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Adaptive Extra-Gradient Methods for Min-Max Optimization and Games | 1 Introduction The surge of recent breakthroughs in generative adversarial networks ( GANs ) [ 20 ] , robust reinforcement learning [ 41 ] , and other adversarial learning models [ 27 ] has sparked renewed interest in the theory of min-max optimization problems and games . In this broad setting , it has become empirica... | This work proposed a stepsize for the extragradient/mirror-prox method that works both in smooth and non-smooth settings. The stepsize is based on the empirical values of gradient/operator differences, and mirror maps are used to allow for non-euclidian geometry such as KL divergence. The paper offers us three converge... | SP:c5d1720922dfde389abbce3110a7f049e972192a |
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework | 1 INTRODUCTION . Transfer Learning has shown great potential to accelerate single-agent RL via leveraging prior knowledge from past learned policies of relevant tasks ( Yin & Pan , 2017 ; Yang et al. , 2020 ) . Inspired by this , transfer learning in multiagent reinforcement learning ( MARL ) ( Claus & Boutilier , 1998... | This paper proposed an option-based framework for multiple agents to share knowledge with each other in the same MARL task. For scalability and robustness, two variants of the framework are designed, including 1) a global option advisor, which has the access to the global information of the environment; 2) local option... | SP:6bf54a72a9f670d7c47a134440b73b2c3e07ee91 |
Transfer among Agents: An Efficient Multiagent Transfer Learning Framework | 1 INTRODUCTION . Transfer Learning has shown great potential to accelerate single-agent RL via leveraging prior knowledge from past learned policies of relevant tasks ( Yin & Pan , 2017 ; Yang et al. , 2020 ) . Inspired by this , transfer learning in multiagent reinforcement learning ( MARL ) ( Claus & Boutilier , 1998... | The paper proposes a new option-based policy transfer framework for multi-agent reinforcement learning (MARL) called MAOPT. By framing multi-agent transfer as an option learning problem, MAOPT methods are able to learn when to give advice to agents and when to stop it. Authors provide a version of MOAPT for fully coope... | SP:6bf54a72a9f670d7c47a134440b73b2c3e07ee91 |
C-Learning: Horizon-Aware Cumulative Accessibility Estimation | 1 INTRODUCTION . Multi-goal reinforcement learning tackles the challenging problem of reaching multiple goals , and as a result , is an ideal framework for real-world agents that solve a diverse set of tasks . Despite progress in this field ( Kaelbling , 1993 ; Schaul et al. , 2015 ; Andrychowicz et al. , 2017 ; Ghosh ... | The paper proposes C-learning, which is an essentially a horizon aware Q-learning. In a nutshell, the authors proposes changing the Q function from Q(s, a) to C(s, a, h) where s is the state, a is the action and h is allowed time horizon i.e. the agent should get to the goal state using less than h states. Since C can ... | SP:f32600d6223672363a45f0797dd6be29e6fd491d |
C-Learning: Horizon-Aware Cumulative Accessibility Estimation | 1 INTRODUCTION . Multi-goal reinforcement learning tackles the challenging problem of reaching multiple goals , and as a result , is an ideal framework for real-world agents that solve a diverse set of tasks . Despite progress in this field ( Kaelbling , 1993 ; Schaul et al. , 2015 ; Andrychowicz et al. , 2017 ; Ghosh ... | The paper highlights a problem in existing goal-reaching RL agents, in that they do not explicitly allow for trading off speed (how fast you reach the goal) and reliability (how often you reach the goal). While this tradeoff is implicitly determined by the discount factor in training, the paper asserts that in practice... | SP:f32600d6223672363a45f0797dd6be29e6fd491d |
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play | 1 INTRODUCTION . This paper is concerned with the problem of multi-agent reinforcement learning ( multi-agent RL ) , in which multiple agents learn to make decisions in an unknown environment in order to maximize their ( own ) cumulative rewards . Multi-agent RL has achieved significant recent success in traditionally ... | This paper studies learning in stochastic games, which are extensions of Markov decision processes (MDPs) from the single-agent setup to the multi-agent one. Here the objective of each learner is to optimize her own reward function. Similarly to the case of MDPs, here one can devise learning algorithms with controlled ... | SP:e4db134ad2f3217ae370cb58399efc86047166f7 |
A Sharp Analysis of Model-based Reinforcement Learning with Self-Play | 1 INTRODUCTION . This paper is concerned with the problem of multi-agent reinforcement learning ( multi-agent RL ) , in which multiple agents learn to make decisions in an unknown environment in order to maximize their ( own ) cumulative rewards . Multi-agent RL has achieved significant recent success in traditionally ... | The authors introduce new algorithms to solve two-players zero-sum Markov games, as well as two-players Markov game in the reward-free setting. The approach is model-based, based on successive episods of planning and counting for updating the model estimate. It involves solving a matrix game at each iteration, looking ... | SP:e4db134ad2f3217ae370cb58399efc86047166f7 |
Adapt-and-Adjust: Overcoming the Long-tail Problem of Multilingual Speech Recognition | 1 INTRODUCTION . Deploying a single Automatic Speech Recognition ( ASR ) model to recognize multiple languages is highly desired but very challenging for real-world multilingual ASR scenarios due to the wellknown long-tailed distribution challenge , namely , that some resource-rich languages like English have abundant ... | This paper addresses multi-lingual speech synthesis, where one ASR model is responsible for recognizing speech in multiple languages. In this example the authors look at 11 languages with between 80 and 4 hours of training data. The "long-tail problem" (which isn't clearly stated) that this work is addressing is that... | SP:85884c827deebdf6be8feacefde4800e4837b55a |
Adapt-and-Adjust: Overcoming the Long-tail Problem of Multilingual Speech Recognition | 1 INTRODUCTION . Deploying a single Automatic Speech Recognition ( ASR ) model to recognize multiple languages is highly desired but very challenging for real-world multilingual ASR scenarios due to the wellknown long-tailed distribution challenge , namely , that some resource-rich languages like English have abundant ... | The paper proposes three additions to improve a monolithic multilingual end-to-end ASR system. The problem of training a monolithic multilingual ASR system is that using data from multiple languages does not necessary improve over individual monolingual systems. The three additions are a large multilingual language mod... | SP:85884c827deebdf6be8feacefde4800e4837b55a |
More or Less: When and How to Build Convolutional Neural Network Ensembles | 1 INTRODUCTION . Scaling capacity of deep learning models . Convolutional neural network models are becoming as accurate as humans on perceptual tasks . They are now used in numerous and diverse applications such as drug discovery , data compression , and automating gameplay . These models increasingly grow in size wit... | This paper establish a robust and holistic framework to compare scaling up an ensemble with scaling up a single networks, where test accuracy, number of paramaters, inference time, memory consumption and training time to converge are considered. To reduce the intractably large design space of scaling up an ensemble, t... | SP:934cb790cd96e5a81539938b05d63d0dcb82df0a |
More or Less: When and How to Build Convolutional Neural Network Ensembles | 1 INTRODUCTION . Scaling capacity of deep learning models . Convolutional neural network models are becoming as accurate as humans on perceptual tasks . They are now used in numerous and diverse applications such as drug discovery , data compression , and automating gameplay . These models increasingly grow in size wit... | This paper addresses when to use a single network model vs an ensemble of convolutional neural network models based on resource budgets. The authors challenge the notion that ensemble methods should only be used when resources are a non-issue. The authors compare single networks to width-equivalent and depth-equivalent... | SP:934cb790cd96e5a81539938b05d63d0dcb82df0a |
To be Robust or to be Fair: Towards Fairness in Adversarial Training | 1 INTRODUCTION . The existence of adversarial examples ( Goodfellow et al. , 2014 ; Szegedy et al. , 2013 ) causes huge concerns when applying deep neural networks on safety-critical tasks , such as autonomous driving vehicles and face identification ( Morgulis et al. , 2019 ; Sharif et al. , 2016 ) . These adversarial... | The authors study adversarially trained classifiers and observe that the accuracy discrepancy between classes is larger than that of standard models. They then propose a theoretical model where this phenomenon provably arises. Finally, they propose a method to reduce the (standard and robust) accuracy discrepancy betwe... | SP:29537439a3017e0d6982b9b819dd83ea0c3b20ab |
To be Robust or to be Fair: Towards Fairness in Adversarial Training | 1 INTRODUCTION . The existence of adversarial examples ( Goodfellow et al. , 2014 ; Szegedy et al. , 2013 ) causes huge concerns when applying deep neural networks on safety-critical tasks , such as autonomous driving vehicles and face identification ( Morgulis et al. , 2019 ; Sharif et al. , 2016 ) . These adversarial... | This paper introduces a fairness perspective on accuracy performance among distinct classes in the context of adversarial training. It makes an observation that adversarial training algorithms (Madry et al. 2017, Zhang et al. 2019) yield biased performances on CIFAR 10. It also offers a theoretical study under a Gaussi... | SP:29537439a3017e0d6982b9b819dd83ea0c3b20ab |
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks | 1 INTRODUCTION . Temporal networks consider dynamically interacting elements as nodes , interactions as temporal links , with labels of when those interactions happen . Such temporal networks provide abstractions to study many real-world dynamic systems ( Holme & Saramäki , 2012 ) . Researchers have investigated tempor... | The authors provide in-depth analysis on the critical topic of capturing dynamic laws for the inductive representation learning of temporal graphs. The authors leverage the causal anonymous walk to capture the topological laws of the dynamic graph, while not requiring to memorize node identities such that inductive lea... | SP:de83ec082fee45976ef980f33e068a32da3fdcd9 |
Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks | 1 INTRODUCTION . Temporal networks consider dynamically interacting elements as nodes , interactions as temporal links , with labels of when those interactions happen . Such temporal networks provide abstractions to study many real-world dynamic systems ( Holme & Saramäki , 2012 ) . Researchers have investigated tempor... | This paper proposes Causal Anonymous Walks (CAWs) that are extracted by temporal random walks and work as automatic retrieval of temporal network motifs to represent network dynamics while avoiding the time-consuming selection and counting of those motifs. CAWs adopt an anonymization strategy that replaces node identit... | SP:de83ec082fee45976ef980f33e068a32da3fdcd9 |
Deep Partial Updating | 1 INTRODUCTION . To deploy deep neural networks ( DNNs ) on resource-constrained edge devices , extensive research has been done to compress a well-trained model via pruning ( Han et al. , 2016 ; Renda et al. , 2020 ) and quantization ( Courbariaux et al. , 2015 ; Rastegari et al. , 2016 ) . During on-device inference ... | In this paper, the authors have proposed a new approach to determine the optimized subset of weights instead of simply conduct full weights updating. In order to better update the weights, they measure each weight's contribution to the analytical upper bound on the loss reduction from two sides (global and locally). Af... | SP:41db1e50777ea9db3c15ce7d62a2fc50925abe5b |
Deep Partial Updating | 1 INTRODUCTION . To deploy deep neural networks ( DNNs ) on resource-constrained edge devices , extensive research has been done to compress a well-trained model via pruning ( Han et al. , 2016 ; Renda et al. , 2020 ) and quantization ( Courbariaux et al. , 2015 ; Rastegari et al. , 2016 ) . During on-device inference ... | This paper presents a method to reduce the bandwidth required to update DNN models on edge devices. The key insight is that model updates typically incorporate new data (training samples), and that after doing so, a minority of weights capture the majority of change due to retraining. The authors propose a method by... | SP:41db1e50777ea9db3c15ce7d62a2fc50925abe5b |
BOIL: Towards Representation Change for Few-shot Learning | 1 INTRODUCTION . Meta-learning , also known as “ learning to learn , ” is a methodology that imitates human intelligence that can adapt quickly with even a small amount of previously unseen data through the use of previous learning experiences . To this aim , meta-learning with deep neural networks has mainly been stud... | This paper proposes a variant of MAML, called body only in the inner loop (BOIL), in which the output layer is not trained (learning rate is set to 0, always) but gradients are still backpropagated to the remaining layers, which are trained as in MAML. In essence, this is the inverse of the ANIL method of Raghu et al.,... | SP:be6df295f535480586a951445b824142bb60b56e |
BOIL: Towards Representation Change for Few-shot Learning | 1 INTRODUCTION . Meta-learning , also known as “ learning to learn , ” is a methodology that imitates human intelligence that can adapt quickly with even a small amount of previously unseen data through the use of previous learning experiences . To this aim , meta-learning with deep neural networks has mainly been stud... | Previous work studied MAML and showed that representation reuse is the main contributing factor in performance and not representation change. This paper first asks the question of whether representation reuse is enough for meta-learning? The paper hypothesizes and empirically evaluates the need/benefit for representati... | SP:be6df295f535480586a951445b824142bb60b56e |
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning | 1 INTRODUCTION In this paper , we address the problem of spatiotemporal object-centric reasoning in videos . Specifically , we focus on the problem of object permanence , which is the ability to represent the existence and the trajectory of hidden moving objects ( Baillargeon , 1986 ) . Object permanence can be essenti... | This paper introduces an architecture (Multi-Hop Transformer) for spatio-temporal reasoning in video, focusing on a localisation task for scenes where the object of interest is often occluded (Snitch Localisation task in CATER). The model extracts objects using an external object-detector and predicts objects’ trajecto... | SP:fa3f5e47eea572915c94134222535f4f48b2fe83 |
Hopper: Multi-hop Transformer for Spatiotemporal Reasoning | 1 INTRODUCTION In this paper , we address the problem of spatiotemporal object-centric reasoning in videos . Specifically , we focus on the problem of object permanence , which is the ability to represent the existence and the trajectory of hidden moving objects ( Baillargeon , 1986 ) . Object permanence can be essenti... | This paper proposes a multi-hop transformer method for the video-based object permanence task. The proposed method performs multi-hop reasoning via the encoder-decoder architecture of transformers over critical frames in the video. To mitigate the problem of lacking ground truth for the middle hops, the paper proposes ... | SP:fa3f5e47eea572915c94134222535f4f48b2fe83 |
Contrastive Video Textures | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) and Variational Autoencoders ( VAEs ) ( Kingma & Welling , 2013 ) have achieved great success in generating images “ from scratch ” . While one might have hoped that video generation would be a simple extension of image-generation me... | In this paper, the authors proposed a non-parametric approach for video generation, i.e., video frame (un)conditional resampling. The proposed method is inspired by Video Textures (Sch¨odl et al., 2000), which synthesizes new videos by stitching together snippets of an existing video. Comparing to existing 'video textu... | SP:bafcf085e146530fbae6b30f7966f413eb003df6 |
Contrastive Video Textures | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) ( Goodfellow et al. , 2014 ) and Variational Autoencoders ( VAEs ) ( Kingma & Welling , 2013 ) have achieved great success in generating images “ from scratch ” . While one might have hoped that video generation would be a simple extension of image-generation me... | of this paper: In this work, the authors propose a method to learn to generate long-range video sequences. The general idea is starting from a prior work (Video Textures) and extending this work with a learning framework. Specifically, during training a model is used to learn the transition probability between differen... | SP:bafcf085e146530fbae6b30f7966f413eb003df6 |
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation | 1 INTRODUCTION . Loss functions are of indispensable components in training deep networks , as they drive the feature learning process for various applications with specific evaluation metrics . However , most metrics , like the commonly used 0-1 classification error , are non-differentiable in their original forms and... | This paper aims to directly optimize the metrics of semantic segmentation tasks, such as mIoU, which is different from the most existing methods which minimize the cross-entropy as a proxy. The metrics typically contain one-hot labels and logical operations. In order to directly optimize them, the authors first relax t... | SP:e6b2bd6e602c95d5f0e17ea55e966da9955bd718 |
Auto Seg-Loss: Searching Metric Surrogates for Semantic Segmentation | 1 INTRODUCTION . Loss functions are of indispensable components in training deep networks , as they drive the feature learning process for various applications with specific evaluation metrics . However , most metrics , like the commonly used 0-1 classification error , are non-differentiable in their original forms and... | 1.) In comparison with traditional loss function such as Cross-Entropy, WCE, DPCE, and SSIM, the proposed method achieves competitive performance. In addition, the authors also compare with the searched loss functions such as searched mIoU, searched FWIoU, etc. By combining the searched mIoU with the BIoU/BFI surrogate... | SP:e6b2bd6e602c95d5f0e17ea55e966da9955bd718 |
Revisiting Few-sample BERT Fine-tuning | 1 INTRODUCTION . Fine-tuning self-supervised pre-trained models has significantly boosted state-of-the-art performance on natural language processing ( NLP ) tasks ( Liu , 2019 ; Yang et al. , 2019a ; Wadden et al. , 2019 ; Zhu et al. , 2020 ; Guu et al. , 2020 ) . One of the most effective models for this process is B... | Large language models (LM) architectures, such as BERT, XLNet, etc., are not generally trained from scratch, but rather used as pretrained models. Among all, BERT is one of the most widely used ones, and its use on downstream tasks mainly consists on a stage of fine-tuning, where the new layers added are trained, and t... | SP:9a71fc1f596ef67bf5228d779246f8e9ae04c8e0 |
Revisiting Few-sample BERT Fine-tuning | 1 INTRODUCTION . Fine-tuning self-supervised pre-trained models has significantly boosted state-of-the-art performance on natural language processing ( NLP ) tasks ( Liu , 2019 ; Yang et al. , 2019a ; Wadden et al. , 2019 ; Zhu et al. , 2020 ; Guu et al. , 2020 ) . One of the most effective models for this process is B... | This paper investigates fine-tuning BERT for few-sample datasets. Notably, the authors find debiasing omission in BERT-adam. They find original debiased adam is better than BERT-adam. Besides, they also find re-initializing top layers can speed up learning and achieve better performance. These two findings are interest... | SP:9a71fc1f596ef67bf5228d779246f8e9ae04c8e0 |
On Nondeterminism and Instability in Neural Network Optimization | 1 INTRODUCTION . Consider this common scenario : you have a baseline “ current best ” model , and are trying to improve it . Now , one of your experiments has produced a model whose metrics are slightly better than the baseline . Yet you have your reservations — how do you know the improvement is “ real ” , and not due... | The paper investigates the effect of nondeterminism and stability in Neural Networks (NNs) for supervised learning tasks in a systematic manner. The paper is very well-written. All the steps towards the claims of the paper are clearly stated. The empirical analysis is systematic and the two main results are thought pro... | SP:40ed38eb25d29057685c22a2118da3f7d269535b |
On Nondeterminism and Instability in Neural Network Optimization | 1 INTRODUCTION . Consider this common scenario : you have a baseline “ current best ” model , and are trying to improve it . Now , one of your experiments has produced a model whose metrics are slightly better than the baseline . Yet you have your reservations — how do you know the improvement is “ real ” , and not due... | This paper sheds light on the impact of nondeterminism to the run-to-run variability of neural network performance---a situation many people using neural networks have experienced. The authors establish an experimental strategy to analyze the different sources of nondeterminism. Some sources of nondeterminism are param... | SP:40ed38eb25d29057685c22a2118da3f7d269535b |
Learning to Share in Multi-Agent Reinforcement Learning | 1 INTRODUCTION . In multi-agent reinforcement learning ( MARL ) , there are multiple agents interacting with the environment via their joint action to cooperatively optimize an objective . Many methods of centralized training and decentralized execution ( CTDE ) have been proposed for cooperative MARL , such as VDN ( S... | The paper addresses multi-agent RL problems by presenting a decentralized approach where the agents learn to share their reward with their neighbors. In this method, a high-level policy determines a weight vector for weighting the reward of neighboring agents, and then each agent learns their own independent policy. Th... | SP:3c7efb6ff61e1589c76a8bbcb156cd6d4fb6b4af |
Learning to Share in Multi-Agent Reinforcement Learning | 1 INTRODUCTION . In multi-agent reinforcement learning ( MARL ) , there are multiple agents interacting with the environment via their joint action to cooperatively optimize an objective . Many methods of centralized training and decentralized execution ( CTDE ) have been proposed for cooperative MARL , such as VDN ( S... | The paper present a new method, called LToS which enables agents to share rewards in MARL. Two levels of policies, high-level and low-level, determines rewards and optimize global objectives. Three diverse scenarios were used to test the performance of LToS compared to other baseline methods. LToS consistently outperfo... | SP:3c7efb6ff61e1589c76a8bbcb156cd6d4fb6b4af |
Adaptive Procedural Task Generation for Hard-Exploration Problems | 1 INTRODUCTION . The effectiveness of reinforcement learning ( RL ) relies on the agent ’ s ability to explore the task environment and collect informative experiences . Given tasks handcrafted with human expertise , RL algorithms have achieved significant progress on solving sequential decision making problems in vari... | This paper tackles the problem of facilitating RL agents' learning in sparse reward, hard-exploration problems. The authors approached this challenge by generating a curriculum of tasks needed to finish the originally assigned task. Though using other auxiliary tasks to assist RL training has been heatedly discussed, t... | SP:e0600fd9c60fc02e37e332e608673682687f0190 |
Adaptive Procedural Task Generation for Hard-Exploration Problems | 1 INTRODUCTION . The effectiveness of reinforcement learning ( RL ) relies on the agent ’ s ability to explore the task environment and collect informative experiences . Given tasks handcrafted with human expertise , RL algorithms have achieved significant progress on solving sequential decision making problems in vari... | This paper presents a procedurally content generation approach (APT-Gen) that generates a sequence of tasks for an agent to solve. These tasks are automatically generated in a way that helps an RL agent to learn hard-exploration problems. A main innovation of the approach is a task generator system that is both rewarde... | SP:e0600fd9c60fc02e37e332e608673682687f0190 |
Max-sliced Bures Distance for Interpreting Discrepancies | We propose the max-sliced Bures distance , a lower bound on the max-sliced Wasserstein-2 distance , to identify the instances associated with the maximum discrepancy between two samples . The max-slicing can be decomposed into two asymmetric divergences each expressed in terms of an optimal slice or equivalently a ‘ wi... | This paper proposes a sliced version of the Bures distance, which is a lower bound on the 2-Wasserstein distance. The purpose behind this is to identify instances that are have the highest contribution towards the discrepancy between two distributions. But compared to other sliced OT distances, this one operates on a t... | SP:d154782d1a48802582244b6aa037e8483f58ef19 |
Max-sliced Bures Distance for Interpreting Discrepancies | We propose the max-sliced Bures distance , a lower bound on the max-sliced Wasserstein-2 distance , to identify the instances associated with the maximum discrepancy between two samples . The max-slicing can be decomposed into two asymmetric divergences each expressed in terms of an optimal slice or equivalently a ‘ wi... | This paper studies a family of integral probability metric (IPM) divergence on Hilbert spaces. This family can be characterized by the choice of the witness function, and specific witness function may give rise to the Bures distance, the MMD, Wasserstein, as well as many sliced variants. While this family has been well... | SP:d154782d1a48802582244b6aa037e8483f58ef19 |
ItNet: iterative neural networks for fast and efficient anytime prediction | 1 INTRODUCTION . For massively-parallel hardware accelerators ( Schemmel et al. , 2010 ; Merolla et al. , 2014 ; Yao et al. , 2020 ; Graphcore , 2020a ) , every neuron and synapse in the network model has its physical counterpart on the hardware system . Usually , by design , memory and computation is not separated any... | This paper studies homogenious networks, which is defined by the paper as networks that reuse building blocks with shared or different weights multiple times during the inference of the network. During the inference, the network iteratively use the same set of blocks to process input feature maps with different resolut... | SP:a96e9c050813608f1e198a8b6cdce1a6724060bd |
ItNet: iterative neural networks for fast and efficient anytime prediction | 1 INTRODUCTION . For massively-parallel hardware accelerators ( Schemmel et al. , 2010 ; Merolla et al. , 2014 ; Yao et al. , 2020 ; Graphcore , 2020a ) , every neuron and synapse in the network model has its physical counterpart on the hardware system . Usually , by design , memory and computation is not separated any... | This paper proposes a homogeneous network structure for semantic segmentation, which optimizes for prediction accuracy, latency as well as memory footprint. The paper studies anytime prediction setting and designs a re-usable single building block to reduce the memory footprint. Experimental results on CamVid data sho... | SP:a96e9c050813608f1e198a8b6cdce1a6724060bd |
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? | 1 INTRODUCTION Neural networks achieve high predictive accuracy in many tasks , but they are known to have two substantial weaknesses : First , neural networks are not robust against adversarial perturbations , i.e. , semantically meaningless input changes that lead to wrong predictions ( Szegedy et al. , 2014 ; Goodfe... | This manuscript addresses an important question of how Drichlet-based uncertainty (DBU) measures can be used to quantify robustness to adversarial label attacks. Robustness to OOD samples of these models were already shown in the papers they were proposed but this work differs from them in using adversarial samples as ... | SP:d856b17ff19142ea30fcb687ee8a911b135fc3f5 |
Evaluating Robustness of Predictive Uncertainty Estimation: Are Dirichlet-based Models Reliable? | 1 INTRODUCTION Neural networks achieve high predictive accuracy in many tasks , but they are known to have two substantial weaknesses : First , neural networks are not robust against adversarial perturbations , i.e. , semantically meaningless input changes that lead to wrong predictions ( Szegedy et al. , 2014 ; Goodfe... | The paper focuses on quantifying uncertainty for classification problems using Dirichlet based uncertainty (DBU) estimation techniques. The authors study these techniques for their robustness properties under adversarial attacks, proposes a novel attack type targeting uncertainty estimates through differential entropy,... | SP:d856b17ff19142ea30fcb687ee8a911b135fc3f5 |
Later Span Adaptation for Language Understanding | Pre-trained contextualized language models ( PrLMs ) broadly use fine-grained tokens ( words or sub-words ) as minimal linguistic units in the pre-training phase . Introducing span-level information in pre-training has shown capable of further enhancing PrLMs . However , such methods require enormous resources and lack... | This paper presents an approach to incorporate span information in pre-trained language models like BERT during fine-tuning. In the proposed approach, the segmentation of a sentence is obtained according to a pre-sampled n-gram dictionary. The fine-grained representation in a same span within the segmentation is aggreg... | SP:f70037a4e9a9be5eeedca1384b11aeb11ae248f6 |
Later Span Adaptation for Language Understanding | Pre-trained contextualized language models ( PrLMs ) broadly use fine-grained tokens ( words or sub-words ) as minimal linguistic units in the pre-training phase . Introducing span-level information in pre-training has shown capable of further enhancing PrLMs . However , such methods require enormous resources and lack... | Previous works reveal that span-level information can enhance the performance of PrLMs if they are used in pre-training. However, the existing methods require enormous resources and lack adaptivity. To this end, the paper proposes a method that combines span-level information into the representations generated by PrLMs... | SP:f70037a4e9a9be5eeedca1384b11aeb11ae248f6 |
On Flat Minima, Large Margins and Generalizability | 1 INTRODUCTION . Understanding under which conditions a neural network will generalize from seen to unseen data is crucial , as it motivates design choices and principles which can greatly improve performance . Complexity or generalization measures are used to quantify the properties of a neural network which lead to g... | The paper presents empirical evidence that the output margin - as a measure of the confidence of a multiclass predictor - is strongly correlated to the Hessian trace when using cross-entropy loss with softmax. Moreover, the paper presents a method for estimating the Hessian trace using the input norm and softmax output... | SP:8bbf8ac04f86085bfff8c946e16ffcece6e4065e |
On Flat Minima, Large Margins and Generalizability | 1 INTRODUCTION . Understanding under which conditions a neural network will generalize from seen to unseen data is crucial , as it motivates design choices and principles which can greatly improve performance . Complexity or generalization measures are used to quantify the properties of a neural network which lead to g... | This paper studies the correlation between the flatness of the converged local minimum and the margin. The authors report experimental results that verify the positive correlation. They suggest using margin-based measures to assess the generalizability. Also, the authors argue that large-batch optimization does not hav... | SP:8bbf8ac04f86085bfff8c946e16ffcece6e4065e |
Shape Matters: Understanding the Implicit Bias of the Noise Covariance | The noise in stochastic gradient descent ( SGD ) provides a crucial implicit regularization effect for training overparameterized models . Prior theoretical work largely focuses on spherical Gaussian noise , whereas empirical studies demonstrate the phenomenon that parameter-dependent noise — induced by minibatches or ... | This paper considers the effect of label noise on stochastic gradient descent. The setup is that there is a vector $v \in R^d$. We observe samples from $v^2\cdot x$. We only have $n < d$ samples but $v$ is $r$-sparse for $r < n < d$ which makes recovery possible information theoretically. The main result is that stocha... | SP:0b31a46fda77a01e02a6a94c52381af4aa759743 |
Shape Matters: Understanding the Implicit Bias of the Noise Covariance | The noise in stochastic gradient descent ( SGD ) provides a crucial implicit regularization effect for training overparameterized models . Prior theoretical work largely focuses on spherical Gaussian noise , whereas empirical studies demonstrate the phenomenon that parameter-dependent noise — induced by minibatches or ... | This paper considers the implicit regularization of stochastic gradient decent (SGD). The authors analyze SGD with label nose in the quadratically-parameterized model and prove that it converges to the sparse ground-truth even if started with large initialization. The authors also prove that SGD with Gaussian noise (La... | SP:0b31a46fda77a01e02a6a94c52381af4aa759743 |
QPLEX: Duplex Dueling Multi-Agent Q-Learning | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) has broad prospects for addressing many complex real-world problems , such as sensor networks ( Zhang & Lesser , 2011 ) , coordination of robot swarms ( Hüttenrauch et al. , 2017 ) , and autonomous cars ( Cao et al. , 2012 ) . However , cooperativ... | The paper proposed a multi-agent Q Learning algorithm with an entire IGM function class for cooperative games. The key idea is to leverage a duplex dueling network architecture to factorize the joint action-value function into individual action-value functions. The main contributions of the work lie in that the propose... | SP:ad5a9c6598151e60f84bf54984621a3832276c14 |
QPLEX: Duplex Dueling Multi-Agent Q-Learning | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) has broad prospects for addressing many complex real-world problems , such as sensor networks ( Zhang & Lesser , 2011 ) , coordination of robot swarms ( Hüttenrauch et al. , 2017 ) , and autonomous cars ( Cao et al. , 2012 ) . However , cooperativ... | This paper proposes a novel value decomposition approach to learn decentralized Q function in multi-agent setting. This idea is to follow Individual-Global-Max (IGM) principle. The main contribution is to use dueling structure (Q_i = V_i+A_i) for each agent i, and separately combining advantage/value terms respectively... | SP:ad5a9c6598151e60f84bf54984621a3832276c14 |
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer | 1 INTRODUCTION . While the size of training data can influence the machine learning model quality a lot , the data are often dispersed over different parties in reality . Due to regulations on data privacy , the data can not be centralized to a single party for training . To address these issues , federated learning ( ... | This submission proposes a new federated learning framework based on knowledge transfer. Local dataset at each party are partitioned and each partition is used to train a teacher model. All teacher models at each party are used to train a student model using pseudo labels based on voting on public dataset. Student mode... | SP:c126dc4e6625a18fdeecbd54f61abeff7e38f796 |
Model-Agnostic Round-Optimal Federated Learning via Knowledge Transfer | 1 INTRODUCTION . While the size of training data can influence the machine learning model quality a lot , the data are often dispersed over different parties in reality . Due to regulations on data privacy , the data can not be centralized to a single party for training . To address these issues , federated learning ( ... | The paper considers classification tasks in the federated learning scenario when each device/worker is powerful in terms of computational power and storage space, but, the communication between devices is constrained. The paper proposes a novel algorithm for federated learning that reduces the number of communication r... | SP:c126dc4e6625a18fdeecbd54f61abeff7e38f796 |
Numeric Encoding Options with Automunge | 1 INTRODUCTION . Of the various modalities of machine learning application ( e.g . images , language , audio , etc . ) tabular data , aka structured data , as may comprise tables of feature set columns and collected sample rows , in my experience does not command as much attention from the research community , for whic... | This paper introduces a number of data preprocessing options for numeric features provided by an open source library automunge. The most of the paper focuses on explaining the specific transformations offered under each option, including normalization, binning, and noise injection. For normalization, a new transformati... | SP:af32b11ab09c21c2732a744bf8c694cd80acf309 |
Numeric Encoding Options with Automunge | 1 INTRODUCTION . Of the various modalities of machine learning application ( e.g . images , language , audio , etc . ) tabular data , aka structured data , as may comprise tables of feature set columns and collected sample rows , in my experience does not command as much attention from the research community , for whic... | The paper describes a library (Automunge) for pre-processing tabular data to prepare the data for downstream machine learning tasks. The paper also describes how to use the said library and the various options (including some new forms of normalization) available in the library. Experimental evaluation on Higgs Boson i... | SP:af32b11ab09c21c2732a744bf8c694cd80acf309 |
Differentiable Spatial Planning using Transformers | We consider the problem of spatial path planning . In contrast to the classical solutions which optimize a new plan from scratch and assume access to the full map with ground truth obstacle locations , we learn a planner from the data in a differentiable manner that allows us to leverage statistical regularities from p... | This paper presents Spatial Planning Transformers (SPTs); neural network modules that perform spatial planning over grid-like state spaces. The paper also goes on to present the idea that differentiable mapping and differntiable planning modules could be trained end-to-end, for better performance. This is evaluated aga... | SP:9ca7082f4aa0970c3c35409c9f259e5ad844c553 |
Differentiable Spatial Planning using Transformers | We consider the problem of spatial path planning . In contrast to the classical solutions which optimize a new plan from scratch and assume access to the full map with ground truth obstacle locations , we learn a planner from the data in a differentiable manner that allows us to leverage statistical regularities from p... | The paper provides an interesting direction in the field of spatial path planning. The method is interesting as it is fully learnt in an end to end fashion. The key idea is to use a transformer like architecture to model long range dependencies. Also the paper extend its findings to out of distribution maps and the ca... | SP:9ca7082f4aa0970c3c35409c9f259e5ad844c553 |
Deep Learning Solution of the Eigenvalue Problem for Differential Operators | 1 INTRODUCTION . Eigenfunctions and eigenvalues of the Laplacian ( among other operators ) are important in various applications ranging , inter alia , from image processing to computer vision , shape analysis and quantum mechanics . It is also of major importance in various engineering applications where resonance is ... | The manuscript proposes a deep learning solver for the eigenvalue problem of differential self-adjoint operators. Specifically, the aim is to calculate M lowest eigenvalues and their corresponding eigenfunctions. This work is a natural follow up of the work by Bar and Sochen (2019) for solving PDE-based problems. The ... | SP:32047b62ffb4a15e2ff7d757d8506abeb8770dde |
Deep Learning Solution of the Eigenvalue Problem for Differential Operators | 1 INTRODUCTION . Eigenfunctions and eigenvalues of the Laplacian ( among other operators ) are important in various applications ranging , inter alia , from image processing to computer vision , shape analysis and quantum mechanics . It is also of major importance in various engineering applications where resonance is ... | The authors frame the decomposition of the Laplacian equation as an unsupervised regression problem that is using a 5-level (and fully connected?) neural network as regression function. A cost function to be used in the optimization is proposed that is expanded to eigendecomposition problems of increasing complexity. ... | SP:32047b62ffb4a15e2ff7d757d8506abeb8770dde |
Oblivious Sketching-based Central Path Method for Solving Linear Programming Problems | 1 Introduction . Linear programming is one of the fundamental models widely used in both theory and practice . It has been extensively applied in many fields such as economics Tintner ( 1955 ) ; Dorfman et al . ( 1987 ) , operations research Delson & Shahidehpour ( 1992 ) , compressed sensing Donoho ( 2006 ) ; Candes e... | The paper studies the problem of solving LP and more generally convex programming via sketching based approaches. In particular, the running time of proposed algorithm in this paper matches the running time of the best known algorithms [Cohen et al(19b) and Lee et al(19)]. However, this paper provides some further usef... | SP:dbabb260baf9daed8066d80ef49ec0cfa9f70ae6 |
Oblivious Sketching-based Central Path Method for Solving Linear Programming Problems | 1 Introduction . Linear programming is one of the fundamental models widely used in both theory and practice . It has been extensively applied in many fields such as economics Tintner ( 1955 ) ; Dorfman et al . ( 1987 ) , operations research Delson & Shahidehpour ( 1992 ) , compressed sensing Donoho ( 2006 ) ; Candes e... | The paper is based on the works of Lee et al and Cohen et al. Building upon these works, the paper comes up with an interior point method that matches the state-of-the-art in LPs. The paper's contribution is in a new type of sketching used inside the interior point method, that demonstrates some advantages over those o... | SP:dbabb260baf9daed8066d80ef49ec0cfa9f70ae6 |
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series | 1 INTRODUCTION . The past decade has witnessed a rising proliferation in Multivariate Time Series ( MTS ) data , along with a plethora of applications in domains as diverse as IoT data analysis , medical informatics , and network security . Given the huge amount of MTS data , it is crucial to learn their representation... | This work proposes an AutoML framework for multivariate irregularly sampled time series. To achieve this, the proposed framework integrates different modules: data-augmentation self-supervised loss (Equation 7), an anomaly detection loss (Equation 5), and a reconstruction loss. Besides, hyperparameters and model’s conf... | SP:c3a4b596e4a86f0032646a166f9506f73a34d60d |
TimeAutoML: Autonomous Representation Learning for Multivariate Irregularly Sampled Time Series | 1 INTRODUCTION . The past decade has witnessed a rising proliferation in Multivariate Time Series ( MTS ) data , along with a plethora of applications in domains as diverse as IoT data analysis , medical informatics , and network security . Given the huge amount of MTS data , it is crucial to learn their representation... | This paper proposes an autonomous representation learning framework for multivariate time series with irregular sampling rates. Specifically, there are three major components proposed in the framework. 1) An AutoML solution for hyperparameters optimization under Bayesian framework is proposed to automatically seek opti... | SP:c3a4b596e4a86f0032646a166f9506f73a34d60d |
Probing BERT in Hyperbolic Spaces | 1 INTRODUCTION . Contextualized word representations with pretrained language models have significantly advanced NLP progress ( Peters et al. , 2018a ; Devlin et al. , 2019 ) . Previous works point out that abundant linguistic knowledge implicitly exists in these representations ( Belinkov et al. , 2017 ; Peters et al.... | This paper proposes probes based on hyperbolic embedding spaces, and compares them to the behaviour of Euclidean probes from recent work. The main result is that these probes allow for better recovery of syntactic properties of sentences from contextualized word embeddings compared to context-independent ones, when com... | SP:9f713e213bde5ac69147e961004757cf0b6ae956 |
Probing BERT in Hyperbolic Spaces | 1 INTRODUCTION . Contextualized word representations with pretrained language models have significantly advanced NLP progress ( Peters et al. , 2018a ; Devlin et al. , 2019 ) . Previous works point out that abundant linguistic knowledge implicitly exists in these representations ( Belinkov et al. , 2017 ; Peters et al.... | In the same vein as Hewitt & Manning 2019, the authors present an extremely lightly parametrized “probe” model to determine the presence of syntactic structure in the embedding space of BERT models. While Hewitt & Manning examine the Euclidean distance between linearly transformed token embeddings and its correlation w... | SP:9f713e213bde5ac69147e961004757cf0b6ae956 |
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification | 1 INTRODUCTION . Graph convolutional network ( GCN ) can be seen as the migration of convolutional neural network ( CNN ) on non-Euclidean structure data . Due to its its excellent ability in representation learning , GCN has achieved significant success in many graph-based learning tasks , including node clustering , ... | This paper presents a self-training algorithm based on GCN to improve the semi-supervised node classification on graphs. The key idea is to add new nodes with high confidence as supervision to enlarge the labeled nodes. Although the experimental results show the proposed method outperforms or performs similarly to base... | SP:64794a022e18c8cee6599446d5846dd00bc0b8ab |
FASG: Feature Aggregation Self-training GCN for Semi-supervised Node Classification | 1 INTRODUCTION . Graph convolutional network ( GCN ) can be seen as the migration of convolutional neural network ( CNN ) on non-Euclidean structure data . Due to its its excellent ability in representation learning , GCN has achieved significant success in many graph-based learning tasks , including node clustering , ... | This paper proposes a self-training based semi-supervised framework for node classification using Graph Neural Networks when the amount of labelled data is very limited. Self-training is performed by incorporating highly confident samples with their corresponding predicted class as the pseudo label. Authors show that i... | SP:64794a022e18c8cee6599446d5846dd00bc0b8ab |
Deep Gated Canonical Correlation Analysis | 1 INTRODUCTION . Canonical Correlation Analysis ( CCA ) ( Hotelling , 1936 ; Thompson , 2005 ) , is a classic statistical method for finding the maximally correlated linear transformations of two modalities ( or views ) . Using modalities X ∈ RDx×N and Y ∈ RDY ×N , which are centered and have N samples with Dx and Dy f... | This paper presents a new deep CCA method to learn non-linear relationships between two modalities. It trains two neural networks each for a modality to maximize the total correlations of their output representations. Gating is applied to input variables by associating each with a latent Bernoulli variables which is th... | SP:ce147e13a4126d022aa6c22dca433ca81062f924 |
Deep Gated Canonical Correlation Analysis | 1 INTRODUCTION . Canonical Correlation Analysis ( CCA ) ( Hotelling , 1936 ; Thompson , 2005 ) , is a classic statistical method for finding the maximally correlated linear transformations of two modalities ( or views ) . Using modalities X ∈ RDx×N and Y ∈ RDY ×N , which are centered and have N samples with Dx and Dy f... | This paper proposes a DL method for learning sparse non-linear transformations that maximize correlations between two views. In particular, each view is passed through a separate network. Stochastic Gating is applied to the input layer of each network. The two networks are jointly trained by maximising the correlation ... | SP:ce147e13a4126d022aa6c22dca433ca81062f924 |
Einstein VI: General and Integrated Stein Variational Inference in NumPyro | 1 INTRODUCTION . Interest in Bayesian deep learning has surged due to the need for quantifying the uncertainty of predictions provided by machine learning algorithms . The idea behind Bayesian learning is to describe observed data x using a model with latent variable z ( representing model parameters and nuisance varia... | This paper introduces EinStein VI: a lightweight composable library for Stein Variational Inference (Stein VI). The library is built on top of NumPyro and can take advantage of many of NumPyro's capabilities. It supports recent techniques associated with Stein VI, as well as novel features. The paper provides examples ... | SP:07e42db34b99c1c6bd5c7c5823db9f0bffe5ecdb |
Einstein VI: General and Integrated Stein Variational Inference in NumPyro | 1 INTRODUCTION . Interest in Bayesian deep learning has surged due to the need for quantifying the uncertainty of predictions provided by machine learning algorithms . The idea behind Bayesian learning is to describe observed data x using a model with latent variable z ( representing model parameters and nuisance varia... | The paper shows how a particle-based nonparameteric Variational Inference methodology known as Stein Variational Inference is integrated in a full-featured Probabilistic Programming Language, NumPyro. The paper goes into a fair amount detail describing a number of enhancements that have been made into numpyro using the... | SP:07e42db34b99c1c6bd5c7c5823db9f0bffe5ecdb |
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning | In this paper , we consider intrinsic reward generation for sparse-reward reinforcement learning based on model prediction errors . In typical model-prediction-errorbased intrinsic reward generation , an agent has a learning model for the underlying environment . Then , intrinsic reward is designed as the error between... | This paper proposes an intrinsic reward formulation to address the challenge of sparse reward in reinforcement learning. The key idea is to learn multiple models. The prediction error of each model is used as a component of the intrinsic reward. These components are fused using an alpha-mean function. The parameter alp... | SP:bd65ec9b7d991a0c23b882bb137653f2f2f94c71 |
Adaptive Multi-model Fusion Learning for Sparse-Reward Reinforcement Learning | In this paper , we consider intrinsic reward generation for sparse-reward reinforcement learning based on model prediction errors . In typical model-prediction-errorbased intrinsic reward generation , an agent has a learning model for the underlying environment . Then , intrinsic reward is designed as the error between... | In this paper, the authors explore a model based intrinsic reward generation mechanism, in environment settings where the reward assignment is sparse. The authors used an ensemble of models, and computed the alpha-mean value of their KL divergences with respect to the "true transitions". The alpha-mean serves as the i... | SP:bd65ec9b7d991a0c23b882bb137653f2f2f94c71 |
MULTI-SPAN QUESTION ANSWERING USING SPAN-IMAGE NETWORK | 1 INTRODUCTION . Answering questions posted as text to search engines or spoken to virtual assistants like Alexa has become a key feature in information retrieval systems . Publicly available reading comprehension datasets including WikiQA ( Yang et al. , 2015 ) , TriviaQA ( Joshi et al. , 2017 ) , NewsQA ( Trischler e... | The paper proposes a novel method for predicting multiple answer spans in question-answering (QA) tasks. When the Span-Image technique is applied to a base BERT model, the authors show performance gains on a single-span dataset (SQuAD) and substantial improvements on a multi-span dataset (an internal Amazon dataset). T... | SP:23d329d9d5208e429f714761d33eb48498700153 |
MULTI-SPAN QUESTION ANSWERING USING SPAN-IMAGE NETWORK | 1 INTRODUCTION . Answering questions posted as text to search engines or spoken to virtual assistants like Alexa has become a key feature in information retrieval systems . Publicly available reading comprehension datasets including WikiQA ( Yang et al. , 2015 ) , TriviaQA ( Joshi et al. , 2017 ) , NewsQA ( Trischler e... | This paper introduces a new QA model based on BERT, which is called Span-Image Network. The paper first points out that previous span extraction models model independent probability of the start and the end of the span, making the extension to multi-span extraction harder. Span-Image Network model the joint probability... | SP:23d329d9d5208e429f714761d33eb48498700153 |
Random Network Distillation as a Diversity Metric for Both Image and Text Generation | Generative models are increasingly able to produce remarkably high quality images and text . The community has developed numerous evaluation metrics for comparing generative models . However , these metrics do not effectively quantify data diversity . We develop a new diversity metric that can readily be applied to dat... | In this paper, the authors introduce a new quantitative diversity measure advocating its usage for generative models evaluation. In a nutshell, to measure the diversity of a particular set, the authors split it into disjoint train/val subsets and learn a DNN to predict the outputs of another randomly initialized DNN on... | SP:feabfeef5c1282d0b8de3d98611588b698013baf |
Random Network Distillation as a Diversity Metric for Both Image and Text Generation | Generative models are increasingly able to produce remarkably high quality images and text . The community has developed numerous evaluation metrics for comparing generative models . However , these metrics do not effectively quantify data diversity . We develop a new diversity metric that can readily be applied to dat... | This paper applies random network distillation (RND) as a method for quantifying how diverse samples from a generative model are. Samples from the generative model (or any dataset) are used to train a neural network to mimic a randomly initialized network. Intuitively, this is a more difficult task on a more diverse da... | SP:feabfeef5c1282d0b8de3d98611588b698013baf |
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets | 1 INTRODUCTION . Learning models that generalize effectively to complex open-world settings , from image recognition ( Krizhevsky et al. , 2012 ) to natural language processing ( Devlin et al. , 2019 ) , relies on large , high-capacity models and large , diverse , and representative datasets . Leveraging this recipe fo... | In this paper, the authors intend to accelerate on-line reinforcement learning with off-line datasets. To achieve this goal, they propose an algorithm called advantage weighted actor-critic (AWAC), which uses an implicit constraint to reduce accumulated bootstrapping error when doing off-line training and reduce the co... | SP:7e456aff1e90c9f11b51c22e9ec7132eca76d700 |
AWAC: Accelerating Online Reinforcement Learning with Offline Datasets | 1 INTRODUCTION . Learning models that generalize effectively to complex open-world settings , from image recognition ( Krizhevsky et al. , 2012 ) to natural language processing ( Devlin et al. , 2019 ) , relies on large , high-capacity models and large , diverse , and representative datasets . Leveraging this recipe fo... | This paper studies shorting coming of existing off-policy methods when it comes to prior data and fine-tuning and shows that those existing methods can't effectively utilize previously collected data with online updates. To address this problem, they propose to constraint policy updates with respect to behavioral polic... | SP:7e456aff1e90c9f11b51c22e9ec7132eca76d700 |
On Data-Augmentation and Consistency-Based Semi-Supervised Learning | 1 INTRODUCTION . Consider a datasetD = DL∪DU that is comprised of labelled samplesDL = { xi , yi } i∈IL as well as unlabelled samples DU = { xi } i∈IU . Semi-Supervised Learning ( SSL ) is concerned with the use of both the labelled and unlabeled data for training . In many scenarios , collecting labelled data is diffi... | This paper analyses the consistency-based SSL methods in settings where data lie a manifold of much lower dimension than the input space and obtains tractable results. The paper relates the analysis with Manifold Tangent Classifiers and shows that the quality of the perturbations plays a key role to achieve a promis... | SP:49bb8457a99e6a178e7893c9629e6543b15a564a |
On Data-Augmentation and Consistency-Based Semi-Supervised Learning | 1 INTRODUCTION . Consider a datasetD = DL∪DU that is comprised of labelled samplesDL = { xi , yi } i∈IL as well as unlabelled samples DU = { xi } i∈IU . Semi-Supervised Learning ( SSL ) is concerned with the use of both the labelled and unlabeled data for training . In many scenarios , collecting labelled data is diffi... | The authors analyze consistency-based models in specific settings where analytically tractable results can be obtained. They establish that leveraging more sophisticated data augmentation schemes is crucial to obtain huge gains when using consistency based models. Finally, they propose an extension of Hidden Manifold M... | SP:49bb8457a99e6a178e7893c9629e6543b15a564a |
FTSO: Effective NAS via First Topology Second Operator | 1 INTRODUCTION . Since the great success of AlexNet ( Krizhevsky et al. , 2012 ) in image classification , most modern machine learning models have been developed based on deep neural networks . For neural networks , their performance is greatly determined by the architectures . Thus , in the past decade , a tremendous... | This work researches the issue of neural architecture search (NAS), which is of significance for practical applications of deep neural networks and has become an active research topic in the past several years. Many methods on NAS have been developed recently. The computational efficiency of search has been one of the ... | SP:ef74c56a1e29fc366078c7d3ac2746e5282f496f |
FTSO: Effective NAS via First Topology Second Operator | 1 INTRODUCTION . Since the great success of AlexNet ( Krizhevsky et al. , 2012 ) in image classification , most modern machine learning models have been developed based on deep neural networks . For neural networks , their performance is greatly determined by the architectures . Thus , in the past decade , a tremendous... | The paper proposes a method for Neural Architecture Search (NAS) with two stages of search. In the first stage, the topology of the cell is searched with only one operator (skip connection) using graph pruning through gradient descents. In the later stage, there are two ways to search the operators. In the first appro... | SP:ef74c56a1e29fc366078c7d3ac2746e5282f496f |
Learning to Observe with Reinforcement Learning | We consider a decision making problem where an autonomous agent decides on which actions to take based on the observations it collects from the environment . We are interested in revealing the information structure of the observation space illustrating which type of observations are the most important ( such as positio... | In contrast to standard reinforcement learning (RL), the paper investigates the variant where the observation made by the agent about its state has a cost. The authors propose to model the problem as a POMDP with an augmented action space (normal action + observation accuracy) and a new reward function that is defined ... | SP:4a1a4003949cbe2f0d0fee232d166874ab52716f |
Learning to Observe with Reinforcement Learning | We consider a decision making problem where an autonomous agent decides on which actions to take based on the observations it collects from the environment . We are interested in revealing the information structure of the observation space illustrating which type of observations are the most important ( such as positio... | The paper proposes a reinforcement learning algorithm that enables an agent to "fine tune" the quality/accuracy of its sensors to its current task. The paper considers a partially observable MDP setting where the agent, besides the control actions, is endowed with a set of "tuning actions" that control the noise in the... | SP:4a1a4003949cbe2f0d0fee232d166874ab52716f |
Addressing Extrapolation Error in Deep Offline Reinforcement Learning | 1 INTRODUCTION . Agents are , fundamentally , entities which map observations to actions and can be trained with reinforcement learning ( RL ) in either an online or offline fashion . When trained online , an agent learns through trial and error by interacting with its environment . Online RL has had considerable succe... | The paper deals with offline aka batch RL for discrete actions. Three techniques ((i) behavior value estimation, (ii) ranking regularization, and (iii) reparametrization of the value function), which can be combined with each other, are presented. These techniques are compared with other methods in different experiment... | SP:6fa0afdd0b767254f88f9b06494f4193b4fd2c4f |
Addressing Extrapolation Error in Deep Offline Reinforcement Learning | 1 INTRODUCTION . Agents are , fundamentally , entities which map observations to actions and can be trained with reinforcement learning ( RL ) in either an online or offline fashion . When trained online , an agent learns through trial and error by interacting with its environment . Online RL has had considerable succe... | This paper focuses on the problem of Q value over-estimation in offline reinforcement learning and proposes three approaches (tricks) to help solve this problem. (1) estimate Q value of behavior policy avoiding max-operator in Q learning and take greedy action according to the behavior value estimation. (2) introduce r... | SP:6fa0afdd0b767254f88f9b06494f4193b4fd2c4f |
Intriguing class-wise properties of adversarial training | 1 INTRODUCTION The existence of adversarial examples ( Szegedy et al. , 2014 ) reveals the vulnerability of deep neural networks , which greatly hinders the practical deployment of deep learning models . Adversarial training ( Madry et al. , 2018 ) has been demonstrated to be one of the most successful defense methods ... | This paper examines the robustness of adversarially robust models at the class-level. Specifically, they note a disparity in the class-wise robustness of models for standard datasets. Furthermore, they suggest that many of these class-level vulnerabilities are eliminated if the model is trained without the correspondi... | SP:62517d35207a58ae175ea3c3787512424e8ece51 |
Intriguing class-wise properties of adversarial training | 1 INTRODUCTION The existence of adversarial examples ( Szegedy et al. , 2014 ) reveals the vulnerability of deep neural networks , which greatly hinders the practical deployment of deep learning models . Adversarial training ( Madry et al. , 2018 ) has been demonstrated to be one of the most successful defense methods ... | The authors study the robustness of adversially-trained models across different classes. They find that classes tend to have largely non-uniform robust accuracy--i.e., some are less robust then others. Moreover, certain datapoints can only be misclassified as specific classes and removing these classes during training ... | SP:62517d35207a58ae175ea3c3787512424e8ece51 |
Sandwich Batch Normalization | 1 INTRODUCTION . This paper presents a simple , light-weight , and easy-to-implement modification of Batch Normalization ( BN ) ( Ioffe & Szegedy , 2015 ) , yet strongly motivated by various observations ( Zając et al. , 2019 ; Deecke et al. , 2018 ; Xie et al. , 2019 ; Xie & Yuille , 2019 ) drawn from a number of app... | This paper considers improving the performance of various normalizers by factorizing the affinity operations in normalization layer into on shared affinity operation, as well as several several independent affinity operation, each of which is corresponding to a specific data distribution. The experiments on various tas... | SP:42885bdb86f343bd752c9a406d7e985fec81a7f6 |
Sandwich Batch Normalization | 1 INTRODUCTION . This paper presents a simple , light-weight , and easy-to-implement modification of Batch Normalization ( BN ) ( Ioffe & Szegedy , 2015 ) , yet strongly motivated by various observations ( Zając et al. , 2019 ; Deecke et al. , 2018 ; Xie et al. , 2019 ; Xie & Yuille , 2019 ) drawn from a number of app... | In this paper, the authors propose Sandwich Affine strategy to separate the affine layer in BN into one shared sandwich affine layer, cascaded by several parallel independent affine layers. Such method should well address the inherent feature distribution heterogeneity in many tasks. Following this idea, the SaAuxBN an... | SP:42885bdb86f343bd752c9a406d7e985fec81a7f6 |
Uncertainty in Neural Processes | 1 INTRODUCTION . What makes a probabilistic conditional generative model good ? The belief that a generative model is good if it produces samples that are indistinguishable from those that it was trained on ( Hinton , 2007 ) is widely accepted , and understandably so . This belief also applies when the generator is con... | The paper aims at increasing the sample diversity of neural processes when the condition set is small, while maintaining visual fidelity. The low-data regime is arguably where neural processes are most interesting, and in that regard the paper is right to turn to this setting. The discussion on how different aggregatio... | SP:4f8854605423fab230a26fa2d12d3bfef54b0ca5 |
Uncertainty in Neural Processes | 1 INTRODUCTION . What makes a probabilistic conditional generative model good ? The belief that a generative model is good if it produces samples that are indistinguishable from those that it was trained on ( Hinton , 2007 ) is widely accepted , and understandably so . This belief also applies when the generator is con... | This paper proposes an improvement of the standard NP by using a mixture distribution \q_{\phi}, semi-implicit variational inference, and max pooling to capture the multimodel structure of the posterior distribution. Replacing one normal Gaussian distribution with a mixture (of Gaussians, normally) is a widely-adopted ... | SP:4f8854605423fab230a26fa2d12d3bfef54b0ca5 |
BROS: A Pre-trained Language Model for Understanding Texts in Document | 1 INTRODUCTION . Document intelligence ( DI ) 1 , which understands industrial documents from their visual appearance , is a critical application of AI in business . One of the important challenges of DI is a key information extraction task ( KIE ) ( Huang et al. , 2019 ; Jaume et al. , 2019 ; Park et al. , 2019 ) that... | Authors used BERT alongside to a 2D-position embedding based on a sinusoidal function and a graph-based decoder to improve performance on document information extraction tasks. They do pre-train their model (BROS) on a large dataset with 11M documents, and then used such models to perform downstream tasks in four small... | SP:301524218da40096adedfa6d058b1f6ef93ea882 |
BROS: A Pre-trained Language Model for Understanding Texts in Document | 1 INTRODUCTION . Document intelligence ( DI ) 1 , which understands industrial documents from their visual appearance , is a critical application of AI in business . One of the important challenges of DI is a key information extraction task ( KIE ) ( Huang et al. , 2019 ; Jaume et al. , 2019 ; Park et al. , 2019 ) that... | The paper proposes the pre-trained language model BROS which aims to leverage both text and spatial information to improve information extraction on documents. Using the graph-based decoder from SPADE, BROS achieves the SOTA performance on some entity extraction and entity linking downstream tasks. However, the area-ma... | SP:301524218da40096adedfa6d058b1f6ef93ea882 |
SkillBERT: “Skilling” the BERT to classify skills! | 1 INTRODUCTION . Competency group can be thought of as a group of similar skills required for success in a job . For example , skills such as Apache Hadoop , Apache Pig represent competency in Big Data analysis while HTML , Javascript are part of Front-end competency . Classification of skills into the right competency... | This paper proposes a model for job application screening. Since there is no job-related dataset available, the authors manually assigned labels to a large job application dataset. A skill set (e.g., Apache Hadoop, Apache Pig, HTML, Javascript) is firstly extracted from the job dataset. Then a competency group is cons... | SP:e2f5cc48d84e800d9557d2a0f0e90b636ea22a15 |
SkillBERT: “Skilling” the BERT to classify skills! | 1 INTRODUCTION . Competency group can be thought of as a group of similar skills required for success in a job . For example , skills such as Apache Hadoop , Apache Pig represent competency in Big Data analysis while HTML , Javascript are part of Front-end competency . Classification of skills into the right competency... | The manuscript focuses on a trending topic of applying a Bidirectional Encoder Representations from Transformers (BERT)-based prediction model to a new domain. More precisely, it addresses classifying Electronic Recruitment Records (ERRs) with respect to job skills. Its contributions include, but are not limited to, (i... | SP:e2f5cc48d84e800d9557d2a0f0e90b636ea22a15 |
ROMUL: Scale Adaptative Population Based Training | In most pragmatic settings , data augmentation and regularization are essential , and require hyperparameter search . Population based training ( PBT ) is an effective tool for efficiently finding them as well as schedules over hyperparameters . In this paper , we compare existing PBT algorithms and contribute a new on... | The paper provides a new variant of PBT which utilizes ideas from differential evolution and cross-over. The original PBT and even initiator PBT do not perform crossover on the hyper-parameters, and insufficient cross-over may cause PBT to perform greedy in the initial phases which ends up with a suboptimal convergence... | SP:7e75b1311a12b8c0353180183447e529683a88d6 |
ROMUL: Scale Adaptative Population Based Training | In most pragmatic settings , data augmentation and regularization are essential , and require hyperparameter search . Population based training ( PBT ) is an effective tool for efficiently finding them as well as schedules over hyperparameters . In this paper , we compare existing PBT algorithms and contribute a new on... | This paper focuses on issues in the popular PBT algorithm for hyperparameter optimization. It investigates the 1) step size (which is typically a constant multiplier) 2) the variance induced by better weights and 3) the greediness of the algorithm, which they refer to as short-term vs. long term effects. These issues a... | SP:7e75b1311a12b8c0353180183447e529683a88d6 |
Deep Ecological Inference | 1 INTRODUCTION . Ecological inference ( EI ) , or learning labels from label proportions , is the problem of trying to make predictions about individual units from observations about aggregates . The canonical case is voting . We can not observe individual people ’ s votes , but people live in precincts , and we know f... | This paper proposes a deep learning framework for approximating ecological inference for estimating voting propensities based on demographic aggregates. This is an important problem, as EI has become a court standard for evaluating racially polarized voting in gerrymandering cases for the Gingles factors. Additionally,... | SP:86823c4b45c78992ca5925cd1fb0e241e42a56ea |
Deep Ecological Inference | 1 INTRODUCTION . Ecological inference ( EI ) , or learning labels from label proportions , is the problem of trying to make predictions about individual units from observations about aggregates . The canonical case is voting . We can not observe individual people ’ s votes , but people live in precincts , and we know f... | This paper takes an approach to ecological inference inspired by deep learning. Ecological inference is the problem of learning individual labels when only large sets of aggregated data are available. It requires a way to estimate label propensities as a function of covariates. This paper proposes combining a multi-lev... | SP:86823c4b45c78992ca5925cd1fb0e241e42a56ea |
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery | 1 INTRODUCTION . Drug discovery aims to find chemical compounds with desired target properties , such as high druglikeness ( Bickerton et al. , 2012 , QED ) . The problem is also referred to as molecular design , molecular generation , or molecular search . The space of drug-like chemicals is enormous , approximate 103... | The authors propose a novel way to generate molecules with specified objectives, named MArkov moleculaR Sampling (MARS). The idea of MARS is based on generating the chemical candidates by iterative editing fragments of molecular graphs. To transform a molecule x into another molecule x′, the authors considers two sets ... | SP:0de748131383ac3425179fa9b34e7593c25fd8bd |
MARS: Markov Molecular Sampling for Multi-objective Drug Discovery | 1 INTRODUCTION . Drug discovery aims to find chemical compounds with desired target properties , such as high druglikeness ( Bickerton et al. , 2012 , QED ) . The problem is also referred to as molecular design , molecular generation , or molecular search . The space of drug-like chemicals is enormous , approximate 103... | This paper proposes a method to generate molecular graphs with multiple optimized properties. Molecular graphs are constructed/edited by the iterative addition and removal of molecular fragments. A MCMC search procedure, guided by a learned graph neural network that proposes good graph edit actions, is used to sample m... | SP:0de748131383ac3425179fa9b34e7593c25fd8bd |
Geometry-Aware Gradient Algorithms for Neural Architecture Search | 1 INTRODUCTION . Neural architecture search has become an important tool for automating machine learning ( ML ) but can require hundreds of thousands of GPU-hours to train . Recently , weight-sharing approaches have achieved state-of-the-art performance while drastically reducing the computational cost of NAS to just t... | The submission presents a modification to the DARTS family of efficient Neural Architecture Search algorithms. The authors claim their modification (i) leads to better empirical performance, and (ii) is theoretically well-motivated. DARTS is a Neural Architecture Search algorithm which aims to find the most accurate ne... | SP:6dc30e63334ceb8d3ef8b987b0a1d92167c780c5 |
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