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Transformer protein language models are unsupervised structure learners | 1 INTRODUCTION . Unsupervised modeling of protein contacts has an important role in computational protein design ( Russ et al. , 2020 ; Tian et al. , 2018 ; Blazejewski et al. , 2019 ) and is a central element of all current state-of-the-art structure prediction methods ( Wang et al. , 2017 ; Senior et al. , 2020 ; Yan... | The paper performs a number of analyses centered around the ability of transformer-based language models trained on protein sequence data to learn representations useful for predicting protein secondary and tertiary structure (the latter as contact maps). Specifically, the paper studies several pre-trained transformer ... | SP:a826495e7d92c3cd68a71fc4961c296fec0307ed |
Guiding Representation Learning in Deep Generative Models with Policy Gradients | 1 INTRODCTION . Reinforcement Learning ( RL ) gained much popularity in recent years by outperforming humans in games such as Atari ( Mnih et al . ( 2015 ) ) , Go ( Silver et al . ( 2016 ) ) and Starcraft 2 ( Vinyals et al . ( 2017 ) ) . These results were facilitated by combining novel machine learning techniques such... | This paper proposes a method for reinforcement learning with representations learned from a VAE. The VAE is used to encode the states (images) and the mean of the posterior is used as input to the policy. The VAE is trained using the variational lower bound and the policy is optimized using PPO. The model can be jointl... | SP:8a9e9fec36d06b122a226ccac91a869963b148b2 |
Guiding Representation Learning in Deep Generative Models with Policy Gradients | 1 INTRODCTION . Reinforcement Learning ( RL ) gained much popularity in recent years by outperforming humans in games such as Atari ( Mnih et al . ( 2015 ) ) , Go ( Silver et al . ( 2016 ) ) and Starcraft 2 ( Vinyals et al . ( 2017 ) ) . These results were facilitated by combining novel machine learning techniques such... | The paper focused on the issue of learning a policy for a given task using the learned representations a pre-trained VAE. The authors visualize that using a learned latent space of a pre-trained VAE is not good enough for learning policies and propose a solution for this problem: back-propagate gradient policies throug... | SP:8a9e9fec36d06b122a226ccac91a869963b148b2 |
Benefits of Assistance over Reward Learning | 1 INTRODUCTION . Traditional computer programs are instructions on how to perform a particular task . However , we do not know how to mechanically perform more challenging tasks like translation . The field of artificial intelligence raises the level of abstraction so that we simply specify what the task is , and let t... | The submission provides a survey of two paradigms for ‘agents learning from human feedback.’ The two paradigms are unified under a new formalism (assistance games), which subsumes them as its special cases. Further, a taxonomy of different problems resulting from the formalism is provided (communicative games, two-phas... | SP:fc8a52afd27fff291c1fe55d196aa54a759dd42e |
Benefits of Assistance over Reward Learning | 1 INTRODUCTION . Traditional computer programs are instructions on how to perform a particular task . However , we do not know how to mechanically perform more challenging tasks like translation . The field of artificial intelligence raises the level of abstraction so that we simply specify what the task is , and let t... | This work proposes learning a single control policy for human-in-the-loop learning rather than having a reward learning component and a control component. The key difference is that the action selection can use information from the reward learning module. The authors formulate an assistance game in this setting and sho... | SP:fc8a52afd27fff291c1fe55d196aa54a759dd42e |
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling | 1 INTRODUCTION . The performance of supervised machine learning ( ML ) hinges on the availability of labeled data in sufficient quantity and quality . However , labeled data for applications of ML can be scarce , and the common process of obtaining labels by having annotators inspect individual samples is often expensi... | This paper proposes a new approach for active learning by interactively discovering weak supervision. Instead of asking human to annotate data points, the method collects feedback about candidate label functions, from which a model learns to identify promising label functions. With the final set of label functions, the... | SP:a0d07d2ab41a2c13a2be8f2fb99548828d6ae991 |
Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling | 1 INTRODUCTION . The performance of supervised machine learning ( ML ) hinges on the availability of labeled data in sufficient quantity and quality . However , labeled data for applications of ML can be scarce , and the common process of obtaining labels by having annotators inspect individual samples is often expensi... | This paper proposes a new framework for interactively selecting labeling heuristics in a weakly supervised setting. The main idea of the proposed approach is to combine weak supervision and active learning. Compared to the previous work which relies on human manually create labeling functions (the abstraction of the we... | SP:a0d07d2ab41a2c13a2be8f2fb99548828d6ae991 |
Learning-based Support Estimation in Sublinear Time | log ( 1/ε ) · n1−Θ ( 1/ log ( 1/ε ) ) . We evaluate the proposed algorithms on a collection of data sets , using the neuralnetwork based estimators from Hsu et al , ICLR ’ 19 as predictors . Our experiments demonstrate substantial ( up to 3x ) improvements in the estimation accuracy compared to the state of the art alg... | This paper considers the support size estimation problem using a random sample from the unknown distribution and access to some predictor of the element frequency. Under that setting, the paper improves the estimator by Wu & Yang (2019) by refining the approximation interval promised by the predicted frequency. A theor... | SP:83c6819a4c6458305ec079213fb6fb3ffdcfdcb8 |
Learning-based Support Estimation in Sublinear Time | log ( 1/ε ) · n1−Θ ( 1/ log ( 1/ε ) ) . We evaluate the proposed algorithms on a collection of data sets , using the neuralnetwork based estimators from Hsu et al , ICLR ’ 19 as predictors . Our experiments demonstrate substantial ( up to 3x ) improvements in the estimation accuracy compared to the state of the art alg... | Estimation of the size of the support of a distribution over a discreet domain is a fundamental problem. In the standard setting, this problem is theoretically well-understood with matching upper and lower bounds. The authors assume additional access to a constant approximation of the density function at each point, an... | SP:83c6819a4c6458305ec079213fb6fb3ffdcfdcb8 |
Federated Learning's Blessing: FedAvg has Linear Speedup | 1 INTRODUCTION . Federated learning ( FL ) is a machine learning paradigm where many clients ( e.g. , mobile devices or organizations ) collaboratively train a model under the orchestration of a central server ( e.g. , service provider ) , while keeping the training data decentralized ( Smith et al . ( 2017 ) ; Kairouz... | This paper shows a linear speedup in FedAvg w.r.t. number of devices, mainly theoretically, while most prior works ignore it. The main convergence results are given for three cases: a) Strongly Convex+Smooth, b) Convex+Smoth, and c) Strongly/x convex+Smooth+0 training loss can be achieved. The paper is well-written and... | SP:ceb502f2595c97afdce83fd3a98bcacbdb98e5c5 |
Federated Learning's Blessing: FedAvg has Linear Speedup | 1 INTRODUCTION . Federated learning ( FL ) is a machine learning paradigm where many clients ( e.g. , mobile devices or organizations ) collaboratively train a model under the orchestration of a central server ( e.g. , service provider ) , while keeping the training data decentralized ( Smith et al . ( 2017 ) ; Kairouz... | This paper gives convergence analysis for FedAvg and its accelerated version under data heterogeneity and system heterogeneity. The main improvement comes from a more careful analysis for one-step descent where the authors make use of the term $\alpha_{t} \sum_{k=1}^{N} p_{k}\left[F_{k}\left(\mathbf{w}^{*}\right)-F_{k}... | SP:ceb502f2595c97afdce83fd3a98bcacbdb98e5c5 |
Error Controlled Actor-Critic Method to Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms are combined with function approximation methods to adapt to the application scenarios whose state spaces are combinatorial , large , or even continuous . Many function approximation methods RL methods , including the Fourier basis ( Konidaris et al. , 2011 ) , ... | In this paper, the authors study the error introduced by the estimation of critic function in the Actor-Critic algorithm. Then the author proposed an algorithm that utilizes the idea of double Q learning and using a KL-divergence like regularization method to control this error. Experimentally the proposed algorithm ac... | SP:343ef3ab797100bbd2d8bc91a6fe9d05a67a897a |
Error Controlled Actor-Critic Method to Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithms are combined with function approximation methods to adapt to the application scenarios whose state spaces are combinatorial , large , or even continuous . Many function approximation methods RL methods , including the Fourier basis ( Konidaris et al. , 2011 ) , ... | Authors investigated the effect of approximation error for actor-critic. They derived an upper bound of approximation showing that minimizing the KL divergence between the two consecutive policies can drive this upper bound down. Based on their finding they introduced the Error Controlled Actor-critic (ECAC) algorithm... | SP:343ef3ab797100bbd2d8bc91a6fe9d05a67a897a |
Open-world Semi-supervised Learning | 1 INTRODUCTION . With the advent of deep learning , remarkable breakthroughs have been achieved and current machine learning systems excel on tasks with large quantities of labeled data ( Hinton et al. , 2012 ; LeCun et al. , 2015 ; Silver et al. , 2016 ; Esteva et al. , 2017 ) . Despite the strengths , the vast majori... | The paper considers open-world SSL settings where the model recognizes previously seen classes, and detects novel classes which are not present in the labeled dataset. The method contains three losses to train a model in this setting: a) supervised loss on labeled data, b) unsupervised loss on unlabeled data from pseu... | SP:281bc59d639aa76d84921b3ec4ce1ee8f1ba5b51 |
Open-world Semi-supervised Learning | 1 INTRODUCTION . With the advent of deep learning , remarkable breakthroughs have been achieved and current machine learning systems excel on tasks with large quantities of labeled data ( Hinton et al. , 2012 ; LeCun et al. , 2015 ; Silver et al. , 2016 ; Esteva et al. , 2017 ) . Despite the strengths , the vast majori... | The authors propose a method to tackle a new problem setting of semi-supervised learning, called an open-world semi-supervised learning, where the model is required to accurately discriminate known-class data as well as to appropriately discover unknown classes contained in an unlabeled dataset. The objective function ... | SP:281bc59d639aa76d84921b3ec4ce1ee8f1ba5b51 |
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach | 1 INTRODUCTION . Deep neural networks ( DNNs ) have gained popularity in a wide range of applications . The remarkable success of DNNs often relies on the availability of large-scale datasets . However , data annotation inevitably introduces label noise , and it is extremely expensive and time-consuming to clean up the... | The paper introduces a noise-robust loss function CORES2, motivated by peer loss. The novel loss adds a regularization term that promotes confident prediction and pushes the model prediction away from the prior of the label. Using this loss function, the authors propose a dynamic sample sieve to separate the clean data... | SP:b4e5b4a3546fdec14a958bbe0d387bce946396b0 |
Learning with Instance-Dependent Label Noise: A Sample Sieve Approach | 1 INTRODUCTION . Deep neural networks ( DNNs ) have gained popularity in a wide range of applications . The remarkable success of DNNs often relies on the availability of large-scale datasets . However , data annotation inevitably introduces label noise , and it is extremely expensive and time-consuming to clean up the... | The authors of the paper propose a new method, the CORES (COnfidence REgularized Sample Sieve), to tackle the important problem of learning under instance dependent label noise. The proposed method, in essence, involves the use of a confidence regularization term that encourages more confident predictions and a sieving... | SP:b4e5b4a3546fdec14a958bbe0d387bce946396b0 |
Cross-Modal Retrieval Augmentation for Multi-Modal Classification | 1 INTRODUCTION . Neural networks augmented with non-parametric retrieval components have recently shown impressive results in NLP ( Khandelwal et al. , 2019 ; Guu et al. , 2020 ; Lewis et al. , 2020 ; Izacard & Grave , 2020 ) . In this work , we train a state-of-the-art image-caption alignment model and utilize it in v... | This paper explores a new direction, to utilize the searched results (image caption pair) to improve downstream multimodal learning tasks. They first pre-trained a cross-modal model using the contrastive learning on the image caption dataset. Then they use the pre-trained model to search the relevant terms for image or... | SP:fe52a638fdb309b8fcb1232b0f23e08c96965721 |
Cross-Modal Retrieval Augmentation for Multi-Modal Classification | 1 INTRODUCTION . Neural networks augmented with non-parametric retrieval components have recently shown impressive results in NLP ( Khandelwal et al. , 2019 ; Guu et al. , 2020 ; Lewis et al. , 2020 ; Izacard & Grave , 2020 ) . In this work , we train a state-of-the-art image-caption alignment model and utilize it in v... | This paper proposed a cross-modal retrieval augmentation for the multi-modal classification task (VQA). The authors first introduce a transformer-based image caption retrieval architecture that achieves decent performance. Then, the authors proposed to use the retrieval model to retrieve relevant visual and textual inf... | SP:fe52a638fdb309b8fcb1232b0f23e08c96965721 |
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-Domain Mixup | 1 INTRODUCTION . Performance of deep learning algorithms in real-world applications is often limited by the size of training datasets . Training a deep neural network ( DNN ) model with a small number of training samples usually leads to the over-fitting issue with poor generalization performance . A common yet effecti... | This paper proposes XMixup, a strategy for improving transfer learning in neural networks. Specifically, XMixup consists of mixup applied between target samples and source samples from the class pre-determined to be closest to target sample’s class. Experiments conducting transfer learning from pre-trained ImageNet to ... | SP:7cef694906438e793f2303852173109b603e0dd5 |
XMixup: Efficient Transfer Learning with Auxiliary Samples by Cross-Domain Mixup | 1 INTRODUCTION . Performance of deep learning algorithms in real-world applications is often limited by the size of training datasets . Training a deep neural network ( DNN ) model with a small number of training samples usually leads to the over-fitting issue with poor generalization performance . A common yet effecti... | This paper proposes a simple variant for the mixup training mechanism for transfer learning problems: cross-domain mixup (XMixup). The key idea is to mix up the training samples from both domains where the samples are generated by nearest-center assignment in each class. Experiments on several datasets have shown its e... | SP:7cef694906438e793f2303852173109b603e0dd5 |
Autoencoder Image Interpolation by Shaping the Latent Space | Autoencoders represent an effective approach for computing the underlying factors characterizing datasets of different types . The latent representation of autoencoders have been studied in the context of enabling interpolation between data points by decoding convex combinations of latent vectors . This interpolation ,... | This paper introduces several autoencoder (AE) regularization terms that aim at reproducing continuous realistic deformation by interpolating latent codes of images. The authors assume there is a continuous process generating the data and introduce three novel loss terms (in addition to the standard AE reconstruction l... | SP:924990a4586c7570f0b6a9d4f58d94ad8f4f5cc4 |
Autoencoder Image Interpolation by Shaping the Latent Space | Autoencoders represent an effective approach for computing the underlying factors characterizing datasets of different types . The latent representation of autoencoders have been studied in the context of enabling interpolation between data points by decoding convex combinations of latent vectors . This interpolation ,... | This paper focused on developing a new regularization technique for autoencoders, which shapes the latent representation to follow a manifold that is consistent with the training images and that drives the manifold to be smooth and locally convex. The authors suggest that the manifold structure of continuous data must ... | SP:924990a4586c7570f0b6a9d4f58d94ad8f4f5cc4 |
Sharpness-aware Minimization for Efficiently Improving Generalization | 1 INTRODUCTION . Modern machine learning ’ s success in achieving ever better performance on a wide range of tasks has relied in significant part on ever heavier overparameterization , in conjunction with developing ever more effective training algorithms that are able to find parameters that generalize well . Indeed ,... | Motivated by the connection between the flatness of minima and its generalization ability, the authors propose Sharpness-aware Minimization (SAM), which explicitly minimizes both loss value and loss sharpness during training deep neural networks. They find SAM improves generalization for a range of image classification... | SP:4575567f743dfddde8d82d911115cf806f78042f |
Sharpness-aware Minimization for Efficiently Improving Generalization | 1 INTRODUCTION . Modern machine learning ’ s success in achieving ever better performance on a wide range of tasks has relied in significant part on ever heavier overparameterization , in conjunction with developing ever more effective training algorithms that are able to find parameters that generalize well . Indeed ,... | This paper proposes and empirically evaluates SAM, an optimization method that is designed to seek out regions of uniformly low training loss. The method is derived from a bound on the generalization performance of parameters $w$ in terms of the maximal training loss in a region around $w$. After various approximations... | SP:4575567f743dfddde8d82d911115cf806f78042f |
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams | 1 INTRODUCTION . The prevalence of data streams in contemporary applications urges systems to learn in a continual fashion . Autonomous vehicles , sensory robot data , and video streaming yield never-ending streams of data , with abrupt changes in the observed environment behind every vehicle turn , robot entering a ne... | This article introduces a learner-evaluator framework that incorporates the different variations of problems related to incremental learning. It also proposes a method called Continual Prototype Evolution for dealing with the most general version of the problem, incremental learning on data streams, in which the learni... | SP:4045aeb245ca2e1341b85397e81090d9f99217cf |
Continual Prototype Evolution: Learning Online from Non-Stationary Data Streams | 1 INTRODUCTION . The prevalence of data streams in contemporary applications urges systems to learn in a continual fashion . Autonomous vehicles , sensory robot data , and video streaming yield never-ending streams of data , with abrupt changes in the observed environment behind every vehicle turn , robot entering a ne... | This paper covers an interesting topic of continual learning of the stream of data. One limitation of the existing classification algorithms is their close-set assumption. In close-set methods, a predefined set of classes are considered and a model is trained on the available data from these classes, based on the assum... | SP:4045aeb245ca2e1341b85397e81090d9f99217cf |
Empirically Verifying Hypotheses Using Reinforcement Learning | This paper formulates hypothesis verification as an RL problem . Specifically , we aim to build an agent that , given a hypothesis about the dynamics of the world , can take actions to generate observations which can help predict whether the hypothesis is true or false . Existing RL algorithms fail to solve this task ,... | This paper introduces a problem setting where an RL agent must interact with its environment to predict whether a given hypothesis is true or false. On modified versions of environments like gridworld and cartpole, they show that PPO with a sparse reward is unable to correctly test the hypothesis. The key technical con... | SP:457e8a56292b7c3001e0bae17c86b978ab95bcce |
Empirically Verifying Hypotheses Using Reinforcement Learning | This paper formulates hypothesis verification as an RL problem . Specifically , we aim to build an agent that , given a hypothesis about the dynamics of the world , can take actions to generate observations which can help predict whether the hypothesis is true or false . Existing RL algorithms fail to solve this task ,... | This paper considers the general problem of testing hypotheses about the world by a kind of reinforcement learning, much as a person might learn by taking actions and observing their outcomes -- equivalently learning policies that can generate observations to validate a hypothesis. A hypothesis is a symbolic represen... | SP:457e8a56292b7c3001e0bae17c86b978ab95bcce |
Learning with Feature-Dependent Label Noise: A Progressive Approach | 1 INTRODUCTION . Addressing noise in training set labels is an important problem in supervised learning . Incorrect annotation of data is inevitable in large-scale data collection , due to intrinsic ambiguity of data/class and mistakes of human/automatic annotators ( Yan et al. , 2014 ; Andreas et al. , 2017 ) . Develo... | The paper presents a learning method for the scenario of feature dependent label noise. A framework where label noise diminishes away from the decision boundary is established and a relabeling strategy based on this by relabeling highly confident points is proposed. The method is a straight-forward adaptive method whi... | SP:470d98d23a746a65b18404aaabf1a15d34fc24fa |
Learning with Feature-Dependent Label Noise: A Progressive Approach | 1 INTRODUCTION . Addressing noise in training set labels is an important problem in supervised learning . Incorrect annotation of data is inevitable in large-scale data collection , due to intrinsic ambiguity of data/class and mistakes of human/automatic annotators ( Yan et al. , 2014 ; Andreas et al. , 2017 ) . Develo... | Label noise is very frequently in many real world applications. However, the noise can be with different distributions. If we build the learning model under a certain distribution, it is difficult to capture the discriminative information. In this paper, without assuming that the noise is a certain distribution, the pr... | SP:470d98d23a746a65b18404aaabf1a15d34fc24fa |
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation | 1 INTRODUCTION . The density ratio estimation ( DRE ) problem has attracted a great deal of attention as an essential task in data science for its various industrial applications , such as domain adaptation ( Shimodaira , 2000 ; Plank et al. , 2014 ; Reddi et al. , 2015 ) , learning with noisy labels ( Liu & Tao , 2014... | The paper studies density ratio estimation (DRE), addressing the 'train-loss hacking' problems which often arise and hamper estimation when models are too flexible. The authors propose a new risk estimator for DRE, providing a non-negative Bregman divergence estimator, with the non-negative correction. Theoretical anal... | SP:aeb3da5e74ad99557ef60627ab355b6402a88e77 |
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio Estimation | 1 INTRODUCTION . The density ratio estimation ( DRE ) problem has attracted a great deal of attention as an essential task in data science for its various industrial applications , such as domain adaptation ( Shimodaira , 2000 ; Plank et al. , 2014 ; Reddi et al. , 2015 ) , learning with noisy labels ( Liu & Tao , 2014... | The paper addresses learning the ratio between two densities from their samples, with applications to outlier detection and covariate shift adaption. An existing approach is to minimize the Bregman (BR) divergence's empirical approximation while modeling the density ratio function $r^*$ by a flexible hypothesis family,... | SP:aeb3da5e74ad99557ef60627ab355b6402a88e77 |
Rethinking Parameter Counting: Effective Dimensionality Revisited | 1 INTRODUCTION . Parameter counting pervades the narrative in modern deep learning . “ One of the defining properties of deep learning is that models are chosen to have many more parameters than available training data . In light of this capacity for overfitting , it is remarkable that simple algorithms like SGD reliab... | In this article, the authors revisited the idea of *effective dimensionality* as a complexity measure for large-scale machine learning systems, and in particular, modern deep neural networks. Theoretical arguments were provided for linear and generalized linear models (Theorem 4.1 and 4.2). Connections were made betwee... | SP:66d433dfb2512bdb004f50f94d38514636a89fc6 |
Rethinking Parameter Counting: Effective Dimensionality Revisited | 1 INTRODUCTION . Parameter counting pervades the narrative in modern deep learning . “ One of the defining properties of deep learning is that models are chosen to have many more parameters than available training data . In light of this capacity for overfitting , it is remarkable that simple algorithms like SGD reliab... | The paper applies the effective dimensionality (introduced by MacKay, Gull and others) to study the generalization properties of large probabilistic models. Effective dimensionality is the number of parameters determined by the data (derived from the curvature of the posterior at the MAP estimate), and shown to be more... | SP:66d433dfb2512bdb004f50f94d38514636a89fc6 |
Interpretable Relational Representations for Food Ingredient Recommendation Systems | 1 INTRODUCTION . Data mining and machine learning methods play an increasingly prominent role in food preference modeling , food ingredient pairing discovery and new recipe generation . Solving these tasks is nontrivial , since the goodness of ingredient combinations depends on many factors like taste , smell , cuisine... | This paper tackles ingredient recommender systems problem. This paper proposes the Interpretable Relational Representation Model (IRRM) to achieve both usefulness and interpretableness. There are two variants of the model, first is to model latent relation between two ingredients, the second is to leverage external kno... | SP:b663cb80c4f664393b5820a27e9ae004ef4ec413 |
Interpretable Relational Representations for Food Ingredient Recommendation Systems | 1 INTRODUCTION . Data mining and machine learning methods play an increasingly prominent role in food preference modeling , food ingredient pairing discovery and new recipe generation . Solving these tasks is nontrivial , since the goodness of ingredient combinations depends on many factors like taste , smell , cuisine... | The paper studies a promising task of interpretable food ingredients recommendation - there has been a growing interest in modeling recipes. The idea of leveraging KG to improve the interpretability/faithfulness of recipe-related ML tasks seems like a contribution to the community. In particular, the author proposes a ... | SP:b663cb80c4f664393b5820a27e9ae004ef4ec413 |
PDE-Driven Spatiotemporal Disentanglement | 1 INTRODUCTION . The interest of the machine learning community in physical phenomena has substantially grown for the last few years ( Shi et al. , 2015 ; Long et al. , 2018 ; Greydanus et al. , 2019 ) . In particular , an increasing amount of works studies the challenging problem of modeling the evolution of dynamical... | The paper presents a spatiotemporal disentanglement method for handling sequence data. Solving high-dimensional PDEs, for deriving the exact dynamics, is difficult; hence, this work proposes learning time-invariant and time-dependent representations separately to solve this problem. To achieve this goal, the authors de... | SP:fdee0ede48073d769467a45af029bf5c798ab6ce |
PDE-Driven Spatiotemporal Disentanglement | 1 INTRODUCTION . The interest of the machine learning community in physical phenomena has substantially grown for the last few years ( Shi et al. , 2015 ; Long et al. , 2018 ; Greydanus et al. , 2019 ) . In particular , an increasing amount of works studies the challenging problem of modeling the evolution of dynamical... | The authors present a generative model for videos where the latent trajectories have two components - a term without a slowness loss that represents "content" and a term with a slowness loss that represents "style". They present results on a dataset simulating the wave equation and on videos of moving MNIST digits and ... | SP:fdee0ede48073d769467a45af029bf5c798ab6ce |
Dual Graph Complementary Network | As a powerful representation learning method on graph data , graph neural networks ( GNNs ) have shown great popularity in tackling graph analytic problems . Although many attempts have been made in literatures to find strategies about extracting better embedding of the target nodes , few of them consider this issue fr... | This paper introduces a method on semi-supervised graph classification. For each graph, the method first constructs another view based on the cosine similarity between nodes' features, and from the two views (topology and feature similarity), GCN and GAT are applied to extract representations. All node representations ... | SP:d1f2bfa043d6ce88a18bbef8a0e694e92ab43d2e |
Dual Graph Complementary Network | As a powerful representation learning method on graph data , graph neural networks ( GNNs ) have shown great popularity in tackling graph analytic problems . Although many attempts have been made in literatures to find strategies about extracting better embedding of the target nodes , few of them consider this issue fr... | The paper presents a GNN model to jointly encode both topology and feature graphs to enhance node representations' quality. In particular, the model DGCN uses two GCNs to learn and propagate two different types of node representations on the topology graph, respectively. The model also utilizes two GATs to learn and pr... | SP:d1f2bfa043d6ce88a18bbef8a0e694e92ab43d2e |
Emergent Properties of Foveated Perceptual Systems | The goal of this work is to characterize the representational impact that foveation1 operations have for machine vision systems , inspired by the foveated human visual2 system , which has higher acuity at the center of gaze and texture-like encoding in3 the periphery . To do so , we introduce models consisting of a fir... | I thank the authors for a thoroughly written paper studying an important question for both machine learning and neuroscience. The authors propose a biologically inspired modification to CNN architectures by introducing foveation. Several thorough experiments are performed to assess the benefit of foveation with reasona... | SP:bdda04b701b73b4e5e5fec405a1b1219fbee5de7 |
Emergent Properties of Foveated Perceptual Systems | The goal of this work is to characterize the representational impact that foveation1 operations have for machine vision systems , inspired by the foveated human visual2 system , which has higher acuity at the center of gaze and texture-like encoding in3 the periphery . To do so , we introduce models consisting of a fir... | In this paper, authors study the functional advantages of a foveal transform of visual inputs. It is nicely introduced with a very comprehensive review of the literature. The method introduces a 2 two stage model of the visual system, where the first stage corresponds to the (fixed and non adaptive) foveation stage and... | SP:bdda04b701b73b4e5e5fec405a1b1219fbee5de7 |
Shape or Texture: Understanding Discriminative Features in CNNs | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) have achieved unprecedented performance in various computer vision tasks , such as image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; He et al. , 2016 ) , object detection ( Ren et al. , 2015 ; He et al. , 2017 ) and semantic segmentat... | Texture vs shape sensitivity is a basic and important question in understanding how deep convolutional nets work. This paper investigates this question by asking how CNNs represent shape versus texture information internally. Using a (texture-vs-shape) stylized imagenet pioneered by Geirhos (2018), the paper applies a... | SP:04982111c9f052a633c1cacb113263af408e6a24 |
Shape or Texture: Understanding Discriminative Features in CNNs | 1 INTRODUCTION . Convolutional neural networks ( CNNs ) have achieved unprecedented performance in various computer vision tasks , such as image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ; He et al. , 2016 ) , object detection ( Ren et al. , 2015 ; He et al. , 2017 ) and semantic segmentat... | The method provides two measures for assessing the degree at which shape is represented in CNNs. The first measure attempts to asses, within a given representation layer, the dimentionality required to encoder shape information. The second, evaluates the per-pixel shape representation, by attempting to generate the inp... | SP:04982111c9f052a633c1cacb113263af408e6a24 |
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers | 1 INTRODUCTION . A feedforward deep neural network consists of a stack of H layers , where H is the depth of the network . The value for the depthH is typically a hyperparameter and is chosen by network designers ( e.g. , ResNet-101 in He et al . 2016 ) . Each layer computes some transformation of the output of the pre... | This submission studies the dynamics and convergence properties of "deep equilibrium models", which are parametric fixed-point iterations corresponding to the infinite depth limit of "weight-tied" neural networks. As the authors point out, these networks differ from deep linear networks and networks in the NTK scaling ... | SP:894dddca0a75e8ac6e32583238fa19efce663601 |
On the Theory of Implicit Deep Learning: Global Convergence with Implicit Layers | 1 INTRODUCTION . A feedforward deep neural network consists of a stack of H layers , where H is the depth of the network . The value for the depthH is typically a hyperparameter and is chosen by network designers ( e.g. , ResNet-101 in He et al . 2016 ) . Each layer computes some transformation of the output of the pre... | The paper discusses the theory of deep equilibrium models with linear activations. The model weights are softmaxed to ensure that inference converges to a fixed point, a necessary condition for training deep equilibrium models. The paper then analyzes the gradient flow dynamics of such models. The main result is that l... | SP:894dddca0a75e8ac6e32583238fa19efce663601 |
Symmetry-Aware Actor-Critic for 3D Molecular Design | Automating molecular design using deep reinforcement learning ( RL ) has the potential to greatly accelerate the search for novel materials . Despite recent progress on leveraging graph representations to design molecules , such methods are fundamentally limited by the lack of three-dimensional ( 3D ) information . In ... | The paper proposes an actor-critic neural network architecture for autoregressive generation of 3D molecular structures with reinforcement learning (RL). It builds upon the RL approach by Simm et al. (2020) which makes use of internal coordinates in order to deal with the symmetries that occur when placing atoms in the... | SP:32be26cc5561e9335adc2179a3c832258c2a346e |
Symmetry-Aware Actor-Critic for 3D Molecular Design | Automating molecular design using deep reinforcement learning ( RL ) has the potential to greatly accelerate the search for novel materials . Despite recent progress on leveraging graph representations to design molecules , such methods are fundamentally limited by the lack of three-dimensional ( 3D ) information . In ... | This work presents an approach for 3D molecular design using reinforcement learning that exploits rotational symmetries in molecular conformations. The formulation involves an MDP that selects atoms from a “bag” and positions them in 3D space. The reward function is based on PM6 energies to encourage the generation of ... | SP:32be26cc5561e9335adc2179a3c832258c2a346e |
Graph Autoencoders with Deconvolutional Networks | 1 INTRODUCTION . Autoencoders have demonstrated excellent performance on tasks such as unsupervised representation learning ( Bengio , 2009 ) and de-noising ( Vincent et al. , 2010 ) . Recently , several studies ( Zeiler & Fergus , 2014 ; Long et al. , 2015 ) have demonstrated that the performance of autoencoders can b... | The main contribution of this paper is that the authors design a graph deconvolutional network that combines inverse filters in the spectral domain and de-noising layers in the wavelet domain. Further graph autoencoders are proposed based on the graph convolutional networks and the graph deconvolutional networks. Many ... | SP:00af76b0f241598c5d3c11fc330d6426a1dcd473 |
Graph Autoencoders with Deconvolutional Networks | 1 INTRODUCTION . Autoencoders have demonstrated excellent performance on tasks such as unsupervised representation learning ( Bengio , 2009 ) and de-noising ( Vincent et al. , 2010 ) . Recently , several studies ( Zeiler & Fergus , 2014 ; Long et al. , 2015 ) have demonstrated that the performance of autoencoders can b... | The authors proposed graph deconvolution layers (GDNs) and employ GDNs to learn graph embedding in a encoder-decoder framework. The authors performs inverse signal recovery in spectral domain and then conducts a de-noising step in wavelet domain to remove the amplified noise. The proposed method can outperform baseline... | SP:00af76b0f241598c5d3c11fc330d6426a1dcd473 |
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly | The learning rate ( LR ) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs . However , it is also one of the least automated parts of machine learning systems and usually costs significant manual effort and computing . Though there are pre-defined LR schedules and optimizers... | This paper uses Bayesian optimization (BO) to dynamically tune the learning rate during the course of training of DNNs. In every stage of training, the algorithm firstly uses BO to explore different learning rates with the help of a parametric exponential model for learning rate extrapolation, and then applies the sel... | SP:807c7df69d51b93b5a0da3ea56506a9bfadd0595 |
AutoLRS: Automatic Learning-Rate Schedule by Bayesian Optimization on the Fly | The learning rate ( LR ) schedule is one of the most important hyper-parameters needing careful tuning in training DNNs . However , it is also one of the least automated parts of machine learning systems and usually costs significant manual effort and computing . Though there are pre-defined LR schedules and optimizers... | Training deep neural networks is typically done using gradient-based methods with either pre-defined learning-rate schedules or off-the-shelf adaptive optimizers (such as Adam). The former can not reliably align with the non-linear loss landscape, while the latter add additional hyperparameters to tune. This paper prop... | SP:807c7df69d51b93b5a0da3ea56506a9bfadd0595 |
Ensembles of Generative Adversarial Networks for Disconnected Data | 1 INTRODUCTION . Generative networks , such as generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) and variational autoencoders ( Kingma & Welling , 2013 ) , have shown impressive performance in generating highly realistic images that were not observed in the training set ( Karras et al. , 2017 ; 2019... | This work proposes that most relevant datasets to the machine learning community today have support on a mixture of disconnected components. They argue that popular GAN models cannot fit distributions of this kind and provide a number of proofs to convince the reader of this claim. The authors discuss a number of simpl... | SP:cfcc0751394443ee8179098ee4fec54128f668a3 |
Ensembles of Generative Adversarial Networks for Disconnected Data | 1 INTRODUCTION . Generative networks , such as generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) and variational autoencoders ( Kingma & Welling , 2013 ) , have shown impressive performance in generating highly realistic images that were not observed in the training set ( Karras et al. , 2017 ; 2019... | This paper addresses the problem that models like GANS which learn continuous mappings from a connected latent space, are incapable of producing a mapping that contains disconnected components. The authors argue that real-world classes like "badger" and "zebra" are indeed disconnected and thus the inability of GANs to ... | SP:cfcc0751394443ee8179098ee4fec54128f668a3 |
Generalizing Graph Convolutional Networks via Heat Kernel | Graph convolutional networks ( GCNs ) have emerged as a powerful framework for mining and learning with graphs . A recent study shows that GCNs can be simplified as a linear model by removing nonlinearities and weight matrices across all consecutive layers , resulting the simple graph convolution ( SGC ) model . In thi... | This submission introduced a new graph convolutional operator based on heat diffusion, named heat kernel GCN (HKGCN). First, continuous-time heat diffusion on graphs is reviewed, where the solution is given by the heat equation (6). Then, the authors showed that classical GCN can be approximated in the same formulation... | SP:e01809728bbe427d7b505f3f36d51b55a8e49d49 |
Generalizing Graph Convolutional Networks via Heat Kernel | Graph convolutional networks ( GCNs ) have emerged as a powerful framework for mining and learning with graphs . A recent study shows that GCNs can be simplified as a linear model by removing nonlinearities and weight matrices across all consecutive layers , resulting the simple graph convolution ( SGC ) model . In thi... | This paper studies semi-supervised node classification in graph data. One powerful approach to the task is graph convolutional networks, which use discrete layers to perform information propagation. The paper generalizes GCNs into a continuous model via heat kernel, where the proposed model uses continuous layers for i... | SP:e01809728bbe427d7b505f3f36d51b55a8e49d49 |
A Unified Paths Perspective for Pruning at Initialization | 1 INTRODUCTION . A wealth of recent work has been dedicated to characterizing the training dynamics and generalization bounds of neural networks under a linearized approximation of the network depending on its parameters at initialization ( Jacot et al. , 2018 ; Arora et al. , 2019 ; Lee et al. , 2019a ; Woodworth et a... | The paper studies the problem of neural network pruning at initialization through the lens of neural tangent kernels (NTK). As a result, the paper delivers a unified perspective on SNIP, GRASP, and SynFlow. Based on the framework, the paper provides a method to approximate the convergence dynamics of pruned models. The... | SP:c1d77b1cc1c26d9d596eea8d19b4b3607eb218b9 |
A Unified Paths Perspective for Pruning at Initialization | 1 INTRODUCTION . A wealth of recent work has been dedicated to characterizing the training dynamics and generalization bounds of neural networks under a linearized approximation of the network depending on its parameters at initialization ( Jacot et al. , 2018 ; Arora et al. , 2019 ; Lee et al. , 2019a ; Woodworth et a... | In this paper, the authors propose a new kernel named Path Kernel to understand deep neural network training. The key idea is to reparameterize the network with respect to the active path in the network. In this way, they can decompose the Tangent Kernel into data-dependent and architecture-dependent pieces. They autho... | SP:c1d77b1cc1c26d9d596eea8d19b4b3607eb218b9 |
GINN: Fast GPU-TEE Based Integrity for Neural Network Training | 1 INTRODUCTION . Every day , Deep Learning ( DL ) is incorporated into some new aspects of the society . As a result , numerous industries increasingly rely on DL models to make decisions , ranging from computer vision to natural language processing . The training process for these DL models requires a substantial quan... | The paper presents new techniques to enable secure neural training in the TEE+GPU paradigm. This is a natural extension of previous work like Slalom which only handles the inference case. The authors propose a two-step approach. First, they clip the gradients during training to force the attacker to insert multiple dev... | SP:e3455519fcc3ce8644fa55c52771b5414a571026 |
GINN: Fast GPU-TEE Based Integrity for Neural Network Training | 1 INTRODUCTION . Every day , Deep Learning ( DL ) is incorporated into some new aspects of the society . As a result , numerous industries increasingly rely on DL models to make decisions , ranging from computer vision to natural language processing . The training process for these DL models requires a substantial quan... | The paper targets security challenges of deep neural networks. While solutions can hardly scale up to support realistic DNN model training workloads, the authors propose GINN to support integrity-preserving DL training by random verification of stochastic gradient steps inside trusted execution environments (TEE). GINN... | SP:e3455519fcc3ce8644fa55c52771b5414a571026 |
Stego Networks: Information Hiding on Deep Neural Networks | 1 INTRODUCTION . As much as it goes without saying knowledge is power , inventing methods for keeping and selectively conveying secret messages has been a crucial mission throughout the history of humanity . Among various methods to protect secrets , an effective approach called steganography makes it difficult to dete... | This paper proposed a method to hide information in the parameters of neural network models. To avoid significant perturbation, the paper only considers embed the information in the fraction bits of the parameters. The paper considers hiding the information in either the least significant bits of the most significant b... | SP:8dd0ec8f16a72dad739f6e31b9ced0a8e8989b9e |
Stego Networks: Information Hiding on Deep Neural Networks | 1 INTRODUCTION . As much as it goes without saying knowledge is power , inventing methods for keeping and selectively conveying secret messages has been a crucial mission throughout the history of humanity . Among various methods to protect secrets , an effective approach called steganography makes it difficult to dete... | This paper highlights and studies the interesting possibility of hiding information within neural network weights, which is a form of steganography. The sensitivity of different neural network layers to perturbations is evaluated, and based on this a technique for hiding information is proposed and demonstrated. It is ... | SP:8dd0ec8f16a72dad739f6e31b9ced0a8e8989b9e |
Stabilized Medical Image Attacks | 1 INTRODUCTION . Computer Aided Diagnosis ( CADx ) has been widely applied in the medical screening process . The automatic diagnosis benefits doctors to efficiently obtain health status to avoid disease exacerbation . Recently , Convolutional Neural Networks ( CNNs ) have been utilized in CADx to improve the diagnosis... | The authors present a universal medical attack method that can consistently produce adversarial examples across several medical imaging domains. The authors achieve this by developing a novel objective function that includes two terms, which they refer to as stabilized medical attack (SMA). The first term is the loss d... | SP:a7056ed3154309910542a66d52b98ea7a7e1ba4f |
Stabilized Medical Image Attacks | 1 INTRODUCTION . Computer Aided Diagnosis ( CADx ) has been widely applied in the medical screening process . The automatic diagnosis benefits doctors to efficiently obtain health status to avoid disease exacerbation . Recently , Convolutional Neural Networks ( CNNs ) have been utilized in CADx to improve the diagnosis... | The authors proposed to introduce a combination of a loss deviation term and a loss stabilization term to generate more consistent adversarial perturbations on medical images. The loss deviation term increases the divergence between the CNN prediction of an adversarial example and its ground truth label. At the same ti... | SP:a7056ed3154309910542a66d52b98ea7a7e1ba4f |
Revisiting Graph Neural Networks for Link Prediction | 1 INTRODUCTION . Link prediction is to predict potential or missing links connecting pairwise nodes in a network . It has wide applications in various fields , such as friend recommendation in social networks ( Adamic & Adar , 2003 ) , movie recommendation in Netflix ( Bennett et al. , 2007 ) , protein-protein interact... | The paper focuses on the link prediction task for graph neural networks. More specifically, it compares GAE and SEAL by providing theoretical evidence why GAE is not able to learn structural link representations, which as a result leads to suboptimal performance in the link prediction task. The paper also introduces a ... | SP:ff7cdd0d7c011a59c7fc3088f7dbd6145fc3ca72 |
Revisiting Graph Neural Networks for Link Prediction | 1 INTRODUCTION . Link prediction is to predict potential or missing links connecting pairwise nodes in a network . It has wide applications in various fields , such as friend recommendation in social networks ( Adamic & Adar , 2003 ) , movie recommendation in Netflix ( Bennett et al. , 2007 ) , protein-protein interact... | This paper provides theoretical analysis of graph neural networks for link prediction, following a number of recent papers that have developed the theoretical understanding of graph neural networks. For example, the work of Xu et al (ICLR 2019) draws on the Weisfeiler-Lehman graph isomorphism test to develop a theoreti... | SP:ff7cdd0d7c011a59c7fc3088f7dbd6145fc3ca72 |
Symbol-Shift Equivariant Neural Networks | Neural networks have been shown to have poor compositionality abilities : while they can produce sophisticated output given sufficient data , they perform patchy generalization and fail to generalize to new symbols ( e.g . switching a name in a sentence by a less frequent one or one not seen yet ) . In this paper , we ... | This paper proposes a new type of models that are equivariant to entity permutations, which is an important criterion to build language models that can easily generalize to new entities. The authors modified a Memory-Network and a Third-order tensor product RNN to make them symbolic-shit invariant. The new models were ... | SP:6f78f139e4868101aba22e15be3678379fdccb6c |
Symbol-Shift Equivariant Neural Networks | Neural networks have been shown to have poor compositionality abilities : while they can produce sophisticated output given sufficient data , they perform patchy generalization and fail to generalize to new symbols ( e.g . switching a name in a sentence by a less frequent one or one not seen yet ) . In this paper , we ... | The authors propose a network that is equivariant to entity permutations without requiring the pre-specification of the set of entities. To this end, the authors propose a hybrid semantic-symbolic embedding which they integrate into two QA models. Finally, the authors show significant gains on the bAbi tasks, with espe... | SP:6f78f139e4868101aba22e15be3678379fdccb6c |
Estimation of Number of Communities in Assortative Sparse Networks | 1 INTRODUCTION . Statistical analysis of network data has now become an extensively studied field within statistics and machine learning ( see ( Goldenberg et al. , 2010 ; Kolaczyk & Csárdi , 2014 ; Newman , 2018 ) for reviews ) . Network datasets show up in several disciplines . Examples include networks originating f... | The authors propose a spectral framework using the Bethe Hessian matrix to infer the number of communities in sparse networks. The method relies on the eigendecomposition of the Bethe Hessian matrix for which negative eigenvalues are preserved and the number of such eigenvalues used to define the number of communities.... | SP:93ca1ca8da285e1dbe05f3c83a51042ad0a1b3be |
Estimation of Number of Communities in Assortative Sparse Networks | 1 INTRODUCTION . Statistical analysis of network data has now become an extensively studied field within statistics and machine learning ( see ( Goldenberg et al. , 2010 ; Kolaczyk & Csárdi , 2014 ; Newman , 2018 ) for reviews ) . Network datasets show up in several disciplines . Examples include networks originating f... | In this paper the authors consider the problem of computing the number of communities K in an arbitrarily sparse graph generated under the Stochastic Block Model (SMB). Previous studies that consider the problem of computing K show theoretical guarantees only in graphs with average degree $\Omega(\log n)$. One of the p... | SP:93ca1ca8da285e1dbe05f3c83a51042ad0a1b3be |
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria | 1 INTRODUCTION . Multi-agent reinforcement learning ( MARL ) ( Bu et al. , 2008 ; Sutton & Barto , 2018 ) has achieved great empirical success , e.g. , in playing the game of Go ( Silver et al. , 2016 ; 2017 ) , Dota 2 ( Berner et al. , 2019 ) , and StarCraft 2 ( Vinyals et al. , 2019 ) , which are all driven by policy... | The authors consider self-play in zero-sum discounted two-player Markov games with compact state space and finite actions. They present a smooth fictitious self-play algorithm where each player adopts an entropy-regularized policy optimization method with the average of the past generated Q-values. Under appropriate as... | SP:529fd3a7215e22cd444370bd86d7b0522cdbd526 |
Policy Optimization in Zero-Sum Markov Games: Fictitious Self-Play Provably Attains Nash Equilibria | 1 INTRODUCTION . Multi-agent reinforcement learning ( MARL ) ( Bu et al. , 2008 ; Sutton & Barto , 2018 ) has achieved great empirical success , e.g. , in playing the game of Go ( Silver et al. , 2016 ; 2017 ) , Dota 2 ( Berner et al. , 2019 ) , and StarCraft 2 ( Vinyals et al. , 2019 ) , which are all driven by policy... | This paper studies the problem of learning to play a Nash equilibrium in two-player, zero-sum Markov games. This is a longstanding problem, with many algorithms proposed but relatively few theoretical convergence guarantees, and most of those either for quite restricted settings or with strong assumptions. This is in... | SP:529fd3a7215e22cd444370bd86d7b0522cdbd526 |
H-divergence: A Decision-Theoretic Probability Discrepancy Measure | 1 INTRODUCTION . Quantifying the difference between two probability distributions is a fundamental problem in machine learning . Modelers choose different types of discrepancies , or probability divergences , to encode their prior knowledge , i.e . which aspects should be considered to evaluate the difference , and how... | This paper proposes a H divergence that is a generalization of many popular f divergences and IPMs. The paper gives an empirical estimator with convergence rates for this divergence, where the rates are very fast when the two distributions are equal. The paper shows how the empirical estimator has practical use for two... | SP:8d4e00c5a4fac78c1fa8a9161fd4e5c72f7ad508 |
H-divergence: A Decision-Theoretic Probability Discrepancy Measure | 1 INTRODUCTION . Quantifying the difference between two probability distributions is a fundamental problem in machine learning . Modelers choose different types of discrepancies , or probability divergences , to encode their prior knowledge , i.e . which aspects should be considered to evaluate the difference , and how... | The distance or divergence between two probability distributions is essential for machine learning. This paper introduces a new class of divergence functions based on optimal decision loss function. They first introduce a class of entropy functional, namely the loss function depending on the action and state. This type... | SP:8d4e00c5a4fac78c1fa8a9161fd4e5c72f7ad508 |
Double Generative Adversarial Networks for Conditional Independence Testing | 1 INTRODUCTION . Conditional independence ( CI ) is a fundamental concept in statistics and machine learning . Testing conditional independence is a key building block and plays a central role in a wide variety of statistical learning problems , for instance , causal inference ( Pearl , 2009 ) , graphical models ( Koll... | The paper proposed a novel simulation based testing procedure for conditional independence: X\perp Y | Z. The testing procedure incorporate the techniques of GAN, which is especially useful for dealing with high-dimensional data. The testing procedure first learn the generative adversarial network that is able to simul... | SP:0b9ca8ea62df97ffbdae1028e156966f82edd366 |
Double Generative Adversarial Networks for Conditional Independence Testing | 1 INTRODUCTION . Conditional independence ( CI ) is a fundamental concept in statistics and machine learning . Testing conditional independence is a key building block and plays a central role in a wide variety of statistical learning problems , for instance , causal inference ( Pearl , 2009 ) , graphical models ( Koll... | This paper considers the problem of conditional independence testing, especially when the variables are high-dimensional. The authors proposed a double GAN based algorithm. Two GANs are designed to learn the conditional probability distributions P_{X|Z} and P_{Y|Z}, then used to generate samples to compute the test sta... | SP:0b9ca8ea62df97ffbdae1028e156966f82edd366 |
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures | 1 INTRODUCTION Proteins perform specific biological functions essential for all living organisms and hence play a key role when investigating the most fundamental questions in the life sciences . These biomolecules are composed of one or several chains of amino acids , which fold into specific conformations to enable v... | The authors describe a method to transform 3D protein structures for supervised machine learning. Their method introduces a convolution operation that considers both the intrinsic distances between atoms as defined by their bond structure and the extrinsic distances as defined by 3D proximity. They also introduce inter... | SP:913965fb0d96422d092b90304a652255f57f2a3c |
Intrinsic-Extrinsic Convolution and Pooling for Learning on 3D Protein Structures | 1 INTRODUCTION Proteins perform specific biological functions essential for all living organisms and hence play a key role when investigating the most fundamental questions in the life sciences . These biomolecules are composed of one or several chains of amino acids , which fold into specific conformations to enable v... | This paper describes a deep learning architecture for representing and performing classifications on protein structures. The representation involves three different distances: Euclidean distance and the shorted path between two atoms, where edges are either along covalent bonds or also include hydrogen bonds. Each at... | SP:913965fb0d96422d092b90304a652255f57f2a3c |
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval | 1 INTRODUCTION . Open domain question answering is a challenging task where the answer to a given question needs to be extracted from a large pool of documents . The prevailing approach ( Chen et al. , 2017 ) tackles the problem in two stages . Given a question , a retriever first produces a list of k candidate documen... | This paper proposes multi-hop dense retrieval for open-domain multi-hop question answering. It extends previous dense passage retrieval into the corresponding multi-hop version by using retrieved passages to latently reformulate the query representation after each retrieval pass. In the end, it can significantly improv... | SP:facb7e43da318900edf3d247467a45c3d3ae7d42 |
Answering Complex Open-Domain Questions with Multi-Hop Dense Retrieval | 1 INTRODUCTION . Open domain question answering is a challenging task where the answer to a given question needs to be extracted from a large pool of documents . The prevailing approach ( Chen et al. , 2017 ) tackles the problem in two stages . Given a question , a retriever first produces a list of k candidate documen... | This paper extends the recently proposed dense retrieval methods to the multi-hop open-domain questions, so as to handle complex multi-hop queries. The overall idea is simple, direct but effective. The authors conduct extensive experiments on two multi-hop datasets, HotpotQA and multi-evidence FEVER, and evaluation res... | SP:facb7e43da318900edf3d247467a45c3d3ae7d42 |
Post-Training Weighted Quantization of Neural Networks for Language Models | 1 INTRODUCTION . Training techniques for deep neural networks ( DNNs ) have been developed in ways to incur a lot of parameter redundancy to expedite seeking local minima ( Denil et al. , 2013 ; Jonathan Frankle , 2019 ) . As a result , various model compression techniques including parameter pruning ( Han et al. , 201... | Based on a previous classic binary coding scheme, this paper proposed to introduce a modification $m_i$ on the binarization scaling factor $\alpha$, by considering the weight magnitude. It further use 3 hyperparamters to refine $m_i$ by constraining its upper/lower bound and exponent. Besides, this work spent lengthy ... | SP:fad2af574548c00ab1e950a118a2e0d206663b94 |
Post-Training Weighted Quantization of Neural Networks for Language Models | 1 INTRODUCTION . Training techniques for deep neural networks ( DNNs ) have been developed in ways to incur a lot of parameter redundancy to expedite seeking local minima ( Denil et al. , 2013 ; Jonathan Frankle , 2019 ) . As a result , various model compression techniques including parameter pruning ( Han et al. , 201... | The paper employs the binary-coding-based post-training quantization (without retraining) for language modeling. The key contribution is that weight importance is considered while determining binary code (a, B). Two methods, Greedy and Alternating, are also modified to use the importance. The algorithm uses a novel nor... | SP:fad2af574548c00ab1e950a118a2e0d206663b94 |
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic | 1 INTRODUCTION . Significant progress has been made in quantizing ( or even binarizing ) neural networks , and numerous methods have been proposed that reduce the precision of weights , activations , and even gradients while retaining high accuracy ( Courbariaux et al. , 2016 ; Hubara et al. , 2016 ; Li et al. , 2016 ;... | This paper explores to solve an often ignored issue in quantization: accumulation precision. As the bit-width of input scales down, the area/energy cost of the accumulator starts to dominate. The cyclic method proposed by the authors at the first glance is not intuitive. However, it's surprising that the surveyed mode... | SP:1415d403cc5b85e50a37458d786bf31d01045f60 |
WrapNet: Neural Net Inference with Ultra-Low-Precision Arithmetic | 1 INTRODUCTION . Significant progress has been made in quantizing ( or even binarizing ) neural networks , and numerous methods have been proposed that reduce the precision of weights , activations , and even gradients while retaining high accuracy ( Courbariaux et al. , 2016 ; Hubara et al. , 2016 ; Li et al. , 2016 ;... | The paper introduces a new neural network layer that enables training NNs with quantized activations using reduced bit-width accumulators. The cyclic activation layer makes overflows smooth, instead of being discontinuous and this enables achieving better accuracy on quantized networks on reduced bit-width accumulators... | SP:1415d403cc5b85e50a37458d786bf31d01045f60 |
Smooth Activations and Reproducibility in Deep Networks | 1 INTRODUCTION . Recent developments in deep learning leave no question about the advantages of deep networks over classical methods , which relied heavily on linear convex optimization solutions . With their astonishing unprecedented success , deep models are providing solutions to a continuously increasing number of ... | This paper addresses the problem that deep neural networks (DNNs) can lead to different predictions (even when they are initialized the same way) due to the stochasticity of samples selected in mini-batch SGD and update procedures from different optimizers, which leads to convergence to different regions along the loss... | SP:ce4275ab9437fd5c73c61a5ff17ed24881fdd717 |
Smooth Activations and Reproducibility in Deep Networks | 1 INTRODUCTION . Recent developments in deep learning leave no question about the advantages of deep networks over classical methods , which relied heavily on linear convex optimization solutions . With their astonishing unprecedented success , deep models are providing solutions to a continuously increasing number of ... | The paper claims that smooth activations are more reproducible than ReLU. The accuracy gain claims seem marginal and not carefully carried out, further ablation studies are needed to strengthen the conclusion on accuracy. However, the main point of the paper is reproducibility where the feature is measured by the ‘Pred... | SP:ce4275ab9437fd5c73c61a5ff17ed24881fdd717 |
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning | 1 INTRODUCTION . The goal of disentangled representation learning is to encode input data into a low-dimensional space that preserves information about the salient factors of variation , so that each dimension of the representation corresponds to a distinct factor in the data ( Bengio et al. , 2013 ; Locatello et al. ,... | In $\beta$-VAE, one challenge is to choose the hyper-parameter $\beta$ that controls the trade-off between the reconstruction quality and the disentanglement. This paper proposes a method called DynamicVAE. Rather than using a fixed hyperparameter $\beta$, the method leverages a modified incremental Proportional-integr... | SP:86c007cb8af744f84b03a3a424da380984309ae8 |
DynamicVAE: Decoupling Reconstruction Error and Disentangled Representation Learning | 1 INTRODUCTION . The goal of disentangled representation learning is to encode input data into a low-dimensional space that preserves information about the salient factors of variation , so that each dimension of the representation corresponds to a distinct factor in the data ( Bengio et al. , 2013 ; Locatello et al. ,... | This paper introduces a strategy for controlling the beta value of a beta-VAE during training using approaches from control theory that allow it to target a designated level of KL divergence between the encoder distribution and the prior. This is done in a way that aims to achieve good reconstructions while maintainin... | SP:86c007cb8af744f84b03a3a424da380984309ae8 |
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning | 1 INTRODUCTION Many real-world multi-agent tasks contain scenarios in which an agent must deal with varying numbers and/or types of cooperative agents , antagonist enemies or other entities . Agents , however , can often select their optimal actions while ignoring a subset of agents/entities . For example , in the spor... | This paper proposes an observation factorization method to avoid the influence of the irrelevant part on value estimation. Specifically, they design an entity-wise attention network with a masking procedure. This network is used to filter the irrelevant part of the original observation of each agent. Then the output is... | SP:abd06100a48b4b5467cd63cd024d6387d163f96a |
Randomized Entity-wise Factorization for Multi-Agent Reinforcement Learning | 1 INTRODUCTION Many real-world multi-agent tasks contain scenarios in which an agent must deal with varying numbers and/or types of cooperative agents , antagonist enemies or other entities . Agents , however , can often select their optimal actions while ignoring a subset of agents/entities . For example , in the spor... | This paper proposes to incorporate a masked attention mechanism in QMIX for value function factorization to disentangle value predictions from irrelevant agents/entities. The masking is based on a random sampling from the whole set of agents to from random subsets, based on which it can compute within-group and without... | SP:abd06100a48b4b5467cd63cd024d6387d163f96a |
RSO: A Gradient Free Sampling Based Approach For Training Deep Neural Networks | 1 INTRODUCTION . Deep neural networks solve a variety of problems using multiple layers to progressively extract higher level features from the raw input . The commonly adopted method to train deep neural networks is backpropagation ( Rumelhart et al . ( 1985 ) ) and it has been around for the past 35 years . Backpropa... | The paper proposes an RSO (random search optimization) method for training deep neural networks. This method is gradient-free and based on the Markov Chain Monte Carlo search. In particular, it adds a perturbation to weight in a deep neural network and tests if it reduces the loss on a mini-batch: the weight is updated... | SP:30fffab6fd645af0a4f8b9c96d5535f11469667e |
RSO: A Gradient Free Sampling Based Approach For Training Deep Neural Networks | 1 INTRODUCTION . Deep neural networks solve a variety of problems using multiple layers to progressively extract higher level features from the raw input . The commonly adopted method to train deep neural networks is backpropagation ( Rumelhart et al . ( 1985 ) ) and it has been around for the past 35 years . Backpropa... | Instead of back-propagation, the authors consider a randomized search heuristic to train the parameters of neural networks. The proposed work is based on the hypothesis that the initial set of neural network weights is close to the final solution. The authors identify the problem that existing randomized search methods... | SP:30fffab6fd645af0a4f8b9c96d5535f11469667e |
When does preconditioning help or hurt generalization? | 1 INTRODUCTION . We study the generalization property of an estimator θ̂ obtained by minimizing the empirical risk ( or the training error ) L ( fθ ) via a preconditioned gradient update with preconditioner P : θt+1 = θt − ηP ( t ) ∇θtL ( fθt ) , t = 0 , 1 , . . . ( 1.1 ) Setting P = I recovers gradient descent ( GD ) ... | The authors theoretically study the prediction performance of pre-conditioned gradient descent/flow with linear models and squared loss aligning in the setting of least squares regression and non-parametric regression. For parametric least squares, the predication performance of the limiting solution for preconditioned... | SP:4394b824230526bc513436acebdacb85254e7e81 |
When does preconditioning help or hurt generalization? | 1 INTRODUCTION . We study the generalization property of an estimator θ̂ obtained by minimizing the empirical risk ( or the training error ) L ( fθ ) via a preconditioned gradient update with preconditioner P : θt+1 = θt − ηP ( t ) ∇θtL ( fθt ) , t = 0 , 1 , . . . ( 1.1 ) Setting P = I recovers gradient descent ( GD ) ... | The paper studies the effects of preconditioning on generalization properties in deep learning. By using a bias-variance decomposition of the expected risk, the paper determines optimal precondition matrix $P$ for bias and variance. Then the paper analyzes the generalization performance via the aspects: clean labels, w... | SP:4394b824230526bc513436acebdacb85254e7e81 |
The Compact Support Neural Network | Neural networks are popular and useful in many fields , but they have the problem of giving high confidence responses for examples that are away from the training data . This makes the neural networks very confident in their prediction while making gross mistakes , thus limiting their reliability for safety critical ap... | The paper presents an approach that supports better performance when out of distribution cases occur. It does so by letting neurons be of only compact support and thus if the input is out of distribution (OOD) it is expected to be outside that support and therefore the output will be zero. This is used to detect OOD ex... | SP:d5fc92a93b6eb506bfe8cd218f09cb28fdc29b75 |
The Compact Support Neural Network | Neural networks are popular and useful in many fields , but they have the problem of giving high confidence responses for examples that are away from the training data . This makes the neural networks very confident in their prediction while making gross mistakes , thus limiting their reliability for safety critical ap... | The authors propose a new neural network unit and training algorithm in order to improve OOD detection. Units can smoothly be changed between standard (dot-product) and RBF type through a shape hyperparameter. During training, this hyperparameter is slowly moved in the direction of the RBF shape. Empirical comparisons ... | SP:d5fc92a93b6eb506bfe8cd218f09cb28fdc29b75 |
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP | 1 INTRODUCTION . Deep neural networks are the bedrock of artificial intelligence tasks such as object detection , speech recognition , and natural language processing ( Redmon & Farhadi , 2018 ; Chorowski et al. , 2015 ; Devlin et al. , 2019 ) . While modern networks have hundreds of millions to billions of parameters ... | This paper introduces an optimization method for pruning channels in networks. The authors first motivated the proposed approach by showing that current pruning methods will result in "inactive weights" for the following layer. Then the authors introduce a QCQP optimization method that can constrain the exact amout of... | SP:3879a6f9b904429fa766fe496ca16cefecdc5d02 |
Succinct Network Channel and Spatial Pruning via Discrete Variable QCQP | 1 INTRODUCTION . Deep neural networks are the bedrock of artificial intelligence tasks such as object detection , speech recognition , and natural language processing ( Redmon & Farhadi , 2018 ; Chorowski et al. , 2015 ; Devlin et al. , 2019 ) . While modern networks have hundreds of millions to billions of parameters ... | In this manuscript, a new pruning method is proposed by considering the inherent quadratic constraint between consecutive layers. Without this constraint, inactive weights cannot be safely removed. Even with the same objective function, the optimized result is different, as shown in the motivation section. Based on thi... | SP:3879a6f9b904429fa766fe496ca16cefecdc5d02 |
Graph-Based Continual Learning | 1 INTRODUCTION . Recent breakthroughs of deep neural networks often hinge on the ability to repeatedly iterate over stationary batches of training data . When exposed to incrementally available data from non-stationary distributions , such networks often fail to learn new information without forgetting much of its prev... | This paper presented a memory-based continual learning model where relationships between training samples are represented with a random graph that is defined from the non-linear embedding of the input data. Catastrophic forgetting between tasks is partially (1) alleviated with a graph regularization that penalizes chan... | SP:4e23c046f8234b35d88e3957b0725fb7a3d06374 |
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