful while labelling requires scarce resources and expert attention , for example in medical imaging or scientific experimentation . A promising solution to this is active learning—picking the most informative datapoints to label that will hopefu... | The authors consider the bias (in the risk) introduced by active sampling strategies with respect to the true underlying data generating distribution. They then propose two estimators of the risk -- SURE and PURE -- that are unbiased and asymptotically consistent under certain assumptions. The authors consider two toy ... | SP:8facdf5c306fe8f0e82a7072ebb51ce7efd0df7f |
On Statistical Bias In Active Learning: How and When to Fix It | 1 INTRODUCTION . In modern machine learning , unlabelled data can be plentiful while labelling requires scarce resources and expert attention , for example in medical imaging or scientific experimentation . A promising solution to this is active learning—picking the most informative datapoints to label that will hopefu... | This paper analyzes the bias of models in pool-based active learning settings where the sampling procedure is probabilistic (non-deterministic). It proposes two unbiased estimators of the population risk that weight the loss for each sampled data point. Empirical experiments demonstrate that these unbiased estimators w... | SP:8facdf5c306fe8f0e82a7072ebb51ce7efd0df7f |
Towards certifying $\ell_\infty$ robustness using Neural networks with $\ell_\infty$-dist Neurons | 1 INTRODUCTION . Modern neural networks are usually sensitive to small , adversarially chosen perturbations to the inputs ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) . Given an image x that is correctly classified by a neural network , a malicious attacker may find a small adversarial perturbation δ such that the ... | In this paper, the authors consider the task of adversarial/robust learning with respect to neural networks. The problem is a well-motivated one: suppose there is a neural network that on input a training set T={(x_i,y_i)} does a good classification job, but an adversary comes along and modifies some parts of T, then i... | SP:a84853c2ccc676838a77ef3323d5eb60fb5b638b |
Towards certifying $\ell_\infty$ robustness using Neural networks with $\ell_\infty$-dist Neurons | 1 INTRODUCTION . Modern neural networks are usually sensitive to small , adversarially chosen perturbations to the inputs ( Szegedy et al. , 2013 ; Biggio et al. , 2013 ) . Given an image x that is correctly classified by a neural network , a malicious attacker may find a small adversarial perturbation δ such that the ... | The contribution of the paper is threefold: First, it proposes a novel variation of AdderNet (Chen et al. 2020) that ensures the network is always 1-Lipschitz with respect to $\ell_{\infty}$ norm. The architecture allows one to generate robustness certificates with respect to $\ell_{\infty}$ norm with only a single for... | SP:a84853c2ccc676838a77ef3323d5eb60fb5b638b |
Generalizing Tree Models for Improving Prediction Accuracy | 1 INTRODUCTION . Can we generalize and improve the representation power of tree models ? The tree models learn a structure where the decision process is easy to follow ( Breiman et al. , 1984 ) . Due to this attractive feature of tree models , various efforts are being put to improve their performance or to utilize the... | The authors propose Decision Transformer Networks (DTNs): a model that generalizes decision trees to deep network style decision graphs. Structurally, DTNs are similar to deep neural networks with layers of nodes operating as stochastic decision functions and which output probabilities of an input vector belonging to e... | SP:023f656e324a7c753e79703a9e9ff7638990a11c |
Generalizing Tree Models for Improving Prediction Accuracy | 1 INTRODUCTION . Can we generalize and improve the representation power of tree models ? The tree models learn a structure where the decision process is easy to follow ( Breiman et al. , 1984 ) . Due to this attractive feature of tree models , various efforts are being put to improve their performance or to utilize the... | The authors propose a new model that consists in transforming decision trees by seeing such models as a sequence of "layers" made of all nodes at the same tree depth. Each layer is then modelled as a stochastic decision (multiplication by a stochastic matrix) which can mimic the behaviour of both standard decision tree... | SP:023f656e324a7c753e79703a9e9ff7638990a11c |
Representational aspects of depth and conditioning in normalizing flows | 1 INTRODUCTION . Deep generative models are one of the lynchpins of unsupervised learning , underlying tasks spanning distribution learning , feature extraction and transfer learning . Parametric families of neural-network based models have been improved to the point of being able to model complex distributions like im... | The normalizing flows (NF) are among popular generative models, as they have the capability of converting a simple base distribution to complex distributions by successively applying change of variable formula. More importantly, NFs let us do inference by maximizing exact likelihood functions instead of other approxima... | SP:1d9409b7e7670ef4b1083ebf80e9d2d83259467b |
Representational aspects of depth and conditioning in normalizing flows | 1 INTRODUCTION . Deep generative models are one of the lynchpins of unsupervised learning , underlying tasks spanning distribution learning , feature extraction and transfer learning . Parametric families of neural-network based models have been improved to the point of being able to model complex distributions like im... | The paper gives very thorough mathematical representation for two challenges related to normalization flows, namely model’s large depth and conditioning which relates to the smallest singular value of the forward map. Topic is presented in a very orderly and comprehensive manner. All variables and concepts are explaine... | SP:1d9409b7e7670ef4b1083ebf80e9d2d83259467b |
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation | 1 INTRODUCTION . Deep CNNs that are widely used for image classification ( Huang et al . ( 2017 ) ) often require large computing resources and process each image with high computational complexity ( Livni et al . ( 2014 ) ) . In real-world scenarios , the prior probability of occurrence of individual classes in an ima... | A knowledge distillation framework is proposed for efficient object recognition. In this framework, the teacher network (TN) performs high accuracy prediction while two student networks (SN) mimic the prediction from TN. The first SN learns from TN while the second SN is a binarized form (BSN) of the first SN. The des... | SP:22c51358e79eeee073c17182afec21de4f57148a |
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation | 1 INTRODUCTION . Deep CNNs that are widely used for image classification ( Huang et al . ( 2017 ) ) often require large computing resources and process each image with high computational complexity ( Livni et al . ( 2014 ) ) . In real-world scenarios , the prior probability of occurrence of individual classes in an ima... | The proposed work trains a teacher-student network using an online distillation paradigm. The student is a binarized network (BSN) trained to be accurate on frequent classes. An attention triplet loss is employed to improve the accuracy of the BSN and its ability to detect outlier classes. Faster convergency of BSN vs ... | SP:22c51358e79eeee073c17182afec21de4f57148a |
Whitening for Self-Supervised Representation Learning | 1 INTRODUCTION . One of the current main bottlenecks in deep network training is the dependence on large annotated training datasets , and this motivates the recent surge of interest in unsupervised methods . Specifically , in self-supervised representation learning , a network is ( pre- ) trained without any form of m... | The paper proposes to first do representation "whitening", so that the representations are scattered in the space and not collapsing to a single data point; then compute distance metric on top of that (e.g. Euclidean, cosine similarity). A nice thing about explicit scattering is that it does not require large numbers o... | SP:a3a46e67002f078845d83f3575b704160c30cffb |
Whitening for Self-Supervised Representation Learning | 1 INTRODUCTION . One of the current main bottlenecks in deep network training is the dependence on large annotated training datasets , and this motivates the recent surge of interest in unsupervised methods . Specifically , in self-supervised representation learning , a network is ( pre- ) trained without any form of m... | This paper proposes the mean square error loss with whitening operation to project positive pairs closely to each others while projecting the different positive pairs far away from each other on a unit sphere. This way, similar to BYOL, this paper removes the construction of negative pairs while improving the MoCo-V2 s... | SP:a3a46e67002f078845d83f3575b704160c30cffb |
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations | 1 INTRODUCTION . Recommendation systems have gained great success in various commercial applications for decades . The objective of these systems is to retrieve relevant candidate items from an corpus based on user features and historical behaviors . One of the early successful techniques of recommendation systems is t... | This paper presents an end-to-end deep retrieval method for recommendation. The model encodes all candidates into a discrete latent space, and learns the latent space parameters alongside the other neural network parameters. Recommendation is performed through beam search. The paper compares the method on two public da... | SP:f2a32676f88aaeda591223904a57338a1a2b699a |
Deep Retrieval: An End-to-End Structure Model for Large-Scale Recommendations | 1 INTRODUCTION . Recommendation systems have gained great success in various commercial applications for decades . The objective of these systems is to retrieve relevant candidate items from an corpus based on user features and historical behaviors . One of the early successful techniques of recommendation systems is t... | The paper presents a method for "end-to-end" learning for retrieving top-k items in recommendation system setup. This is achieved by learning the hidden representations of the items and under neural network as a single objective function optimized using the expectation maximization framework. It is claimed that the pro... | SP:f2a32676f88aaeda591223904a57338a1a2b699a |
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis | 1 INTRODUCTION . The advent of deep generative models ( e.g . Burgess et al. , 2019 ; Greff et al. , 2019 ; Engelcke et al. , 2019 ) with their aptitude for unsupervised representation learning casts a new light on learning affordances ( Gibson , 1977 ) . This kind of representation learning raises a tantalising questi... | This paper presents a method with two goals: (1) estimate if a given tool can solve a given task, and (2) generate a tool that can solve a given task. One encoder maps a tool image and silhouette into a latent code, and a decoder maps this code into a mesh; another encoder maps a task image into a latent code; this cod... | SP:a43b09142b12e0b68e3e658a9011068615c46481 |
Imagine That! Leveraging Emergent Affordances for 3D Tool Synthesis | 1 INTRODUCTION . The advent of deep generative models ( e.g . Burgess et al. , 2019 ; Greff et al. , 2019 ; Engelcke et al. , 2019 ) with their aptitude for unsupervised representation learning casts a new light on learning affordances ( Gibson , 1977 ) . This kind of representation learning raises a tantalising questi... | The authors try to tackle the problem of tool synthesis by using a classifier to guide the learning and exploitation of a generative model through activation maximization. Experiments are conducted on the proposed simulated reaching dataset on tool selection and tool imagination. While the idea of synthesizing tools s... | SP:a43b09142b12e0b68e3e658a9011068615c46481 |
Improving Model Robustness with Latent Distribution Locally and Globally | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved impressive performance on a broad range of datasets , yet can be easily fooled by adversarial examples or perturbations ( LeCun et al. , 2015 ; He et al. , 2016 ; Gers et al. , 1999 ) . Adversarial examples have been shown to be ubiquitous beyond different ta... | This paper presents a framework for adversarial robustness via incorporating local and global structure of the data manifold. Specifically, the key motivation is that standard adversarial methods typically use only sample specific perturbations for generating the adversarial examples, and thus using them for robustness... | SP:cc0fa305b4443aa39690c404edc80ecf9c5757ae |
Improving Model Robustness with Latent Distribution Locally and Globally | 1 INTRODUCTION . Deep Neural Networks ( DNNs ) have achieved impressive performance on a broad range of datasets , yet can be easily fooled by adversarial examples or perturbations ( LeCun et al. , 2015 ; He et al. , 2016 ; Gers et al. , 1999 ) . Adversarial examples have been shown to be ubiquitous beyond different ta... | The paper analyzes the property of local and global data manifold for adversarial training. In particular, they used a discriminator-classifier model, where the discriminator tries to differentiate between the natural and adversarial space, and the classifier aims to classify between them while maintaining the constrai... | SP:cc0fa305b4443aa39690c404edc80ecf9c5757ae |
Learning explanations that are hard to vary | 1 INTRODUCTION Consider the top of Figure 1 , which shows a view from above of the loss surface obtained as we vary a two dimensional parameter vector θ “ pθ1 , θ2q , for a fictional dataset containing two observations xA and xB . Note the two global minima on the top-right and bottom-left . Depending on the initial va... | this work posits that invariant mechanisms exist in a dataset. a machine learning algorithm that is trained using gradient descent usually averages gradients across examples. the thesis is that by averaging gradients, information is lost. the method posits that in a gradient descent algorithm, instead of an arithmetic ... | SP:7db5acb622d42134949e6aa08fbcf0c4d40bf83c |
Learning explanations that are hard to vary | 1 INTRODUCTION Consider the top of Figure 1 , which shows a view from above of the loss surface obtained as we vary a two dimensional parameter vector θ “ pθ1 , θ2q , for a fictional dataset containing two observations xA and xB . Note the two global minima on the top-right and bottom-left . Depending on the initial va... | The authors introduce and formalize the concept of Invariant Learning Consistency (ICL), which is motivated by the idea that "good explanations are hard to vary" in the context of deep learning. Instead of using the arithmetic mean to pool gradients (logical OR), the authors propose to use the element-wise geometric me... | SP:7db5acb622d42134949e6aa08fbcf0c4d40bf83c |
The large learning rate phase of deep learning | The choice of initial learning rate can have a profound effect on the performance of deep networks . We present empirical evidence that networks exhibit sharply distinct behaviors at small and large learning rates . In the small learning rate phase , training can be understood using the existing theory of infinitely wi... | This paper analyzes the effect of choosing a large step-size on the generalization of deep networks. They suggest that starting with a large learning rate, the loss initially increases before converging to a flatter minimum with improved generalization (catapult effect). When the learning rate is above a certain thresh... | SP:d76bc6a749e9576dade07060b012715c7b96f534 |
The large learning rate phase of deep learning | The choice of initial learning rate can have a profound effect on the performance of deep networks . We present empirical evidence that networks exhibit sharply distinct behaviors at small and large learning rates . In the small learning rate phase , training can be understood using the existing theory of infinitely wi... | The paper is a detailed account of how large of a learning rate a given mode can take when it is trained by constant step size SGD. Many papers investigated the effect of learning rate (and batch size and width etc…) to the final accuracy. In general, the findings indicate increased accuracy up to a certain threshold (... | SP:d76bc6a749e9576dade07060b012715c7b96f534 |
BREEDS: Benchmarks for Subpopulation Shift | 1 INTRODUCTION . Robustness to distribution shift has been the focus of a long line of work in machine learning ( Schlimmer & Granger , 1986 ; Widmer & Kubat , 1993 ; Kelly et al. , 1999 ; Shimodaira , 2000 ; Sugiyama et al. , 2007 ; Quionero-Candela et al. , 2009 ; Moreno-Torres et al. , 2012 ; Sugiyama & Kawanabe , 2... | The authors develop a framework named BREEDS for studying population shift, putting it in their words, they address the problem of how well do models generalize to data subpopulations they have seen during training, in the specific domain of images, without altering the inputs or requiring new data. They propose to cre... | SP:ed3aac329a61708dcf1038eb390b0a2c5216bc46 |
BREEDS: Benchmarks for Subpopulation Shift | 1 INTRODUCTION . Robustness to distribution shift has been the focus of a long line of work in machine learning ( Schlimmer & Granger , 1986 ; Widmer & Kubat , 1993 ; Kelly et al. , 1999 ; Shimodaira , 2000 ; Sugiyama et al. , 2007 ; Quionero-Candela et al. , 2009 ; Moreno-Torres et al. , 2012 ; Sugiyama & Kawanabe , 2... | This paper addresses the problem of model robustness to subpopulation shift. Authors propose building large-scale subpopulation shift benchmarks wherein the data subpopulations present during model training and evaluation differ. In this regard, their approach is based on leveraging existing dataset labels and use them... | SP:ed3aac329a61708dcf1038eb390b0a2c5216bc46 |
Untangle: Critiquing Disentangled Recommendations | 1 Introduction . Figure 1 : Untangle model is trained in two phases : Disentangling phase : Input to encoder is a one hot representation of an item ( green dotted line ) . Obtained representation is disentangled across A attributes . Recommendation phase : Input to encoder is the items user interacted with ( solid red ... | The paper proposes a framework to learn disentangled representations for collaborative filtering systems. To model the user-item interactions, the authors adopt the likelihood model proposed by \beta-Multi-VAE. The auxiliary task of predicting item labels is considered to increase the disentanglement and the ability to... | SP:3e632e5be2e41e5bb76f85ab673c8530d3e9a169 |
Untangle: Critiquing Disentangled Recommendations | 1 Introduction . Figure 1 : Untangle model is trained in two phases : Disentangling phase : Input to encoder is a one hot representation of an item ( green dotted line ) . Obtained representation is disentangled across A attributes . Recommendation phase : Input to encoder is the items user interacted with ( solid red ... | The submission aims to develop a disentangled recommender that explicitly grants the users the ability to control a chosen aspect of the recommendation list. This is achieved by (1) learning disentangled user/item representations via beta-VAE and (2) aligning text tags with certain dimensions via an auxiliary loss (the... | SP:3e632e5be2e41e5bb76f85ab673c8530d3e9a169 |
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding | 1 INTRODUCTION . Data augmentation approaches have successfully improved large-scale neural-network-based models , ( Laine & Aila , 2017 ; Xie et al. , 2019 ; Berthelot et al. , 2019 ; Sohn et al. , 2020 ; He et al. , 2020 ; Khosla et al. , 2020 ; Chen et al. , 2020b ) , however , the majority of existing research is g... | The augmentation of NLP samples is an important task with no clear "applicable to all" mechanism. This is in sharp contrast to computer vision where techniques like rotation, modification of hue, saturation as well as umpteen other techniques exist. This work tries to address the issue by proposing a technique that car... | SP:da2bdc7b32660092811416826572a6982cfc6e2c |
CoDA: Contrast-enhanced and Diversity-promoting Data Augmentation for Natural Language Understanding | 1 INTRODUCTION . Data augmentation approaches have successfully improved large-scale neural-network-based models , ( Laine & Aila , 2017 ; Xie et al. , 2019 ; Berthelot et al. , 2019 ; Sohn et al. , 2020 ; He et al. , 2020 ; Khosla et al. , 2020 ; Chen et al. , 2020b ) , however , the majority of existing research is g... | Paper proposes a contrastive learning-based approach to combine different data augmentation techniques for NLP tasks. While the widely used consistency loss focuses on a single example, the proposed contrastive objective allows capturing the relationships among all data samples which helps in producing diverse and info... | SP:da2bdc7b32660092811416826572a6982cfc6e2c |
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction | 1 INTRODUCTION . Deep neural networks ( DNNs ) have been achieving state-of-the-art results in a large variety of complex problems . These include automated decision making and recommendation systems in the medical domain ( Razzak et al. , 2018 ) , autonomous control of drones ( Kaufmann et al. , 2018 ) and self drivin... | This paper proposes a new objective function for training regression networks with prediction intervals. The goal is to provide tight confidence bounds to accompany predictions, which is of course important for practical deployments of ML systems where uncertainty quantification is critical. Previous work has largely a... | SP:0423364bd06f7fe3ef17328f83a5eb99db6459af |
PIVEN: A Deep Neural Network for Prediction Intervals with Specific Value Prediction | 1 INTRODUCTION . Deep neural networks ( DNNs ) have been achieving state-of-the-art results in a large variety of complex problems . These include automated decision making and recommendation systems in the medical domain ( Razzak et al. , 2018 ) , autonomous control of drones ( Kaufmann et al. , 2018 ) and self drivin... | The submission considers the continuous real-valued regression problems and how to obtain accurate point predictions (specific value prediction in the text) and prediction intervals (the uncertainty of the predictions, given by [lower bound LB, upper bound UB]). The paper proposes a loss function which is the weighted ... | SP:0423364bd06f7fe3ef17328f83a5eb99db6459af |
Differentiate Everything with a Reversible Domain-Specific Language | 1 Introduction Most of the popular automatic differentiation ( AD ) tools in the market , such as TensorFlow ( Abadi et al. , 2015 ) , Pytorch ( Paszke et al. , 2017 ) , and Flux ( Innes et al. , 2018 ) implements reverse mode AD at the tensor level to meet the need in machine learning . Later , People in the scientifi... | - This paper presents a new approach to automatic differentiation (AD), namely the use of reversible programming to achieve memory-efficient function inverse and adjoint. The authors have done a good job reviewing the background and laying out the motivation for the new apporach. The implementation is based on adding a... | SP:2fdd3bcbf3e2c79fc91ccc96527d635ed96ecc9b |
Differentiate Everything with a Reversible Domain-Specific Language | 1 Introduction Most of the popular automatic differentiation ( AD ) tools in the market , such as TensorFlow ( Abadi et al. , 2015 ) , Pytorch ( Paszke et al. , 2017 ) , and Flux ( Innes et al. , 2018 ) implements reverse mode AD at the tensor level to meet the need in machine learning . Later , People in the scientifi... | The paper adapts reversible computing techniques to compute gradients. The techniques presented are not new though the Julia based DSL is new. The results presented are for differentiating through a GMM. It is not clear if the technique scale to a modern day neural network models and how they will integrate into curren... | SP:2fdd3bcbf3e2c79fc91ccc96527d635ed96ecc9b |
On the Robustness of Sentiment Analysis for Stock Price Forecasting | 1 INTRODUCTION . Research on the vulnerability of machine learning ( ML ) to adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2013 ) focused , with few exceptions ( Kurakin et al. , 2016 ; Brown et al. , 2017 ) , on adversaries with immediate control over the inputs to an ML model . Yet , ML systems are o... | In this paper, the authors studied the problem of adversarial ML in stock price forecasting. They first replicated an industry standard pipeline, which performs a sentiment analysis of Twitter data to forecast trends in stock prices. Then, they show that an adversary can exploit the lack of provenance to indirectly use... | SP:6a909e6ca1ea605f986c0bd229c852d535270af6 |
On the Robustness of Sentiment Analysis for Stock Price Forecasting | 1 INTRODUCTION . Research on the vulnerability of machine learning ( ML ) to adversarial examples ( Biggio et al. , 2013 ; Szegedy et al. , 2013 ) focused , with few exceptions ( Kurakin et al. , 2016 ; Brown et al. , 2017 ) , on adversaries with immediate control over the inputs to an ML model . Yet , ML systems are o... | The paper studies the impact of adversarial attacks on a ML based system for forecasting stock prices. The authors leverage Twitter data in order to enhance stock price prediction. Then, by determining the sensitivity of the model when perturbing the inputs. Then, small changes are applied to the inputs and output is o... | SP:6a909e6ca1ea605f986c0bd229c852d535270af6 |
Multi-Head Attention: Collaborate Instead of Concatenate | 1 INTRODUCTION . Since the invention of attention ( Bahdanau et al. , 2014 ) and its popularization in the transformer architecture ( Vaswani et al. , 2017 ) , multi-head attention ( MHA ) has become the de facto architecture for natural language understanding tasks ( Devlin et al. , 2019 ) and neural machine translati... | This paper analyzes the multi-head attention in transformers and suggests to use collaboration instead of concatenation of multiple heads. Empirical results on WMT’16 English-German demonstrates that the proposed approach reduces the of parameters without sacrificing performance. Further experiments on pre-trained BERT... | SP:ee20fb0517e11ddb87a713434d3bd8f29812a521 |
Multi-Head Attention: Collaborate Instead of Concatenate | 1 INTRODUCTION . Since the invention of attention ( Bahdanau et al. , 2014 ) and its popularization in the transformer architecture ( Vaswani et al. , 2017 ) , multi-head attention ( MHA ) has become the de facto architecture for natural language understanding tasks ( Devlin et al. , 2019 ) and neural machine translati... | The paper investigates the over-parameterization of attention heads in Transformer’s multi-head attention. The authors show that query-key projections are redundant because trained concatenated heads tend to compute their attention patterns on common features. They propose a reparameterization of multi-head attention a... | SP:ee20fb0517e11ddb87a713434d3bd8f29812a521 |
Truthful Self-Play | 1 INTRODUCTION . Evolving culture prevents deep neural networks from falling into bad local optima ( Bengio , 2012 ) . For example , self-play ( Samuel , 1967 ; Tesauro , 1995 ) has not only demonstrated the ability to abstract high-dimensional state spaces as typified by AlphaGo ( Silver et al. , 2017 ) , but also imp... | This work presents an auxiliary loss that promotes truthfulness in non-cooperative multi-agent games with communication channels. The truthfulness is promoted through prediction rewards, which capture how well each agent can model other agents' policies via observing their messages. For this mechanism to work, it assum... | SP:631bfb07c83d98b09d95e8ad06969f8ecbe936e9 |
Truthful Self-Play | 1 INTRODUCTION . Evolving culture prevents deep neural networks from falling into bad local optima ( Bengio , 2012 ) . For example , self-play ( Samuel , 1967 ; Tesauro , 1995 ) has not only demonstrated the ability to abstract high-dimensional state spaces as typified by AlphaGo ( Silver et al. , 2017 ) , but also imp... | This paper examines the setting of partially observable stochastic games where agents have the possibility of communication. The approach taken is what would be termed “direct revelation” in mechanism design: agents are supposed to reveal their full internal state / history to each other at each step. This gives the ... | SP:631bfb07c83d98b09d95e8ad06969f8ecbe936e9 |
Assisting the Adversary to Improve GAN Training | 1 INTRODUCTION . The generative adversarial network ( GAN ) framework ( Goodfellow et al. , 2014 ) trains a neural network known as a generator which maps from a random vector to an output such as an image . Key to training is another neural network , the adversary ( sometimes called a discriminator or critic ) , which... | This paper concerns how to efficiently regularize the generatior for training generative adversarial networks (GANs). A new regularizer for the generator loss is proposed to penalize the norm of the gradient with respect to discriminator’s parameters ($\phi$). In other words, the generator learns to encourage small nor... | SP:4e822c1ba32ac54bfc6edf160d684c8fb84bdc36 |
Assisting the Adversary to Improve GAN Training | 1 INTRODUCTION . The generative adversarial network ( GAN ) framework ( Goodfellow et al. , 2014 ) trains a neural network known as a generator which maps from a random vector to an output such as an image . Key to training is another neural network , the adversary ( sometimes called a discriminator or critic ) , which... | This paper proposes a new regularizer to improve GAN training. By noticing that the discriminator does not always reach optimum at each iteration, this paper proposes Adversary's Assistant (AdvAs) for helping the discriminator to satisfy this condition. Interestingly, compared to the previous methods for improving GAN ... | SP:4e822c1ba32ac54bfc6edf160d684c8fb84bdc36 |
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms | 1 INTRODUCTION . With the development of technology for data acquisition and storage , the modern dataset has a larger scale , more complex structure , and more practical considerations , which addresses new challenges for data analysis . In recent years , large quantities of sensitive data are collected by individuals... | The paper gives "almost private" algorithms for problem of sign recovery of mean vector and of linear regression. The techniques follow their general framework of Median DC, which is similar to the well-known median-of-means approach. They give theoretical guarantees for the same, along with empirical results comparing... | SP:adad16c183ae1c2806036fef3ae2f6038943ee33 |
Median DC for Sign Recovery: Privacy can be Achieved by Deterministic Algorithms | 1 INTRODUCTION . With the development of technology for data acquisition and storage , the modern dataset has a larger scale , more complex structure , and more practical considerations , which addresses new challenges for data analysis . In recent years , large quantities of sensitive data are collected by individuals... | The paper considers the sign recovery problem in a distributed setting with privacy constraints. The paper proposes an algorithm “median divide-and-conquer (Med-DC)” which takes the sign locally in each machine and then takes the median globally. The paper shows that in the sparse mean estimation setting, Med-DC is cor... | SP:adad16c183ae1c2806036fef3ae2f6038943ee33 |
Implicit Normalizing Flows | 1 INTRODUCTION . Normalizing flows ( NFs ) ( Rezende & Mohamed , 2015 ; Dinh et al. , 2014 ) are promising methods for density modeling . NFs define a model distribution px ( x ) by specifying an invertible transformation f ( x ) from x to another random variable z . By change-of-variable formula , the model density is... | The authors concerns the question of how expressive invertible functions can be constructed. Their ansatz is the defining an invertible layer implicitly, using the root of an equation. While this approach is more general, they employ residual flows (ResFlows) to formulate a particular realisation of such an equation, c... | SP:b9d29e5258471d8fbd0b73e3bb05ed3cdfc68d6a |
Implicit Normalizing Flows | 1 INTRODUCTION . Normalizing flows ( NFs ) ( Rezende & Mohamed , 2015 ; Dinh et al. , 2014 ) are promising methods for density modeling . NFs define a model distribution px ( x ) by specifying an invertible transformation f ( x ) from x to another random variable z . By change-of-variable formula , the model density is... | This work is about a new architecture for normalizing flows inspired by implicit neural networks. In particular, the authors show that a specific implicit neural network build from residual blocks defines a bijective map. From this insight, they show how to efficiently train such architecture by estimating the log Jaco... | SP:b9d29e5258471d8fbd0b73e3bb05ed3cdfc68d6a |
Learning A Minimax Optimizer: A Pilot Study | 1 INTRODUCTION . Many popular applications can be formulated into solving continuous minimax optimization , such as generative adversarial networks ( GAN ) ( Goodfellow et al. , 2014 ) , distributionally robust learning ( Globerson & Roweis , 2006 ) , domain adaptation ( Ganin & Lempitsky , 2014 ) , distributed computi... | The paper introduces the _learning to optimize_ (L2O) framework into the solution of minimax problems. The base model is composed of two decoupled LSTMs with a shared objective, with the two LSTMs being respectively responsible for the update of the min and max variables. On top of this, the authors further investigate... | SP:42ff146511e450b30afabdbb8396c6db640d05fa |
Learning A Minimax Optimizer: A Pilot Study | 1 INTRODUCTION . Many popular applications can be formulated into solving continuous minimax optimization , such as generative adversarial networks ( GAN ) ( Goodfellow et al. , 2014 ) , distributionally robust learning ( Globerson & Roweis , 2006 ) , domain adaptation ( Ganin & Lempitsky , 2014 ) , distributed computi... | Classical iterative minimax optimization algorithms display the unstable dynamics. Their convergence is often sensitive to the parameters and needs to be re-tuned for different problems to ensure convergence. Therefore, there is a practical motivation to develop L2O for minimax problems, so that we could meta-learn and... | SP:42ff146511e450b30afabdbb8396c6db640d05fa |
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks | Deep neural networks ( DNNs ) are known vulnerable to backdoor attacks , a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model ’ s prediction at the test time . Backdoor attacks are notably dangerous since they do not affect the model ’ s performance o... | The paper proposes a simple yet effective approach for purifying a neural network poisoned with backdoor attacks, AKA backdoor erasing. In short, the authors propose a two-step process: 1) fine-tuning the poisoned model on a small portion of clean data, which is a commonly used defense, and 2) treating the poisoned mod... | SP:c26255b9b2de0df893e12a6cf5e61ffc46640418 |
Neural Attention Distillation: Erasing Backdoor Triggers from Deep Neural Networks | Deep neural networks ( DNNs ) are known vulnerable to backdoor attacks , a training time attack that injects a trigger pattern into a small proportion of training data so as to control the model ’ s prediction at the test time . Backdoor attacks are notably dangerous since they do not affect the model ’ s performance o... | This paper proposes a novel approach to erase backdoor triggers from neural networks through distillation. The defense method, called neural attention distillation (NAD), first finetunes the backdoored model on a set of clean data to get a teacher model. The second part of NAD then finetunes another copy of the origina... | SP:c26255b9b2de0df893e12a6cf5e61ffc46640418 |
Human-Level Performance in No-Press Diplomacy via Equilibrium Search | 1 INTRODUCTION . A primary goal for AI research is to develop agents that can act optimally in real-world multi-agent interactions ( i.e. , games ) . In recent years , AI agents have achieved expert-level or even superhuman performance in benchmark games such as backgammon ( Tesauro , 1994 ) , chess ( Campbell et al. ,... | This paper proposes a combination of imitation learning and search applied to the multiplayer, simultaneous-move game of no-press Diplomacy. While both techniques have been used before, even in concert, there are some domain-specific challenges: more than two players, simultaneous moves, and a very large branching fact... | SP:ea31c76a7f4c38f29544f50159cca36fa17d15f3 |
Human-Level Performance in No-Press Diplomacy via Equilibrium Search | 1 INTRODUCTION . A primary goal for AI research is to develop agents that can act optimally in real-world multi-agent interactions ( i.e. , games ) . In recent years , AI agents have achieved expert-level or even superhuman performance in benchmark games such as backgammon ( Tesauro , 1994 ) , chess ( Campbell et al. ,... | In this paper, the authors apply an interesting twist on 1-ply search to the problem of playing no-press Diplomacy. Diplomacy is an especially interesting application, because it is neither zero-sum nor two-player, in contrast to many recent AI success stories. Before each action, they compute an equilibrium of the ne... | SP:ea31c76a7f4c38f29544f50159cca36fa17d15f3 |
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search | 1 INTRODUCTION . Neural Architecture Search ( NAS ) promises to advance representation learning by automatically finding architectures that facilitate the learning of strong representations for a given dataset . NAS has already achieved state-of-the-art performance on many tasks ( Real et al. , 2019 ; Liu et al. , 2019... | This work filled an important gap in the NAS benchmarks. The previous benchmarks only contain small search space due to the expensive cost of evaluation of neural architecture. In this search space, random search often becomes competitive in the narrow search space. Thus, to provide meaningful comparison, this work pro... | SP:59210764f21eadc8e69e720f9d12f3b85cf74ceb |
NAS-Bench-301 and the Case for Surrogate Benchmarks for Neural Architecture Search | 1 INTRODUCTION . Neural Architecture Search ( NAS ) promises to advance representation learning by automatically finding architectures that facilitate the learning of strong representations for a given dataset . NAS has already achieved state-of-the-art performance on many tasks ( Real et al. , 2019 ; Liu et al. , 2019... | The authors propose a new benchmark for evaluating surrogate functions for architecture search. According to the authors, existing tabular architecture search benchmarks are insufficient for this purpose due to using overly small search spaces. The main difference of this benchmark and other existing architecture searc... | SP:59210764f21eadc8e69e720f9d12f3b85cf74ceb |
Learning to Generate Questions by Recovering Answer-containing Sentences | 1 INTRODUCTION . Machine reading comprehension ( MRC ) , which finds the answer to a given question from its accompanying paragraphs ( called context ) , is an essential task in natural language processing . With the release of high-quality human-annotated datasets for this task , such as SQuAD-v1.1 ( Rajpurkar et al. ... | This paper proposes a pretraining technique for question-generation models. The pretraining involves generating the sentence which contains an answer candidate from the context around it. Through several well-executed experiments the paper shows that this type of pretraining can improve performance of several existing ... | SP:b7af23a88486ea212c5c3061202b5cc7c179bc68 |
Learning to Generate Questions by Recovering Answer-containing Sentences | 1 INTRODUCTION . Machine reading comprehension ( MRC ) , which finds the answer to a given question from its accompanying paragraphs ( called context ) , is an essential task in natural language processing . With the release of high-quality human-annotated datasets for this task , such as SQuAD-v1.1 ( Rajpurkar et al. ... | This paper presents a model for unsupervised pre-training for the task of question generation. The model first predicts the number of answer present in a given paragraph and then selects the top-K answer spans from the paragraph. After selecting the answer spans, the model, then tries to generate the answer containing ... | SP:b7af23a88486ea212c5c3061202b5cc7c179bc68 |
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints | 1 INTRODUCTION . There has been tremendous recent progress in designing deep generative models for generating sequence data such as natural language ( Vaswani et al. , 2017 ) or music ( Huang et al. , 2019 ) . These approaches leverage the vast quantities of data available in conjunction with unsupervised and selfsuper... | This paper proposes an approach for generating sequences that possess high-level structure, and in particular structure that can be expressed using hand-crafted symbolic relations. Given a domain and a set of possible relations to consider, this approach first extracts a sequence representation of the relational constr... | SP:ca0c4bdb02f7d939fb6de38b6b446ced4b5984a0 |
Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints | 1 INTRODUCTION . There has been tremendous recent progress in designing deep generative models for generating sequence data such as natural language ( Vaswani et al. , 2017 ) or music ( Huang et al. , 2019 ) . These approaches leverage the vast quantities of data available in conjunction with unsupervised and selfsuper... | This paper proposes a neural-symbolic deep generative model to generate music and poems. Compared to other natural language sentence generation tasks, songs and poems usually require some structural constraints, e.g., rhythm and rhymes. Therefore, their approach first generates a program represented as a set of relatio... | SP:ca0c4bdb02f7d939fb6de38b6b446ced4b5984a0 |
Deep Curvature Suite | 1 INTRODUCTION . The success of deep neural networks trained with gradient based optimisers in speech and object recognition ( LeCun et al. , 2015 ) , has led to an explosion in easy to use high performance software implementations . Automatic differentiation packages such as TensorFlow ( Abadi et al. , 2016 ) and PyTo... | This paper proposes a software package to ease and provide a standard way for Hessian-related computation, both for loss analysis and second order optimization. It also provides analysis on why Lanczos algorithm is a better choice to estimate Hessian eigenvalue compared to Power Iterations. Finally, it empirically show... | SP:37bd7b6bf0f90eb24e82b831b5fd8c57c3b09142 |
Deep Curvature Suite | 1 INTRODUCTION . The success of deep neural networks trained with gradient based optimisers in speech and object recognition ( LeCun et al. , 2015 ) , has led to an explosion in easy to use high performance software implementations . Automatic differentiation packages such as TensorFlow ( Abadi et al. , 2016 ) and PyTo... | - This paper introduces a package for computing the second-order information of neural networks based on the Lanczos algorithm. The authors showcase the usages of the package with 1) visualizing the eigenspectrum of the curvature matrix; 2)visualizing the loss surface along with a specific direction, and 3) comparisons... | SP:37bd7b6bf0f90eb24e82b831b5fd8c57c3b09142 |
Multi-scale Network Architecture Search for Object Detection | 1 INTRODUCTION . Recognizing and localizing objects at vastly different scales is a fundamental challenge in object detection . Detection performance for objects with different scales is highly related to features with different properties such as feature resolution , receptive fields , and feature fusion ways . The ke... | This submission works on the task of architecture search for object detection. The authors focus on two components: how to produce multi-scale features and how to use multi-scale features. The authors formalized a simple search space, and applied an evolution-based search algorithm. Experiments show the proposed search... | SP:648c9f61876219050edc481d57891ec3465617d3 |
Multi-scale Network Architecture Search for Object Detection | 1 INTRODUCTION . Recognizing and localizing objects at vastly different scales is a fundamental challenge in object detection . Detection performance for objects with different scales is highly related to features with different properties such as feature resolution , receptive fields , and feature fusion ways . The ke... | This paper introduces a network architecture search (NAS) suitable for a feature pyramid network (FPN) that provides notable detection accuracy for objects at every scale. Based on the decomposition of FPN structure as (multi-scale) feature generation and feature utilization, the proposed NAS offers a new design strate... | SP:648c9f61876219050edc481d57891ec3465617d3 |
Prototypical Representation Learning for Relation Extraction | 1 INTRODUCTION . Relation extraction aims to predict relations between entities in sentences , which is crucial for understanding the structure of human knowledge and automatically extending knowledge bases ( Cohen & Hirsh , 1994 ; Bordes et al. , 2013 ; Zeng et al. , 2015 ; Schlichtkrull et al. , 2018 ; Shen et al. , ... | This paper presents a pre-training method for encoders (i.e., BERT) of relation extraction, leveraging distant supervision data. The main idea is to introduce a prototype embedding for each relation in the distantly generated data. The loss function to pre-train the encoders involve several terms, aiming to exploit the... | SP:e23a409a3fc7b9ac30367891a19f33934915f9a6 |
Prototypical Representation Learning for Relation Extraction | 1 INTRODUCTION . Relation extraction aims to predict relations between entities in sentences , which is crucial for understanding the structure of human knowledge and automatically extending knowledge bases ( Cohen & Hirsh , 1994 ; Bordes et al. , 2013 ; Zeng et al. , 2015 ; Schlichtkrull et al. , 2018 ; Shen et al. , ... | The authors propose a novel method for learning prototype representation for relations which abstracts the essential semantics of relations between entities in sentences. The learned prototypes are learned based on an objective with clear geometric interpretation and have been shown to be interpretable and robust to no... | SP:e23a409a3fc7b9ac30367891a19f33934915f9a6 |
GN-Transformer: Fusing AST and Source Code information in Graph Networks | 1 INTRODUCTION . Code summarization is the task of generating a readable summary that describes the functionality of a snippet . Such task requires a high-level comprehension of a source code snippet thus it is an effective task to evaluate whether a Deep Learning Model is able to capture complex relations and structur... | This paper focuses on the problem of training a neural model to understand source code. The authors argue that both graph information (such as the parsed abstract syntax tree) and sequence information (such as the raw program tokens) are useful for understanding code, and describe a particular method of adding raw prog... | SP:5c62d26db077ad23a6556481905a52e86f8ef93c |
GN-Transformer: Fusing AST and Source Code information in Graph Networks | 1 INTRODUCTION . Code summarization is the task of generating a readable summary that describes the functionality of a snippet . Such task requires a high-level comprehension of a source code snippet thus it is an effective task to evaluate whether a Deep Learning Model is able to capture complex relations and structur... | In this work, authors propose a new direction in summarizing code snippets by combining their AST and lexical code features in the form of a graph called SCG (which is shown not to be optimal). Their model, GN-Transformer, further extracts information from SCG to summarize the code snippet. This model is a combination... | SP:5c62d26db077ad23a6556481905a52e86f8ef93c |
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks | 1 INTRODUCTION . Graph Representation Learning has attracted much attention from the research communities in recent years , with emerging new work every year . Graph Convolution Neural Networks ( GCNs ) were proposed as the extension of Convolutional Neural Networks ( CNNs ) ( LeCun et al. , 1995 ) on geometric data . ... | This work proposes a new framework, called LINGUINE, to produce high-quality sub-graphs that can assist in effective training of graph convolutional networks (GCNs) with a lower computational cost. The framework uses two consecutive components - (1) Bootstrapping, which learns a meta-model that can assign weights to no... | SP:aaf87fbde816b1f09befbefc7f9198022292e03a |
LINGUINE: LearnIng to pruNe on subGraph convolUtIon NEtworks | 1 INTRODUCTION . Graph Representation Learning has attracted much attention from the research communities in recent years , with emerging new work every year . Graph Convolution Neural Networks ( GCNs ) were proposed as the extension of Convolutional Neural Networks ( CNNs ) ( LeCun et al. , 1995 ) on geometric data . ... | This paper proposes an approach to compute GNNs on pruned subgraphs. The authors use a "meta-model" to learn a good node pruning strategy during training. Then the meta-model is used to generate pruned subgraphs during inference. The proposed pruning algorithm can be applied to various graph samplers. The authors evalu... | SP:aaf87fbde816b1f09befbefc7f9198022292e03a |
Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms | We benchmark commonly used multi-agent deep reinforcement learning ( MARL ) algorithms on a variety of cooperative multi-agent games . While there has been significant innovation in MARL algorithms , algorithms tend to be tested and tuned on a single domain and their average performance across multiple domains is less ... | The major contribution of this paper is benchmarking 5 MARL algorithms on 4 cooperative multi-agent environments. Also, this paper found that under constrained hyperparameter search budgets, the multi-agent PPO algorithm has more consistent performance over the other algorithms across different tested multi-agent envir... | SP:2e305b4762d57663e3c96ae164a9cd385dfe9549 |
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