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SEED: Self-supervised Distillation For Visual Representation | 1 INTRODUCTION The burgeoning studies and success on self-supervised learning ( SSL ) for visual representation are mainly marked by its extraordinary potency of learning from unlabeled data at scale . Accompanying with the SSL is its phenomenal benefit of obtaining task-agnostic representations while allowing the trai... | This paper proposes a knowledge distillation (KD) approach for self-supervised learning (SSL) with small neural network models. The authors first observe that the state-of-the-art contrastive learning-based SSL does not obtain good performance on small models, due to the larger model capacity required for instance d... | SP:9f8b8c56abc19f30f03426367ab036ba47bc1f27 |
Rethinking Compressed Convolution Neural Network from a Statistical Perspective | 1 INTRODUCTION . The introduction of AlexNet ( Krizhevsky et al. , 2012 ) spurred a line of research in 2D CNNs , which progressively achieve high levels of accuracy in the domain of image recognition ( Simonyan & Zisserman , 2015 ; Szegedy et al. , 2015 ; He et al. , 2016 ; Huang et al. , 2017 ) . The current stateof-... | This paper formulated higher-order CNNs into a Tucker form and provides sample complexity analysis to higher-order CNNs and compressed designs of CNNs via tensor analysis. It uses then theoretically analyzes the efficiency of four block designs from ResNet, MobileNetV1, and MobileNetV2. The paper also conducts numerica... | SP:fed81f79f821a00e7bf5e3fdd1bbf3ce269b46f8 |
Rethinking Compressed Convolution Neural Network from a Statistical Perspective | 1 INTRODUCTION . The introduction of AlexNet ( Krizhevsky et al. , 2012 ) spurred a line of research in 2D CNNs , which progressively achieve high levels of accuracy in the domain of image recognition ( Simonyan & Zisserman , 2015 ; Szegedy et al. , 2015 ; He et al. , 2016 ; Huang et al. , 2017 ) . The current stateof-... | This paper provides theoretical analysis of the estimating power of CNN (3 and 5 layers). By formulating the problem using tensors, the authors showed that the estimating error of the learned CNN weights with respect to the true weights is of the order $\sqrt{d/n}$ where $d$ measures model complexity and $n$ is the tra... | SP:fed81f79f821a00e7bf5e3fdd1bbf3ce269b46f8 |
Large Associative Memory Problem in Neurobiology and Machine Learning | 1 INTRODUCTION . Associative memory is defined in psychology as the ability to remember ( link ) many sets , called memories , of unrelated items . Prompted by a large enough subset of items taken from one memory , an animal or computer with an associative memory can retrieve the rest of the items belonging to that mem... | This paper presents a novel class of associative memory models. The model is expressed as a network with two-body interactions (synapses) and a well-defined energy function, and it is shown to generalise and unify several existing approaches (Hopfield Networks, Dense Associative Memories and Modern Hopfield Networks). ... | SP:4496f5847520ba21176ac3ea35183849b6b8239e |
Large Associative Memory Problem in Neurobiology and Machine Learning | 1 INTRODUCTION . Associative memory is defined in psychology as the ability to remember ( link ) many sets , called memories , of unrelated items . Prompted by a large enough subset of items taken from one memory , an animal or computer with an associative memory can retrieve the rest of the items belonging to that mem... | The authors proposed a dynamical system that unifies several associative memory models, including the classical Hopfield network and two recently proposed modern Hopfield networks. The dynamical system is described as interactions between two groups of neurons (feature and memory neurons), providing a more biological i... | SP:4496f5847520ba21176ac3ea35183849b6b8239e |
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback | 1 INTRODUCTION . Zeroth-order optimization ( ZO ) algorithms have been widely used to solve online optimization problems where first or second order information ( i.e. , gradient or Hessian information ) is unavailable at each time instant . Such problems arise , e.g. , in online learning and involve adversarial traini... | This manuscript considers online zeroth order optimization and it develops a gradient estimator based on one query per function. In particular, the proposed method mimics two-point estimators by evaluating two consecutive functions at perturbations of an iterate, as shown in equation (3). Although one-point gradient es... | SP:a13b7c970bfcd4b06913233730bc5a7e1552dd4c |
Boosting One-Point Derivative-Free Online Optimization via Residual Feedback | 1 INTRODUCTION . Zeroth-order optimization ( ZO ) algorithms have been widely used to solve online optimization problems where first or second order information ( i.e. , gradient or Hessian information ) is unavailable at each time instant . Such problems arise , e.g. , in online learning and involve adversarial traini... | The paper considers online optimization with zero-order oracle. Motivated by nonstationarity of the objective function, impracticality is underlined for the two-point feedback approach. Instead, staying in the one-point setting, the proposed approach reuses the objective value from the previous round of observations, w... | SP:a13b7c970bfcd4b06913233730bc5a7e1552dd4c |
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals | 1 INTRODUCTION . The success of modern-day deep learning algorithms in the medical domain has been contingent upon the availability of large , labelled datasets ( Poplin et al. , 2018 ; Tomašev et al. , 2019 ; Attia et al. , 2019 ) . Curating such datasets , however , is a challenge due to the time-consuming nature of ... | The paper proposes an active learning framework called SoCal that is consistency-based and can decide between whether to make use of the oracle to provide a label or to make use of a pseudo-label generated by the algorithm itself instead. The proposed method hopes to address resource-constrained active learning scenari... | SP:962be382d6cbf5cfd5b3406e726ccf0b2a39e049 |
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Cardiac Signals | 1 INTRODUCTION . The success of modern-day deep learning algorithms in the medical domain has been contingent upon the availability of large , labelled datasets ( Poplin et al. , 2018 ; Tomašev et al. , 2019 ; Attia et al. , 2019 ) . Curating such datasets , however , is a challenge due to the time-consuming nature of ... | The authors proposed a consistent-based active learning framework to annotate largely unlabeled physiological signals with the help of human annotators (oracles). The paper is well organized and easy to follow. It is somewhat novel to equipping active learning with consistency learning and selective classification. The... | SP:962be382d6cbf5cfd5b3406e726ccf0b2a39e049 |
Decentralized Knowledge Graph Representation Learning | 1 INTRODUCTION . Knowledge graphs ( KGs ) support many data-driven applications ( Ji et al. , 2020 ) . Recently , learning low-dimensional representations ( a.k.a . embeddings ) of entities and relations in KGs has been increasingly given attentions ( Rossi et al. , 2020 ) . We find that existing models for KG represen... | This paper proposes a "decentralized" method for representation learning in knowledge graphs that doesn't explicitly depend on a learned embedding for the entity node of interest, e_i. Rather, the embedding for e_i is constructed in a distributed fashion (similar in motivation to the distributional hypothesis/skip-gram... | SP:4ceb178b6b3d531512c7740d0fb52a00b7a95f04 |
Decentralized Knowledge Graph Representation Learning | 1 INTRODUCTION . Knowledge graphs ( KGs ) support many data-driven applications ( Ji et al. , 2020 ) . Recently , learning low-dimensional representations ( a.k.a . embeddings ) of entities and relations in KGs has been increasingly given attentions ( Rossi et al. , 2020 ) . We find that existing models for KG represen... | This paper presents a method for knowledge graph embedding based on graph attention networks (GAT). The key idea is to avoid using the information for a node (i.e., its representation vectors) when computing the attention weights for the neighbors of the node. The paper argues that this approach can better generalize t... | SP:4ceb178b6b3d531512c7740d0fb52a00b7a95f04 |
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations | 1 INTRODUCTION As AI systems are increasingly being deployed in scenarios involving high-stakes decisions , the issue of interpretability of their decisions to the humans in the loop has acquired renewed urgency . Most work on interpretability has hither-to focused on one-shot classification tasks , and has revolved ar... | The authors propose a method of explainable AI for inscrutable blackbox models. The explanations build on a set of user-defined primitives, independently trained on the blackbox representation (e.g., visual frames of an Atari game), and use an increasingly popular method of providing contrastive explanations. Two forms... | SP:57cdb30976e9fc5563bbb07a51d90eec8385e594 |
Bridging the Gap: Providing Post-Hoc Symbolic Explanations for Sequential Decision-Making Problems with Inscrutable Representations | 1 INTRODUCTION As AI systems are increasingly being deployed in scenarios involving high-stakes decisions , the issue of interpretability of their decisions to the humans in the loop has acquired renewed urgency . Most work on interpretability has hither-to focused on one-shot classification tasks , and has revolved ar... | This paper presents a novel approach to generate contrastive explanations in a dialogue setting between a human and a planning agent. The setting assumes that the agent generates and offers an optimal plan to the user, and the user in turn challenges the presented plan offering an alternative (i.e. a contrast/foil). Th... | SP:57cdb30976e9fc5563bbb07a51d90eec8385e594 |
Single-Photon Image Classification | 1 INTRODUCTION . Both quantum mechanics and machine learning play a major role in modern technology , and the emerging field of AI applications of quantum computing may well enable major breakthroughs across many scientific disciplines . Yet , as the majority of current machine learning practitioners do not have a thor... | This paper focuses on the quantum computing based machine learning and proposes a toy model to illustrate the quantum information processing. On the common used handwritten digit dataset MNIST, more than 40% images can be classified accurately. The proposed method looks interesting and the focused problem (combining qu... | SP:6b629449901b057a5adea945556494ed61b47e8f |
Single-Photon Image Classification | 1 INTRODUCTION . Both quantum mechanics and machine learning play a major role in modern technology , and the emerging field of AI applications of quantum computing may well enable major breakthroughs across many scientific disciplines . Yet , as the majority of current machine learning practitioners do not have a thor... | The paper identifies two atomic problems, respectively in fields of ML (MNIST classification) and quantum mechanics (measuring a single photon), and brings them together in a simplified setup that uses a single photon emitted according to the spatial distribution of images to classify MNIST/Fashion-MNIST. The introduct... | SP:6b629449901b057a5adea945556494ed61b47e8f |
Can a Fruit Fly Learn Word Embeddings? | 1 INTRODUCTION . Deep learning has made tremendous advances in computer vision , natural language processing and many other areas . While taking high-level inspiration from biology , the current generation of deep learning methods are not necessarily biologically realistic . This raises the question whether biological ... | Although the paper does not say so, my understanding is that the proposed word embedding method actually first perform Kmeans-like clustering on the context vector shown in Figure 2. In the binary word embedding of each word, we set the dimensions corresponding to the k closest cluster centers to 1 and 0 otherwise. Mos... | SP:86ed253fe1f1c48ec1f909f10a5473c4cf27fa83 |
Can a Fruit Fly Learn Word Embeddings? | 1 INTRODUCTION . Deep learning has made tremendous advances in computer vision , natural language processing and many other areas . While taking high-level inspiration from biology , the current generation of deep learning methods are not necessarily biologically realistic . This raises the question whether biological ... | The authors present a formalization of a simple biological network (the mushroom body of the fruit fly), that allows very efficient “biologically inspired” word embeddings. They train this network to generate both static (context-independent) and context dependent embeddings, and evaluate these embeddings using several... | SP:86ed253fe1f1c48ec1f909f10a5473c4cf27fa83 |
Self-supervised Graph-level Representation Learning with Local and Global Structure | 1 INTRODUCTION . Learning informative representations of whole graphs is a fundamental problem in a variety of domains and tasks , such as molecule properties prediction in drug and material discovery ( Gilmer et al. , 2017 ; Wu et al. , 2018 ) , protein function forecast in biological networks ( Alvarez & Yan , 2012 ;... | This paper proposes an unsupervised framework to perform graph representation learning. The local-instance structure is learned by first gets patch-level and graph-level representations for each graph, then maximize the mutual information between both correlated patches and correlated graphs, which are decided by attri... | SP:37732a5c56b1f8ce138ef14d366adc684ef7376c |
Self-supervised Graph-level Representation Learning with Local and Global Structure | 1 INTRODUCTION . Learning informative representations of whole graphs is a fundamental problem in a variety of domains and tasks , such as molecule properties prediction in drug and material discovery ( Gilmer et al. , 2017 ; Wu et al. , 2018 ) , protein function forecast in biological networks ( Alvarez & Yan , 2012 ;... | This paper proposed a method for self-supervised graph-level representation learning. The main idea is to enforce both the instance level smoothness embedding constraints, and a so-called global, semantic grouping structures across all instance graphs in the training data set. To achieve this goal, the authors have ad... | SP:37732a5c56b1f8ce138ef14d366adc684ef7376c |
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Learning control policies for cooperative multi-agent reinforcement learning ( MARL ) remains challenging as agents must search the joint-action space , which grows exponentially with the number of agents . Current state-of-the-art value-based methods such as VDN ( Sunehag et al. , 2017 ) and QMIX ( Ra... | The paper develops and evaluates an algorithm for decision making in the CTDE MARL setting (centralized training and decentralized execution for multiagent reinforcement learning). That is, the concern is how to use closely supervised training to produce agents that can work independently toward a common goal. The prob... | SP:89ffd39dd2f60a2ee0b3e382f3bfde2681405e4d |
UneVEn: Universal Value Exploration for Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Learning control policies for cooperative multi-agent reinforcement learning ( MARL ) remains challenging as agents must search the joint-action space , which grows exponentially with the number of agents . Current state-of-the-art value-based methods such as VDN ( Sunehag et al. , 2017 ) and QMIX ( Ra... | Some popular methods like VDN and QMIX focus on the monotonic factorization of joint-action value function, which is not realistic in non-monotonic cases when the agent’s best action depends on other agents’ actions. This phenomenon is common. For example, in the prisoner’s dilemma the value function can be monotonical... | SP:89ffd39dd2f60a2ee0b3e382f3bfde2681405e4d |
Intention Propagation for Multi-agent Reinforcement Learning | 1 INTRODUCTION . Collaborative multi-agent reinforcement learning is an important sub-field of the multi-agent reinforcement learning ( MARL ) , where the agents learn to coordinate to achieve joint success . It has wide applications in traffic control ( Kuyer et al. , 2008 ) , autonomous driving ( Shalev-Shwartz et al... | The paper considers the cooperative multiagent MARL setting where each agent’s reward depends on the state and the actions of itself and its neighbors The paper has a theoretical claim that, for such reward structure, the optimal maximum entropy joint policy in the form that can be factored into potential functions, on... | SP:243e8027661d500c99d0e2633726895f32141b9e |
Intention Propagation for Multi-agent Reinforcement Learning | 1 INTRODUCTION . Collaborative multi-agent reinforcement learning is an important sub-field of the multi-agent reinforcement learning ( MARL ) , where the agents learn to coordinate to achieve joint success . It has wide applications in traffic control ( Kuyer et al. , 2008 ) , autonomous driving ( Shalev-Shwartz et al... | The paper proposes a scalable approach via intention propagation to learn a multi-agent RL algorithm using communication in a structured environment. An agent encodes its policy and sends the “intention” to the neighboring agents with the assumption that only the closest agents would be the affected by it. The approach... | SP:243e8027661d500c99d0e2633726895f32141b9e |
Simple deductive reasoning tests and numerical data sets for exposing limitation of today's deep neural networks | 1 INTRODUCTION . Deductive reasoning is a branch of artificial intelligence where inferences are represented as assertions or facts over data ( Khemani , 2013 ) . Starting with a set of given facts , the system combines facts based on rules to generate newer facts and update the knowledge store . On the other hand mach... | The paper argues that deductive reasoning is an open problem in current machine learning scenarios where features are learned rather than hand-crafted. To highlight the limitations of current approaches, the paper proposes a benchmark suite of 10 simple tasks (finding the minimum, divisibility test, etc.) that are triv... | SP:487be1dac03389a08da54338075aa5970f8c3588 |
Simple deductive reasoning tests and numerical data sets for exposing limitation of today's deep neural networks | 1 INTRODUCTION . Deductive reasoning is a branch of artificial intelligence where inferences are represented as assertions or facts over data ( Khemani , 2013 ) . Starting with a set of given facts , the system combines facts based on rules to generate newer facts and update the knowledge store . On the other hand mach... | This paper's contribution is introducing a set of tasks and datasets that require deductive approaches as opposed to common induction-based models. The paper tackles an important and interesting problem that helps to shape the future of the neuro-symbolic research area. My main concern however is, the paper ignores and... | SP:487be1dac03389a08da54338075aa5970f8c3588 |
Growing Efficient Deep Networks by Structured Continuous Sparsification | 1 INTRODUCTION . Deep neural networks are the dominant approach to a variety of machine learning tasks , including image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ) , object detection ( Girshick , 2015 ; Liu et al. , 2016 ) , semantic segmentation ( Long et al. , 2015 ; Chen et al. , 2017 ... | This paper proposes a novel NAS method that searches the model architectures by grows the networks. This searching strategy determines the channel and layer configurations by assigning a binary learnable parameter m for each channel or layer. The objective is to optimize a trade-off between the model performance on the... | SP:01d9dfdff7250ca7703a05aa98105d0307f3d899 |
Growing Efficient Deep Networks by Structured Continuous Sparsification | 1 INTRODUCTION . Deep neural networks are the dominant approach to a variety of machine learning tasks , including image classification ( Krizhevsky et al. , 2012 ; Simonyan & Zisserman , 2015 ) , object detection ( Girshick , 2015 ; Liu et al. , 2016 ) , semantic segmentation ( Long et al. , 2015 ; Chen et al. , 2017 ... | This paper proposes a new principled approach to growing deep network architectures based on continuous relaxation of discrete structure optimization combined with a sparse subnetwork sampling scheme. It starts from a simple seed architecture and dynamically grows/prunes both the layers and filters during training. Thr... | SP:01d9dfdff7250ca7703a05aa98105d0307f3d899 |
Learning and Evaluating Representations for Deep One-Class Classification | 1 INTRODUCTION . One-class classification aims to identify if an example belongs to the same distribution as the training data . There are several applications of one-class classification , such as anomaly detection or outlier detection , where we learn a classifier that distinguishes the anomaly/outlier data without a... | This paper proposes an anomaly detection approach that has two stages: a first stage for learning a feature representation and a second stage to train either a one-class classifier based on OC-SVM or KDE. The main contribution of the paper is the feature representation learning that relies on contrastive learning to o... | SP:bf144506fc556d0587b7c2a24e1284dcd69f7c26 |
Learning and Evaluating Representations for Deep One-Class Classification | 1 INTRODUCTION . One-class classification aims to identify if an example belongs to the same distribution as the training data . There are several applications of one-class classification , such as anomaly detection or outlier detection , where we learn a classifier that distinguishes the anomaly/outlier data without a... | This paper proposes a framework for deep one-class classification (an example application being anomaly detection). The basic idea is to combine self-supervised representation learning (eg through a proxy task such as rotation prediction or contrastive learning), with a classical approach to one-class classification, ... | SP:bf144506fc556d0587b7c2a24e1284dcd69f7c26 |
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium | 1 INTRODUCTION . Recently , reinforcement learning ( RL ) has achieved remarkable success in many practical sequential decision problems , such as Go ( Silver et al. , 2017 ) , chess ( Silver et al. , 2018 ) , real-time strategy games ( Vinyals et al. , 2019 ) , etc . In real-world , many sequential decision problems i... | This paper considers a case in the cooperative multiagent reinforcement learning where one single agent can behave adversarially. It claims that the performance of the whole MARL system would deteriorate significantly and demonstrate this phenomenon both theoretically and empirically. Then the author proposes a solut... | SP:9c05898952af007c390c3a6cc385746daad29e65 |
Robust Multi-Agent Reinforcement Learning Driven by Correlated Equilibrium | 1 INTRODUCTION . Recently , reinforcement learning ( RL ) has achieved remarkable success in many practical sequential decision problems , such as Go ( Silver et al. , 2017 ) , chess ( Silver et al. , 2018 ) , real-time strategy games ( Vinyals et al. , 2019 ) , etc . In real-world , many sequential decision problems i... | The paper addresses the issue of robustness in cooperative multi-agent RL setups, where the inclusion at test time of an agent that makes error or is even adversarial can drastically decrease performance. The main idea is to compute a correlated equilibrium, by allowing all agents policies to depend on a common signal.... | SP:9c05898952af007c390c3a6cc385746daad29e65 |
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning | 1 INTRODUCTION . The mixture-of-experts method ( MOE ) ( Jacobs et al. , 1991a ) is testified to be capable of improving the generalisation ability of reinforcement learning ( RL ) agents ( Hausknecht & Stone , 2016a ; Peng et al. , 2016 ; Neumann et al. ) . Among these methods , the Gaussian Mixture Models ( GMM ) are... | The paper studies the problem of differentiating through the policy return when the policy is a Gaussian mixture model. The main contribution of the paper is a heuristic approach for computing this gradient. Having defined the policy update, the authors integrate it to two RL algorithms: PPO and SAC. The experiments sh... | SP:f27bf5238c835413eff4edb3315386543b0aad6c |
Probabilistic Mixture-of-Experts for Efficient Deep Reinforcement Learning | 1 INTRODUCTION . The mixture-of-experts method ( MOE ) ( Jacobs et al. , 1991a ) is testified to be capable of improving the generalisation ability of reinforcement learning ( RL ) agents ( Hausknecht & Stone , 2016a ; Peng et al. , 2016 ; Neumann et al. ) . Among these methods , the Gaussian Mixture Models ( GMM ) are... | The paper focuses on the policy architecture of deep reinforcement learning algorithms. Specifically, the authors apply the probabilistic mixture-of-experts (PMOE) model in the policy of a reinforcement learning agent, where each primitive is a unimodal Gaussian distribution and the gating model is a simple state-condi... | SP:f27bf5238c835413eff4edb3315386543b0aad6c |
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech | 1 INTRODUCTION . End-to-end text-to-speech ( TTS ) systems have recently attracted much attention , as neural TTS models began to generate high-quality speech that is very similar to the human voice ( Sotelo et al. , 2017 ; Wang et al. , 2017 ; Shen et al. , 2018 ; Ping et al. , 2018 ; Li et al. , 2019 ) . Typically , ... | This paper presents BVAE-TTS, which applies hierarchical VAEs (using an approach motivated by NVAE and Ladder VAEs) to the problem of parallel TTS. The main components of the system are a dot product-based attention mechanism that is used during training to produce phoneme duration targets for the parallel duration pr... | SP:99b3ac117a5c787653031eb169f0104a8594c088 |
Bidirectional Variational Inference for Non-Autoregressive Text-to-Speech | 1 INTRODUCTION . End-to-end text-to-speech ( TTS ) systems have recently attracted much attention , as neural TTS models began to generate high-quality speech that is very similar to the human voice ( Sotelo et al. , 2017 ; Wang et al. , 2017 ; Shen et al. , 2018 ; Ping et al. , 2018 ; Li et al. , 2019 ) . Typically , ... | Neural models that autoregressively generate mel spectrograms from text (or phonemes), such as Tacotron, have been used to generate high quality synthetic speech. However, they suffer from slow inference speed due to their autoregressive nature. To alleviate this, non-autoregressive models have been proposed, such as F... | SP:99b3ac117a5c787653031eb169f0104a8594c088 |
Task-Agnostic Morphology Evolution | 1 INTRODUCTION . Recently , deep reinforcement learning has shown impressive success in continuous control problems across a wide range of environments ( Schulman et al. , 2017 ; Barth-Maron et al. , 2018 ; Haarnoja et al. , 2018 ) . The performance of these algorithms is usually measured via the reward achieved by a p... | This paper develops a general morphology evolution algorithm, and demonstrates its utility in a setting where morphologies are encoded as graphs. The methodology is grounded in a theoretical notion of empowerment, and theory is introduced that extends empowerment to the case of morphology. The morphology evolution itse... | SP:c137c12d6f9dfed77ea6b51e05ef79aa8ac2a987 |
Task-Agnostic Morphology Evolution | 1 INTRODUCTION . Recently , deep reinforcement learning has shown impressive success in continuous control problems across a wide range of environments ( Schulman et al. , 2017 ; Barth-Maron et al. , 2018 ; Haarnoja et al. , 2018 ) . The performance of these algorithms is usually measured via the reward achieved by a p... | The paper introduces an algorithm for optimizing the robot morphology in a simulated environment. The key idea is that instead of finding a morphology and a controller for a specific task, they propose to search for a morphology that can reach a large variety of states in a predictable way. Specifically, they developed... | SP:c137c12d6f9dfed77ea6b51e05ef79aa8ac2a987 |
ACT: Asymptotic Conditional Transport | 1 INTRODUCTION . Measuring the difference between two probability distributions is a fundamental problem in statistics and machine learning ( Cover , 1999 ; Bishop , 2006 ; Murphy , 2012 ) . A variety of statistical distances have been proposed to quantify the difference , which often serves as the first step to build ... | The paper proposes a new transport-based divergence between distributions (CT) and a variant for empirical distributions (ACT). The new divergence is claimed to be more suitable for learning deep generative models than existing divergences like KL, JS (as in the vanilla GAN) and Wasserstein (as used in WGAN and its var... | SP:6f5d5acd8b55cc8dd01355d65adf10ea96ae7944 |
ACT: Asymptotic Conditional Transport | 1 INTRODUCTION . Measuring the difference between two probability distributions is a fundamental problem in statistics and machine learning ( Cover , 1999 ; Bishop , 2006 ; Murphy , 2012 ) . A variety of statistical distances have been proposed to quantify the difference , which often serves as the first step to build ... | The paper proposes conditional transport as a new divergence to measure the difference between two distributions. The idea is to learn the conditional transport plan of transporting one point in one distribution to the other marginal distribution. This conditional transport plan is modeled using a neural network. The r... | SP:6f5d5acd8b55cc8dd01355d65adf10ea96ae7944 |
Domain Generalization with MixStyle | 1 INTRODUCTION . Key to automated understanding of digital images is to compute a compact and informative feature representation . Deep convolutional neural networks ( CNNs ) have demonstrated remarkable ability in representation learning , proven to be effective in many visual recognition tasks , such as classifying p... | This work proposes a technique for domain generalization by mixing style of images from different domains. This work adopts a mix up style approach [A] for domain generalization. Different from [A], the paper proposes to conduct mix-up in the intermediate layers, in particular, instance normalization layers. The propos... | SP:b9ce6d4e7451388efdd7cbacca24053f7fa73ab3 |
Domain Generalization with MixStyle | 1 INTRODUCTION . Key to automated understanding of digital images is to compute a compact and informative feature representation . Deep convolutional neural networks ( CNNs ) have demonstrated remarkable ability in representation learning , proven to be effective in many visual recognition tasks , such as classifying p... | This paper proposed a simple regularization technique for domain generalization tasks, termed MixStyle, based on the observation that domains are determined by image styles. By mixing styles of different instances, which generates synthesized domain samples while preserving the content features, the proposed method ach... | SP:b9ce6d4e7451388efdd7cbacca24053f7fa73ab3 |
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms | Machine learning models are being used extensively in many important areas , but there is no guarantee that a model will always perform well or as its developers intended . Understanding the correctness of a model is crucial to prevent potential failures that may have significant detrimental impact in critical applicat... | The paper proposes an active testing approach that actively selects test instances to estimate the performance of a (black box) machine learning model. The key idea is to train a Bayesian Neural Networks (BNN) with a small amount of labeled test data and evaluate how well the model-under-test agrees with the BNN on sam... | SP:31b3653cae47c7e33f5379141951ea37a8c98a79 |
ALT-MAS: A Data-Efficient Framework for Active Testing of Machine Learning Algorithms | Machine learning models are being used extensively in many important areas , but there is no guarantee that a model will always perform well or as its developers intended . Understanding the correctness of a model is crucial to prevent potential failures that may have significant detrimental impact in critical applicat... | The authors have proposed using an active learning approach to estimate evaluation metrics for a given model. The approach learns a sampling function that decides which observations need to be labeled, which are then fed to a Bayesian neural network (BNN) that aims to estimate the distribution Y|X. The authors select w... | SP:31b3653cae47c7e33f5379141951ea37a8c98a79 |
Defining Benchmarks for Continual Few-Shot Learning | 1 INTRODUCTION . Two capabilities vital for an intelligent agent with finite memory are few-shot learning , the ability to learn from a handful of data-points , and continual learning , the ability to sequentially learn new tasks without forgetting previous ones . Taken individually these two areas have recently seen d... | This paper proposes a benchmark for a new task called continual few-shot learning. The benchmark is based on the ImageNet dataset. Basically, the model looks at a part of the support set one after another sequentially, and it is then evaluated on the query set that contains balanced samples from each part of the suppor... | SP:d4fd62a9542f068cffd3aabe30ae8dd7991284e2 |
Defining Benchmarks for Continual Few-Shot Learning | 1 INTRODUCTION . Two capabilities vital for an intelligent agent with finite memory are few-shot learning , the ability to learn from a handful of data-points , and continual learning , the ability to sequentially learn new tasks without forgetting previous ones . Taken individually these two areas have recently seen d... | This paper proposes a new machine learning setting called “Continual Few-Shot Learning” which fuses the up until now disparate paradigms of continual learning and few-shot learning. To evaluate methods in this new setting, a new benchmark and dataset called SlimageNet64 are defined. Various methods are evaluated on the... | SP:d4fd62a9542f068cffd3aabe30ae8dd7991284e2 |
MultiModalQA: complex question answering over text, tables and images | When answering complex questions , people can seamlessly combine information from visual , textual and tabular sources . While interest in models that reason over multiple pieces of evidence has surged in recent years , there has been relatively little work on question answering models that reason across multiple modal... | The paper introduces MultiModalQA, a dataset that requires joint reasoning over table, text and images. The dataset has been created in a semi-automatic way through Wikipedia tables, the Wikientities in them, their related images and related textual question answer pairs from known text QA datasets. For the collection ... | SP:f8b6ecb2877d258f483cf38966828974ad39f2e8 |
MultiModalQA: complex question answering over text, tables and images | When answering complex questions , people can seamlessly combine information from visual , textual and tabular sources . While interest in models that reason over multiple pieces of evidence has surged in recent years , there has been relatively little work on question answering models that reason across multiple modal... | The authors present a new dataset, MultiModalQA, with the intent of measuring a model’s ability to reason across different modalities (free text, structured tables, and images) in question answering, and in which a large percentage of the questions requires cross-modal reasoning. The authors provide a detailed look at ... | SP:f8b6ecb2877d258f483cf38966828974ad39f2e8 |
Legendre Deep Neural Network (LDNN) and its application for approximation of nonlinear Volterra–Fredholm–Hammerstein integral equations | 1 INTRODUCTION . Deep neural networks are a main and beneficial part of machine learning family which are applied in various areas including speech processing , computer vision , natural language processing and image processing ( LeCun et al. , 2015 ; Krizhevsky et al. , 2012 ) . Also , the approximation of the functio... | The paper proposed the Legendre Deep Neural Network (LDNN) to solve Volterra–Fredholm–Hammerstein integral equations. Specifically, the network uses Legendre polynomials as the activation in the first layer and uses Gaussian quadrature to discretize the integral operator as a summation. The numerical examples are perfo... | SP:aa0febaec3494ea69264d71d33081759037e5319 |
Legendre Deep Neural Network (LDNN) and its application for approximation of nonlinear Volterra–Fredholm–Hammerstein integral equations | 1 INTRODUCTION . Deep neural networks are a main and beneficial part of machine learning family which are applied in various areas including speech processing , computer vision , natural language processing and image processing ( LeCun et al. , 2015 ; Krizhevsky et al. , 2012 ) . Also , the approximation of the functio... | The authors present a neural network based method to solve a special class of integral equations. Their approach involves training a neural network with Legendre polynomial based activation functions to approximate the solution $y(x)$ for a given $x$. The network is trained in a supervised fashion to minimize a loss fu... | SP:aa0febaec3494ea69264d71d33081759037e5319 |
Representing Partial Programs with Blended Abstract Semantics | 1 INTRODUCTION . Inductive program synthesis – the problem of inferring programs from examples – offers the promise of building machine learning systems that are interpretable , generalize quickly , and allow us automate software engineering tasks . In recent years , neurally-guided program synthesis , which uses deep ... | This paper proposes a novel top-down program synthesis for programming-by-example which combines concrete evaluation with neural embeddings. The authors take inspiration from abstract execution, which can execute partial programs by abstractly representing sets of possible execution states. Instead of hand-designing an... | SP:4544639f7c0d43d2b79ce9bd8ba5e723cefe9ffd |
Representing Partial Programs with Blended Abstract Semantics | 1 INTRODUCTION . Inductive program synthesis – the problem of inferring programs from examples – offers the promise of building machine learning systems that are interpretable , generalize quickly , and allow us automate software engineering tasks . In recent years , neurally-guided program synthesis , which uses deep ... | This paper proposes an embedding mechanism for partial programs for search space exploration in example-driven synthesis. It executes a sub-expression concretely whenever possible and applies neural module networks on vector representations otherwise. The embeddings of partial programs and goal states are used for dete... | SP:4544639f7c0d43d2b79ce9bd8ba5e723cefe9ffd |
Adaptive Learning Rates for Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Recently , multi-agent reinforcement learning ( MARL ) has been applied to decentralized cooperative systems , e.g. , autonomous driving ( Shalev-Shwartz et al. , 2016 ) , smart grid control ( Yang et al. , 2018 ) , and traffic signal control ( Wei et al. , 2019 ) . Many MARL methods ( Lowe et al. , 20... | This paper proposed AdaMa, which can automatically use adaptive learning rates for each agent in cooperative Multi-Agent Reinforcement Learning (MARL). AdaMa calculated the learning rate of each actor and critic according to their contributions of locally increasing value functions. Simple experiments using toy exampl... | SP:2c24186f9710a15d58c6af94828b80ae796af3f9 |
Adaptive Learning Rates for Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Recently , multi-agent reinforcement learning ( MARL ) has been applied to decentralized cooperative systems , e.g. , autonomous driving ( Shalev-Shwartz et al. , 2016 ) , smart grid control ( Yang et al. , 2018 ) , and traffic signal control ( Wei et al. , 2019 ) . Many MARL methods ( Lowe et al. , 20... | The paper proposes a new algorithm for multi-agent reinforcement learning (MARL) that adaptively picks learning rates for actor and critic. Specifically, the learning rates are updated to directions maximally affecting the Q-function, and the algorithm dynamically balances the learning rates between actor and critic. I... | SP:2c24186f9710a15d58c6af94828b80ae796af3f9 |
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning | 1 INTRODUCTION . Sequence-to-Sequence ( Seq2Seq ) learning ( Sutskever et al. , 2014 ) has advanced the state of the art in various natural language processing ( NLP ) tasks , such as machine translation ( Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ; Wu et al. , 2019 ) , text summarization ( Wang et al. , 2019b ; Z... | This is an interesting idea where the authors propose "SurfaceFusion", where they use the source embeddings learned by the encoder to modulate the output of the decoder at the final layer. The authors claim this is because the embeddings contain valuable information that is lost during encoder processing because the en... | SP:c33d55dadd5fe4399b85968375ddffdeaf64ad61 |
Understanding and Improving Encoder Layer Fusion in Sequence-to-Sequence Learning | 1 INTRODUCTION . Sequence-to-Sequence ( Seq2Seq ) learning ( Sutskever et al. , 2014 ) has advanced the state of the art in various natural language processing ( NLP ) tasks , such as machine translation ( Bahdanau et al. , 2015 ; Vaswani et al. , 2017 ; Wu et al. , 2019 ) , text summarization ( Wang et al. , 2019b ; Z... | The authors perform a thorough analysis of encoder fusion for Transformers: which encoder layer should the N-th decoder layer attend to? It turns out that the final decoder layers often attend to the encoder embeddings, leading the authors to provide them to the last decoder layer which leads to small improvements of p... | SP:c33d55dadd5fe4399b85968375ddffdeaf64ad61 |
Motion Forecasting with Unlikelihood Training | 1 INTRODUCTION . For robotic applications deployed in the real world , the ability to foresee the future motions of agents in the surrounding environment plays an essential role for safe and intelligent decision making . This is a very challenging task . For example , in the autonomous driving domain , to predict nearb... | The present paper considers the problem of context integration in probabilistic agent trajectory predictors, particularly Trajectron++. It starts with the observation that these predictors often do a bad job at considering non-drivable areas in their predictions even if context information is injected as part of the in... | SP:f485de73661d59efd25025ddf9778652edb306c1 |
Motion Forecasting with Unlikelihood Training | 1 INTRODUCTION . For robotic applications deployed in the real world , the ability to foresee the future motions of agents in the surrounding environment plays an essential role for safe and intelligent decision making . This is a very challenging task . For example , in the autonomous driving domain , to predict nearb... | In this paper, the authors focus on vehicular motion forecasting on roadways. To this end, they propose an interesting tweak to existing approaches. In addition to maximizing the likelihood of ground truth trajectories, the authors consider an "unlikelihood" weighted subloss which penalizes sections of the event space ... | SP:f485de73661d59efd25025ddf9778652edb306c1 |
Learning Hyperbolic Representations of Topological Features | 1 INTRODUCTION . Persistent homology is a topological data analysis tool which tracks how topological features ( e.g . connected components , cycles , cavities ) appear and disappear as we analyze the data at different scales or in nested sequences of subspaces ( 1 ; 2 ) . A nested sequence of subspaces is known as a f... | In this paper, the authors proposed a new representation of persistence diagrams that can include `''essential features''. Essential features correspond to the intrinsic topology of the underlying space that will not die during the filtration. To include the fact that the essential features are infinitely far from othe... | SP:95ba08c326437452098f9cc7d8b542a08bb747a3 |
Learning Hyperbolic Representations of Topological Features | 1 INTRODUCTION . Persistent homology is a topological data analysis tool which tracks how topological features ( e.g . connected components , cycles , cavities ) appear and disappear as we analyze the data at different scales or in nested sequences of subspaces ( 1 ; 2 ) . A nested sequence of subspaces is known as a f... | The authors propose to learn a representation for the persistence diagram (PD) in the hyperbolic space to incorporate the essential features (i.e., infinite persistence). The authors show that the hyperbolic representation has stability. Empirically, the authors illustrate that the hyperbolic representation for PD comp... | SP:95ba08c326437452098f9cc7d8b542a08bb747a3 |
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing | 1 INTRODUCTION . Computer vision has made revolutionary advances in recent years by leveraging a combination of deep neural network architectures with abundant high-quality perceptual data . One of the transformative applications of computational perception is autonomous driving , with autonomous cars and trucks alread... | The paper proposes an end-to-end differentiable method for finding adversarial patterns to be added to the environment for autonomous driving. It utilizes image composition with homography thus it can compose the adversarial pattern into the image frames of all image frames of a driving sequence. Combined with a neural... | SP:1b685c4f7f4b3f02bda928ec42ae68d43d0e2668 |
Finding Physical Adversarial Examples for Autonomous Driving with Fast and Differentiable Image Compositing | 1 INTRODUCTION . Computer vision has made revolutionary advances in recent years by leveraging a combination of deep neural network architectures with abundant high-quality perceptual data . One of the transformative applications of computational perception is autonomous driving , with autonomous cars and trucks alread... | This paper proposes a scalable and efficient approach for finding adversarial physical modification to the video inputs of autonomous driving. Assuming the perturbations are in form of a collection of several rectangles, the model parameterizes the physical modifications. By simply ignoring the closed-loop of viewpoint... | SP:1b685c4f7f4b3f02bda928ec42ae68d43d0e2668 |
Rethinking Attention with Performers | 1 INTRODUCTION AND RELATED WORK . Transformers ( Vaswani et al. , 2017 ; Dehghani et al. , 2019 ) are powerful neural network architectures that have become SOTA in several areas of machine learning including natural language processing ( NLP ) ( e.g . speech recognition ( Luo et al. , 2020 ) ) , neural machine transla... | The authors propose to use the kernel feature map self-attention formulation introduced in [1] to efficiently approximate the softmax attention. The main contribution of the paper lies in the proposed _positive random features_ that can approximate softmax with a strictly positive feature map without which the training... | SP:cb35385634bc2ba2381921b491176a5309e754dd |
Rethinking Attention with Performers | 1 INTRODUCTION AND RELATED WORK . Transformers ( Vaswani et al. , 2017 ; Dehghani et al. , 2019 ) are powerful neural network architectures that have become SOTA in several areas of machine learning including natural language processing ( NLP ) ( e.g . speech recognition ( Luo et al. , 2020 ) ) , neural machine transla... | The paper proposed a theoretically grounded O(N) approximation of the softmax attention. The key idea is to interpret attention as a kernel function and construct the random feature projection that can reproduce this kernel. It is highly non-trivial to derive a feature mapping that can accurately approximate the softma... | SP:cb35385634bc2ba2381921b491176a5309e754dd |
Aligning AI With Shared Human Values | 1 INTRODUCTION . Embedding ethics into AI systems remains an outstanding challenge without any concrete proposal . In popular fiction , the “ Three Laws of Robotics ” plot device illustrates how simplistic rules can not encode the complexity of human values ( Asimov , 1950 ) . Some contemporary researchers argue machin... | The authors present a large and thoroughly constructed dataset, containing various types of data points, spanning major aspect of ethics. The dataset is constructed based on deep and “old” human understanding of ethical concepts, taking into consideration more modern aspects of building datasets, such as adversarial fi... | SP:d2a7acc9e746f3db643d59d854b2bc91b6a6a35e |
Aligning AI With Shared Human Values | 1 INTRODUCTION . Embedding ethics into AI systems remains an outstanding challenge without any concrete proposal . In popular fiction , the “ Three Laws of Robotics ” plot device illustrates how simplistic rules can not encode the complexity of human values ( Asimov , 1950 ) . Some contemporary researchers argue machin... | I appreciate the work the authors did by collecting a large dataset that can be used as a benchmark of ethical assessment across different moral concepts. The strong side of this work is its connection to the well-established ethical theories and a careful design and discussion of potential limitations of the dataset (... | SP:d2a7acc9e746f3db643d59d854b2bc91b6a6a35e |
Learning Energy-Based Models by Diffusion Recovery Likelihood | 1 INTRODUCTION . EBMs ( LeCun et al. , 2006 ; Ngiam et al. , 2011 ; Kim & Bengio , 2016 ; Zhao et al. , 2016 ; Goyal et al. , 2017 ; Xie et al. , 2016b ; Finn et al. , 2016 ; Gao et al. , 2018 ; Kumar et al. , 2019 ; Nijkamp et al. , 2019b ; Du & Mordatch , 2019 ; Grathwohl et al. , 2019 ; Desjardins et al. , 2011 ; Ga... | The paper proposed a novel method to train EBMs based on diffusion recovery likelihood. It constructs a sequence of noisy version of data and learn a conditional between consecutive noisy pairs. Compare to working with the likelihood directly, doing so makes the training much easier. Besides, even using a potentially n... | SP:d6970df559439a15ce1d3573e9c9eabe0a6b10d7 |
Learning Energy-Based Models by Diffusion Recovery Likelihood | 1 INTRODUCTION . EBMs ( LeCun et al. , 2006 ; Ngiam et al. , 2011 ; Kim & Bengio , 2016 ; Zhao et al. , 2016 ; Goyal et al. , 2017 ; Xie et al. , 2016b ; Finn et al. , 2016 ; Gao et al. , 2018 ; Kumar et al. , 2019 ; Nijkamp et al. , 2019b ; Du & Mordatch , 2019 ; Grathwohl et al. , 2019 ; Desjardins et al. , 2011 ; Ga... | This paper describes training a sequence of conditional EBMs (inspired by Ho et al. (2020)) instead of training unconditional EBMs. Each conditional energy describes the probability of recovering x, given its noisy version \hat{x}. The noisy version of x can be described as a normal distribution centered at x, so the... | SP:d6970df559439a15ce1d3573e9c9eabe0a6b10d7 |
Learning a unified label space | 2 1 INTRODUCTION . Computer vision aims to produce broad , general-purpose perception systems that work in the wild . Yet object detection is fragmented into datasets ( Lin et al. , 2014 ; Neuhold et al. , 2017 ; Shao et al. , 2019 ; Kuznetsova et al. , 2020 ) and our models are locked into specific domains . This frag... | The paper proposes to learn object detection model, while training on different datasets with different, potentially overlapping, label spaces. While previous methods do the label space mapping, from each dataset specific label space to the common universal label space, manually, this paper proposes to learn such mappi... | SP:2eba253ff91a7543c5269403292f66cb93b68a8d |
Learning a unified label space | 2 1 INTRODUCTION . Computer vision aims to produce broad , general-purpose perception systems that work in the wild . Yet object detection is fragmented into datasets ( Lin et al. , 2014 ; Neuhold et al. , 2017 ; Shao et al. , 2019 ; Kuznetsova et al. , 2020 ) and our models are locked into specific domains . This frag... | The main idea of the proposed work is to learn a universal label space for a given task (say object detection) and a set of different datasets with semantically overlapping labels. The only supervision required by the approach is constituted by the single dataset label spaces and respective annotations. Each dataset l... | SP:2eba253ff91a7543c5269403292f66cb93b68a8d |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings | 1 INTRODUCTION . Cross-lingual word embeddings ( CLWE ) provide a shared representation space for knowledge transfer between languages , yielding state-of-the-art performance in many cross-lingual natural language processing ( NLP ) tasks . Most of the previous works have focused on aligning static embeddings . To util... | This paper proposes the alignment of cross-lingual contextual embeddings not just at the word level, but at the sense level. It does this by relying purely on unaligned, unlabeled monolingual corpora used for pre-training, along with bilingual lexica. It does this by adapting the LM objective to be a sense-aware cross ... | SP:643501d344b4a7404916431d0a56aba58c354e79 |
Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings | 1 INTRODUCTION . Cross-lingual word embeddings ( CLWE ) provide a shared representation space for knowledge transfer between languages , yielding state-of-the-art performance in many cross-lingual natural language processing ( NLP ) tasks . Most of the previous works have focused on aligning static embeddings . To util... | This paper proposes to introduce multiple senses into pre-trained models. The proposed method selects senses dynamically while pretraining the model and applies a sense-aware cross-entropy loss for pretraining. This paper further proposes to jointly pre-train a sense-aware cross-lingual model with sense-level translati... | SP:643501d344b4a7404916431d0a56aba58c354e79 |
Higher-order Structure Prediction in Evolving Graph Simplicial Complexes | 1 INTRODUCTION . Numerous types of networks like social ( Liben-Nowell & Kleinberg , 2007a ) , biological ( Airoldi et al. , 2006 ) , and chemical reaction networks ( Wegscheider , 1911 ) are highly dynamic , as they evolve and grow rapidly via the appearance of new interactions , represented as the introduction of new... | This paper presents an estimator that predict higher-order structure in time-varying graphs. The authors present an kernel-based estimator, prove that it is consistent when the indicator variable for whether a particular (d+1)-dimensional simplex is Bernoulli distributed with a function g. The authors prove that their ... | SP:7d5ca500bb1f17d91c8261ad94af85335278686a |
Higher-order Structure Prediction in Evolving Graph Simplicial Complexes | 1 INTRODUCTION . Numerous types of networks like social ( Liben-Nowell & Kleinberg , 2007a ) , biological ( Airoldi et al. , 2006 ) , and chemical reaction networks ( Wegscheider , 1911 ) are highly dynamic , as they evolve and grow rapidly via the appearance of new interactions , represented as the introduction of new... | This paper provide a method for high-order structure prediction problem. Specifically, the paper first defines a high-order structure on graphs named graph simplicial complex (GSC). Then the paper introduces a feature generation method used for the high-order structures. The features are also used in the proposed metho... | SP:7d5ca500bb1f17d91c8261ad94af85335278686a |
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithm has demonstrated its worth in a series of challenging sequential decision making and control tasks , which train policies to optimize a single scalar reward function ( Mnih et al. , 2015 ; Silver et al. , 2016 ; Haarnoja et al. , 2018 ; Hwangbo et al. , 2019 ) . ... | The paper proposes a robust multi-objective RL approach and a non-linear utility metric to enforce an accurate and evenly distributed representation of the Pareto frontier. Robustness is obtained by formulating the problem as a two-player zero-sum game. The goal of the main agent is thus to learn the policies on the Pa... | SP:d629a2e1996688c91a5294e702eb12b11370eed4 |
Approximating Pareto Frontier through Bayesian-optimization-directed Robust Multi-objective Reinforcement Learning | 1 INTRODUCTION . Reinforcement learning ( RL ) algorithm has demonstrated its worth in a series of challenging sequential decision making and control tasks , which train policies to optimize a single scalar reward function ( Mnih et al. , 2015 ; Silver et al. , 2016 ; Haarnoja et al. , 2018 ; Hwangbo et al. , 2019 ) . ... | This paper seeks to train multi-objective RL policies that are robust to environmental uncertainties. There are two main contributions: a novel approach to solve this problem, and a novel metric to evaluate Pareto fronts. The metric combines the typical hypervolume metric (that captures the quality/performance of a Par... | SP:d629a2e1996688c91a5294e702eb12b11370eed4 |
Denoising Diffusion Implicit Models | 1 INTRODUCTION . Deep generative models have demonstrated the ability to produce high quality samples in many domains ( Karras et al. , 2020 ; van den Oord et al. , 2016a ) . In terms of image generation , generative adversarial networks ( GANs , Goodfellow et al . ( 2014 ) ) currently exhibits higher sample quality th... | This paper develops a variant (DDIM) of an existing method (DDPM) with the goal of accelerating it greatly while still maintaining performance. The authors are working in the context of a denoising process that runs in the reverse direction to a sequence of steps that each add a small amount of Gaussian noise to the or... | SP:e73541ff1e010add393fde5023555c06b4b4d443 |
Denoising Diffusion Implicit Models | 1 INTRODUCTION . Deep generative models have demonstrated the ability to produce high quality samples in many domains ( Karras et al. , 2020 ; van den Oord et al. , 2016a ) . In terms of image generation , generative adversarial networks ( GANs , Goodfellow et al . ( 2014 ) ) currently exhibits higher sample quality th... | This paper proposes a change to the recently popular diffusion models, motivated by increasing the speed of sampling. This is accomplished by changing the “forward” process which adds noise to the data. In the original diffusion models, this forward process is a Markov process whose marginals and conditionals can be co... | SP:e73541ff1e010add393fde5023555c06b4b4d443 |
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks | Training Graph Convolutional Networks ( GCNs ) is expensive as it needs to aggregate data recursively from neighboring nodes . To reduce the computation overhead , previous works have proposed various neighbor sampling methods that estimate the aggregation result based on a small number of sampled neighbors . Although ... | This paper proposed a new distributed training method for GNNs. Specifically, unlike traditional distributed training methods for CNNs where data points are independent, nodes in a graph are dependent on each other. Thus, this dependence incurs communication between different workers in the distributed training of GNNs... | SP:d643be475992d1e14394cb6200b8db3d2b07c34f |
Communication-Efficient Sampling for Distributed Training of Graph Convolutional Networks | Training Graph Convolutional Networks ( GCNs ) is expensive as it needs to aggregate data recursively from neighboring nodes . To reduce the computation overhead , previous works have proposed various neighbor sampling methods that estimate the aggregation result based on a small number of sampled neighbors . Although ... | The paper presents a sampling-based approach to speeding-up training of GCNs in distributed systems. The key step in this task involves exchanging and aggregating messages sent along the edges of the graph. If the nodes of the graph are partitioned between several machines, then exchanging those messages involve costly... | SP:d643be475992d1e14394cb6200b8db3d2b07c34f |
Planning from Pixels using Inverse Dynamics Models | 1 INTRODUCTION . Deep reinforcement learning has proven to be a powerful and effective framework for solving a diversity of challenging decision-making problems ( Silver et al. , 2017a ; Berner et al. , 2019 ) . However these algorithms are typically trained to maximize a single reward function , ignoring information t... | The author proposes Goal-Conditioned Latent Action Models for RL (GLAMOR) a novel approach to learn latent world models by modeling inverse dynamics. The proposed approach learns to track task-relevant dynamics for a diverse distribution of tasks and provide a strong heuristic that enables efficient planning. GLAMOR de... | SP:0884761fc60276d6d151552118758b07cd50a24e |
Planning from Pixels using Inverse Dynamics Models | 1 INTRODUCTION . Deep reinforcement learning has proven to be a powerful and effective framework for solving a diversity of challenging decision-making problems ( Silver et al. , 2017a ; Berner et al. , 2019 ) . However these algorithms are typically trained to maximize a single reward function , ignoring information t... | The paper proposes a model-based reinforcement learning method. The method builds a partial model of the environment through learning inverse dynamics, which is the distribution of action sequences that would bring one state to another state. Through training the model with an iterative relabeling scheme, the model is ... | SP:0884761fc60276d6d151552118758b07cd50a24e |
On the Stability of Multi-branch Network | 1 INTRODUCTION . Multi-branch architecture is a building block in state-of-the-art neural network models for many tasks , e.g. , the ResNeXt ( Xie et al. , 2017 ) for computer vision and the Transformer ( Vaswani et al. , 2017 ) for machine translation . It has been pointed out that the benefit of multi-branch architec... | This paper studies the training of multi-branch networks, i.e. networks formed by linearly combining multiple disjoint branches of the same architecture. The core contribution in this paper is the “STAM” aggregation rule which is to set the combination coefficient to $1/\sqrt{C}$ for a network with $C$ branches. This a... | SP:b57dd473377d8ec5ef38f2cebd1f83a847270b27 |
On the Stability of Multi-branch Network | 1 INTRODUCTION . Multi-branch architecture is a building block in state-of-the-art neural network models for many tasks , e.g. , the ResNeXt ( Xie et al. , 2017 ) for computer vision and the Transformer ( Vaswani et al. , 2017 ) for machine translation . It has been pointed out that the benefit of multi-branch architec... | This study focuses on the stability of multi-branch networks. It analyzes the forward and backward stability of multi-branch network, and builds the relations with some widely-adopted initialization and normalization schemes. A simple new aggregation method is proposed that enjoys better stability than the sum and aver... | SP:b57dd473377d8ec5ef38f2cebd1f83a847270b27 |
RMSprop converges with proper hyper-parameter | 1 INTRODUCTION . RMSprop ( Tieleman & Hinton , 2012 ) remains one of the most popular algorithms for machine learning applications . As a non-momentum version of a more general algorithm Adam , RMSprop ’ s good empirical performance has been well acknowledged by practitioners in generative adversarial networks ( GANs )... | The paper starts off from the recent realization that there exists divergent examples for any set of hyperparameters for algorithms in the Adam family, such as RMSProp. It sets out to study the effect of the beta2 parameter on convergence for a fixed specific problem. The analysis shows that there exists a beta2 < 1 th... | SP:fed001660e9a62c1bb55a1a5500f8b27ab40f348 |
RMSprop converges with proper hyper-parameter | 1 INTRODUCTION . RMSprop ( Tieleman & Hinton , 2012 ) remains one of the most popular algorithms for machine learning applications . As a non-momentum version of a more general algorithm Adam , RMSprop ’ s good empirical performance has been well acknowledged by practitioners in generative adversarial networks ( GANs )... | This work revisits a famous counterexample on the convergence of Adam (originally presented in Reddi 2018). The authors show that, if the EMA parameter beta2 in RMSprop and Adam is chosen high enough, then both methods converge to a bounded region in the stochastic setting. In addition, the authors provide some results... | SP:fed001660e9a62c1bb55a1a5500f8b27ab40f348 |
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | 1 INTRODUCTION . Federated learning ( FL ) is a framework for learning statistical models from heterogeneous data scattered across multiple entities ( or clients ) under the coordination of a central server that has no direct access to the local data ( Kairouz et al. , 2019 ) . To learn models without any data transfer... | The authors propose a new method of generating local (client) updates in Federated Learning (FL), where the clients return an adjusted version of their usual local updates to the server. The authors derive this new local update rigorously from the viewpoint of estimating the posterior distribution of the data (under Ga... | SP:6389ff57423090975659dbcd572192bd48f9c3b5 |
Federated Learning via Posterior Averaging: A New Perspective and Practical Algorithms | 1 INTRODUCTION . Federated learning ( FL ) is a framework for learning statistical models from heterogeneous data scattered across multiple entities ( or clients ) under the coordination of a central server that has no direct access to the local data ( Kairouz et al. , 2019 ) . To learn models without any data transfer... | This paper introduces a new perspective on federated learning through the lens of posterior inference. The paper designs a computation- and communication-efficient posterior inference algorithm—federated posterior averaging (FEDPA), which generalizes FedAvg. FEDPA is compared with the strong baselines in Reddi et al. (... | SP:6389ff57423090975659dbcd572192bd48f9c3b5 |
Learning the Pareto Front with Hypernetworks | We describe an approach to PFL implemented using HyperNetworks , which we term Pareto HyperNetworks ( PHNs ) . PHN learns the entire Pareto front simultaneously using a single hypernetwork , which receives as input a desired preference vector and returns a Pareto-optimal model whose loss vector is in the desired ray . ... | The paper proposes a method for multi-objective optimization. The key idea is to learn the entire Pareto front at once by training a hypernetwork that takes preference vector as an inputs and outputs network parameters, which corresponds to a point on the Pareto set with the desired trade-off specified by the preferenc... | SP:7e51fa9afc6a36b771f966b8f615449dab0191bf |
Learning the Pareto Front with Hypernetworks | We describe an approach to PFL implemented using HyperNetworks , which we term Pareto HyperNetworks ( PHNs ) . PHN learns the entire Pareto front simultaneously using a single hypernetwork , which receives as input a desired preference vector and returns a Pareto-optimal model whose loss vector is in the desired ray . ... | This paper tracks the problem of learning the entire Pareto front to allow the user to select a desired Pareto optimal solution by one inference procedure without retraining the model. The high-level idea is to learn the entire Pareto front simultaneously using a single hyper network, which receives as input the desire... | SP:7e51fa9afc6a36b771f966b8f615449dab0191bf |
Semi-Supervised Learning via Clustering Representation Space | 1 INTRODUCTION . Labeling data is expensive . Thus , it is often hard for us to get enough labeled samples . Thus , semi-supervised learning ( Chapelle et al. , 2009 ) becomes a serious issue . People try to get good performance with limited labeled data and a large amount of unlabeled data . When having a limited amou... | This paper attempts to address the semi-supervised learning topic by proposing a method based on an aggregated loss considering both cross-entry and Davies-Bouldin Index. Cross-entropy is used to ensure the maximum margin between classes and Davies-Bouldin Index is applied to the labeled data and to the whole dataset, ... | SP:5d2c22e82721397371999020d145c432fd6e7a42 |
Semi-Supervised Learning via Clustering Representation Space | 1 INTRODUCTION . Labeling data is expensive . Thus , it is often hard for us to get enough labeled samples . Thus , semi-supervised learning ( Chapelle et al. , 2009 ) becomes a serious issue . People try to get good performance with limited labeled data and a large amount of unlabeled data . When having a limited amou... | The authors propose a novel loss function for semi-supervised learning. Arguing that SOTA semi-supervised learning methods neglect spatial information (latent clustering structure) in the data, the authors propose a loss function which combines clustering objectives with classification objectives. The proposed loss fun... | SP:5d2c22e82721397371999020d145c432fd6e7a42 |
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining | 1 INTRODUCTION . Out-of-distribution ( OOD ) detection has become an indispensable part of building reliable open-world machine learning models ( Amodei et al. , 2016 ) . An OOD detector determines whether an input is from the same distribution as the training data , or a different distribution ( i.e. , out-of-distribu... | In this paper the authors propose a method for training a classifier to be more effective at OOD (out of distribution) detection. Many OOD detection methods work by utilizing an auxiliary dataset as examples of OOD-ness. This is the approach taken in this paper and OOD is trained as being a k+1 classification class. W... | SP:56ffc50ee9fad6bf28dc34d87e8fc42cf56fdc0f |
Informative Outlier Matters: Robustifying Out-of-distribution Detection Using Outlier Mining | 1 INTRODUCTION . Out-of-distribution ( OOD ) detection has become an indispensable part of building reliable open-world machine learning models ( Amodei et al. , 2016 ) . An OOD detector determines whether an input is from the same distribution as the training data , or a different distribution ( i.e. , out-of-distribu... | 1. The paper presents a lot of theory but insufficient evidence. It only employs limited image data (SVHN, CIFAR variants). The paper should be clear that the scope is limited to well-known image datasets only. This is because the approach is dependent on auxiliary data which is available for the image datasets. It is ... | SP:56ffc50ee9fad6bf28dc34d87e8fc42cf56fdc0f |
Sparsifying Networks via Subdifferential Inclusion | 1 INTRODUCTION . Deep neural networks have evolved to the state-of-the-art techniques in a wide array of applications : computer vision ( Simonyan & Zisserman , 2015 ; He et al. , 2016 ; Huang et al. , 2017 ) , automatic speech recognition ( Hannun et al. , 2014 ; Dong et al. , 2018 ; Li et al. , 2019 ; Watanabe et al.... | The paper propose a network compression algorithm by exploiting a reformulation of activation function as proximity operator. The latter is an optimization problem whose optimality condition reveals constraints on the weight matrix W of the neural net. The main idea is then to "biasedly" select W as a minimizer of a sp... | SP:797a59091f5ce57f264400b8fa7e0b485584338c |
Sparsifying Networks via Subdifferential Inclusion | 1 INTRODUCTION . Deep neural networks have evolved to the state-of-the-art techniques in a wide array of applications : computer vision ( Simonyan & Zisserman , 2015 ; He et al. , 2016 ; Huang et al. , 2017 ) , automatic speech recognition ( Hannun et al. , 2014 ; Dong et al. , 2018 ; Li et al. , 2019 ; Watanabe et al.... | In this paper the authors propose a new model compression method based on subdifferential inclusion. The key idea is to make the outputs of the neurons in the sparse and dense networks at the same input close enough. They rewrite the activation function as the proximity operator of a proper convex function and finally ... | SP:797a59091f5ce57f264400b8fa7e0b485584338c |
Exploiting structured data for learning contagious diseases under incomplete testing | One of the ways that machine learning algorithms can help control the spread of an infectious disease is by building models that predict who is likely to get infected making them good candidates for preemptive interventions . In this work we ask : can we build reliable infection prediction models when the observed data... | This paper formulates the contagious disease into a missing label problem with dependence between each data point. The paper targets an important problem, especially in this pandemic, and the effort is greatly appreciated. However, the writing of this paper is confusing and it makes it hard to catch the main contributi... | SP:b3210d565f51a3a5ea729ffa7e99e1727bd65cdd |
Exploiting structured data for learning contagious diseases under incomplete testing | One of the ways that machine learning algorithms can help control the spread of an infectious disease is by building models that predict who is likely to get infected making them good candidates for preemptive interventions . In this work we ask : can we build reliable infection prediction models when the observed data... | In this work, the authors propose an approach, MIINT, for identifying infected individuals using a network-based approach. They also suggest two key properties, potency and similarity among groups, which impact the efficacy of MIINT and similar approaches. A detailed simulation framework is used to compare MIINT to rel... | SP:b3210d565f51a3a5ea729ffa7e99e1727bd65cdd |
ARELU: ATTENTION-BASED RECTIFIED LINEAR UNIT | 1 INTRODUCTION . Activation functions , introducing nonlinearities to artificial neural networks , is essential to networks ’ expressivity power and learning dynamics . Designing activation functions that facilitate fast training of accurate deep neural networks is an active area of research ( Maas et al. , 2013 ; Good... | This work presents a novel learned activation function called Attention-based Rectified Linear Unit (AReLU). Element-wise attention module is developed that learns a sign-based attention (ELSA) which is the novel component of AReLU towards mitigating the gradient vanishing issue. Extensive experiments and analyses have... | SP:e36c7de37059ea0fe7ed64fb32926adfd76b30c1 |
ARELU: ATTENTION-BASED RECTIFIED LINEAR UNIT | 1 INTRODUCTION . Activation functions , introducing nonlinearities to artificial neural networks , is essential to networks ’ expressivity power and learning dynamics . Designing activation functions that facilitate fast training of accurate deep neural networks is an active area of research ( Maas et al. , 2013 ; Good... | In this paper, the authors proposed a new activation function called AReLU which introduces an attention mechanism to the original ReLU function. Based on this new activation function, the output will be adaptively adjusted by the two learnable parameters \alpha and \beta. This kind of adaptive adjustment can be though... | SP:e36c7de37059ea0fe7ed64fb32926adfd76b30c1 |
Unbiased Learning with State-Conditioned Rewards in Adversarial Imitation Learning | 1 INTRODUCTION . Inverse reinforcement learning ( IRL ) is an algorithm of recovering the ground truth reward function from observed behavior ( Ng & Russell , 2000 ) . IRL algorithms—followed by appropriate reinforcement learning ( RL ) algorithms—can optimize policy through farsighted cumulative value measures in the ... | The line of reasoning and analysis followed in the paper is mostly sound. The paper claims that the use of (state-action) occupancy measure make IL and IRL methods brittle due to the high variance of these measures and their inability to transfer to other domains. These two claims are neither properly defined and groun... | SP:08d227e9382cb5eb359462f2e75cca62f3457cf0 |
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