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Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy | 1 INTRODUCTION . Protecting data privacy is a significant concern in many data-driven decision-making applications ( Zhu et al. , 2017 ) , such as social networking service , recommender system , location-based service . For example , the United States Census Bureau will firstly employ differential privacy to the 2020 ... | The paper introduces an algorithm for mitigating disparate impact of private learning (DP-SGD) on different groups of a given population. In each iteration of DP-SGD, instead of using a uniform gradient clipping threshold for all groups, the proposed Fair DP-SGD algorithm uses an optimal clipping threshold (one that mi... | SP:53e0d7909b00c88201dc1d7a8da7bd1efa4eb48e |
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs | 1 INTRODUCTION . Knowledge graphs are collections of real-world facts , which are useful in various applications . Each fact is typically specified as a triplet ( h , r , t ) or equivalently r ( h , t ) , meaning entity h has relation r with entity t. For example , Bill Gates is the Co-founder of Microsoft . As it is i... | There is a lot of recent work on link-prediction in knowledge graphs. One approach is based on embedding entities and relations in a knowledge graph into vector spaces, and the other is based on finding rules that imply relations, and then using these rules to find new links or facts. This paper takes the latter approa... | SP:0bdb9aa34e57b33cc411fd2f5ae54623c9ac0159 |
RNNLogic: Learning Logic Rules for Reasoning on Knowledge Graphs | 1 INTRODUCTION . Knowledge graphs are collections of real-world facts , which are useful in various applications . Each fact is typically specified as a triplet ( h , r , t ) or equivalently r ( h , t ) , meaning entity h has relation r with entity t. For example , Bill Gates is the Co-founder of Microsoft . As it is i... | In this paper, the author proposes RNNLogic for learning FOL rules from the knowledge graph. The proposed method assigns embeddings for each relation type and uses RNN module to generate chain-like rule candidates. Candidates are evaluated with a separate evaluation module that computes the scores. The rule scores are ... | SP:0bdb9aa34e57b33cc411fd2f5ae54623c9ac0159 |
Semi-supervised counterfactual explanations | Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output . A valid counterfactual explanation should have likely feature values . Here , we address the challenge of generating ... | This paper presents a new approach for generating counterfactual explanations. Specifically, the presented method optimizing for a counterfactual explanation using a weighted loss function of L_pred, L_sparsity, L_recon, and L_proto, and differs from previous works in the manner in which the latter two losses are compu... | SP:ec3d792f859916d782bce86107d178f6965fc9b1 |
Semi-supervised counterfactual explanations | Counterfactual explanations for machine learning models are used to find minimal interventions to the feature values such that the model changes the prediction to a different output or a target output . A valid counterfactual explanation should have likely feature values . Here , we address the challenge of generating ... | This paper continues an emerging line of research to find interpretable (post-hoc) counterfactual explanations of classifier predictions. While prior work has made advances in ensuring that resulting counterfactuals lie in the same data distribution as the original dataset by using auto-encoders, this paper provides a ... | SP:ec3d792f859916d782bce86107d178f6965fc9b1 |
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective | 1 INTRODUCTION . A recent study shows that models trained to detect COVID-19 from chest radiographs rely on spurious factors such as the source of the data rather than the lung pathology ( DeGrave et al. , 2020 ) . This is just one of many alarming examples of spurious correlations failing to hold outside a specific tr... | The paper investigates the choice of learning paradigms to reach out-of-distribution generalization, namely IRM vs ERM under different scenarios of domain generalization. Technically, generalization bounds and rates are calculated to be able to compare theoretically how each paradigm fares in the different scenarios. P... | SP:3d8e456a346e4f54909fb689197cbf80c51e601a |
Empirical or Invariant Risk Minimization? A Sample Complexity Perspective | 1 INTRODUCTION . A recent study shows that models trained to detect COVID-19 from chest radiographs rely on spurious factors such as the source of the data rather than the lung pathology ( DeGrave et al. , 2020 ) . This is just one of many alarming examples of spurious correlations failing to hold outside a specific tr... | This paper considers a learning scenario where training data $(X,Y)$ comes from a mixture, where membership in each mixture component (“environment”) is clearly labeled. Generalization is required not just under the same mixture, but potentially under changing mixing distributions. This is captured via several alternat... | SP:3d8e456a346e4f54909fb689197cbf80c51e601a |
Quantifying Differences in Reward Functions | For many tasks , the reward function is inaccessible to introspection or too complex to be specified procedurally , and must instead be learned from user data . Prior work has evaluated learned reward functions by evaluating policies optimized for the learned reward . However , this method can not distinguish between t... | The paper introduces a pseudometric on reward functions, EPIC (Equivalent-Policy Invariant Comparison), based on the potential-based reward shaping (Ng. 2020). It formally analyzes the EPIC distance in detail and demonstrates its usefulness in comparing learned reward functions without the necessity of optimizing rewar... | SP:7f95a11596f1b1321a691b1b45cff3de69027aaf |
Quantifying Differences in Reward Functions | For many tasks , the reward function is inaccessible to introspection or too complex to be specified procedurally , and must instead be learned from user data . Prior work has evaluated learned reward functions by evaluating policies optimized for the learned reward . However , this method can not distinguish between t... | The paper introduced Equivalent-Policy Invariant Comparison (EPIC) pseudometric to compare different reward functions directly without training a policy function. The authors provide an interesting direction for inverse reinforcement learning. The EPIC distance gives a bound on the regret between policies optimizing fo... | SP:7f95a11596f1b1321a691b1b45cff3de69027aaf |
Reinforcement Learning for Control with Probabilistic Stability Guarantee | 1 INTRODUCTION . Reinforcement learning ( RL ) has achieved superior performance on some complicated control tasks ( Kumar et al. , 2016 ; Xie et al. , 2019 ; Hwangbo et al. , 2019 ) for which the traditional control engineering methods can be hardly applicable ( Åström and Wittenmark , 1973 ; Morari and Zafiriou , 1... | This paper studies the probabilistic stability guarantee of control systems. In general, hard stability guarantee is difficult with only finite samples. The authors instead focus on developing probabilistic stability conditions. High probability bound is derived in terms of the number of trajectories and the length of ... | SP:fd270313e05eb0d810f7e0fc8f807ae3bcdb9dd0 |
Reinforcement Learning for Control with Probabilistic Stability Guarantee | 1 INTRODUCTION . Reinforcement learning ( RL ) has achieved superior performance on some complicated control tasks ( Kumar et al. , 2016 ; Xie et al. , 2019 ; Hwangbo et al. , 2019 ) for which the traditional control engineering methods can be hardly applicable ( Åström and Wittenmark , 1973 ; Morari and Zafiriou , 1... | **General overview:** The paper studies guaranteeing the closed-loop stability of a Markov decision process (MDP) using a given a policy, based on a finite number of trajectories each containing finite number of steps. Both the state and the action spaces are assumed to be subsets of finite dimensional Euclidean spaces... | SP:fd270313e05eb0d810f7e0fc8f807ae3bcdb9dd0 |
Momentum Contrastive Autoencoder | 1 INTRODUCTION . The main goal of generative modeling is to learn a given data distribution while facilitating an efficient way to draw samples from them . Popular algorithms such as variational autoencoders ( VAE , Kingma & Welling ( 2013 ) ) and generative adversarial networks ( GAN , Goodfellow et al . ( 2014 ) ) ar... | Description: The authors propose momentum contrastive Wasserstein autoencoders (MoCA), which is an extension of the Wasserstein autoencoder (WAE) that aims to match the prior p(z) and aggregate variational posterior q(z) through the use of contrastive learning, as opposed to earlier proposed techniques (MMD, GAN). The ... | SP:42784c9a7dce81ab38635d4619569766934abff7 |
Momentum Contrastive Autoencoder | 1 INTRODUCTION . The main goal of generative modeling is to learn a given data distribution while facilitating an efficient way to draw samples from them . Popular algorithms such as variational autoencoders ( VAE , Kingma & Welling ( 2013 ) ) and generative adversarial networks ( GAN , Goodfellow et al . ( 2014 ) ) ar... | This paper considers training autoencoders with a hyperspherical latent space distribution. The encoder maps inputs to latent variables with a unit norm, and a contrastive loss with momentum is used to encourage the latent variables to be distributed uniformly over the surface of the unit-hypersphere. Generations of th... | SP:42784c9a7dce81ab38635d4619569766934abff7 |
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization | 1 INTRODUCTION . Applications of reinforcement learning ( RL ) in real-world problems have been proven successful in many domains such as games ( Silver et al. , 2017 ; Vinyals et al. , 2019 ; Ye et al. , 2020 ) and robot control ( Johannink et al. , 2019 ) . However , the implementations so far usually rely on interac... | This paper tackles the problem of offline meta reinforcement learning, where an agent aims to learn a policy which can adapt to an unseen task (dynamics/reward), but from entirely offline data. As a result of being fully offline, the agent can no longer explore in the new task at test time, but instead receives randoml... | SP:4bafcd10c07834bb7e4008f5d089edda2c7a01e5 |
FOCAL: Efficient Fully-Offline Meta-Reinforcement Learning via Distance Metric Learning and Behavior Regularization | 1 INTRODUCTION . Applications of reinforcement learning ( RL ) in real-world problems have been proven successful in many domains such as games ( Silver et al. , 2017 ; Vinyals et al. , 2019 ; Ye et al. , 2020 ) and robot control ( Johannink et al. , 2019 ) . However , the implementations so far usually rely on interac... | The paper studies meta-reinforcement learning in the fully offline setting, and proposes a novel algorithm 'FOCAL'. Given offline datasets for tasks sampled from some prior, the algorithm learns a context encoder using distance-based metrics. The encoder is used for inferring the task-latent $z$, which is used to condi... | SP:4bafcd10c07834bb7e4008f5d089edda2c7a01e5 |
An information-theoretic framework for learning models of instance-independent label noise | 1 INTRODUCTION . Real-world datasets are inherently noisy . Although there are numerous existing methods for learning classifiers in the presence of label noise ( e.g . Han et al . ( 2018 ) ; Hendrycks et al . ( 2018 ) ; Natarajan et al . ( 2013 ) ; Tanaka et al . ( 2018 ) ) , there is still a gap between empirical suc... | The paper considers the problem of estimating instance-independent label noise. More formally, it is assumed that the true labels for any data point are modified based on a noise transition matrix, and the goal is to estimate this noise transition matrix. The paper proposed an information-theoretic approach for this ta... | SP:e2179d114157200f5a22928ba5ae5b41ff1342f4 |
An information-theoretic framework for learning models of instance-independent label noise | 1 INTRODUCTION . Real-world datasets are inherently noisy . Although there are numerous existing methods for learning classifiers in the presence of label noise ( e.g . Han et al . ( 2018 ) ; Hendrycks et al . ( 2018 ) ; Natarajan et al . ( 2013 ) ; Tanaka et al . ( 2018 ) ) , there is still a gap between empirical suc... | The paper aims to develop an information-theoretic framework for learning the underlying model from data with instance-independent label noises. It first formalizes the problem and relevant definitions with information measures, such as conditional entropy and conditional KL divergence, then argues that if the underlyi... | SP:e2179d114157200f5a22928ba5ae5b41ff1342f4 |
Unbiased Teacher for Semi-Supervised Object Detection | 1 INTRODUCTION . The availability of large-scale datasets and computational resources has allowed deep neural networks to achieve strong performance on a wide variety of tasks . However , training these networks requires a large number of labeled examples that are expensive to annotate and acquire . As an alternative ,... | This paper focuses on the pseudo-labeling bias issue in semi-supervised object detection (SS-OD), and proposes an Unbiased Teacher framework to address this issue. More specifically, the unbiased teacher framework combines the Mean Teacher model for semi-supervised image classification (Tarvainen and Valpola 2017) and ... | SP:17933143c0bc4e7957ce9ea48d8b50d55dd98570 |
Unbiased Teacher for Semi-Supervised Object Detection | 1 INTRODUCTION . The availability of large-scale datasets and computational resources has allowed deep neural networks to achieve strong performance on a wide variety of tasks . However , training these networks requires a large number of labeled examples that are expensive to annotate and acquire . As an alternative ,... | This work tackles the task of semi-supervised object detection via a teacher-student method. The authors introduced a training regime where a teacher and student network, who share the same initial weights pre-trained on labeled data, jointly learns on unsupervised data. They find that label imbalance in the object det... | SP:17933143c0bc4e7957ce9ea48d8b50d55dd98570 |
Revisiting Prioritized Experience Replay: A Value Perspective | 1 INTRODUCTION . Learning from important experiences prevails in nature . In rodent hippocampus , memories with higher importance , such as those associated with rewarding locations or large reward-prediction errors , are replayed more frequently ( Michon et al. , 2019 ; Roscow et al. , 2019 ; Salvetti et al. , 2014 ) ... | the idea of prioritized experience replay is revisited, but from a new perspective with new theoretical results. Here, the authors propose the expected value of backup (EVB) as a metric to assess the quality of a sample and its potential improvement on the policy and on the value function. The authors decompose this me... | SP:bc08cb09ecac842d09fd6e03ecf45b2fce9857b6 |
Revisiting Prioritized Experience Replay: A Value Perspective | 1 INTRODUCTION . Learning from important experiences prevails in nature . In rodent hippocampus , memories with higher importance , such as those associated with rewarding locations or large reward-prediction errors , are replayed more frequently ( Michon et al. , 2019 ; Roscow et al. , 2019 ; Salvetti et al. , 2014 ) ... | This work aimed to understand the prioritized experience replay, a widely used technique to improve learning efficiently for RL agents. The authors proposed three different value metrics to quantify the experience, and showed that they are upper bounded by the TD error (up to a constant). The extension to soft Q-learni... | SP:bc08cb09ecac842d09fd6e03ecf45b2fce9857b6 |
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference | 1 INTRODUCTION . The great advancement of deep learning techniques based on deep neural networks ( DNN ) has enabled more practical design of human-machine interfaces ( HMI ) through the analysis of the user ’ s physiological data ( Faust et al. , 2018 ) , such as electroencephalogram ( EEG ) ( Lawhern et al. , 2018 ) ... | The authors present a novel method dubbed AutoBayes that tries to find optimal Bayesian graph models for "nuisance-robust" deep learning. They employ the Bayes-Ball algorithm to construct reasonable inference graphs from a generative model given by iterative search. The corresponding DNN modules are then built/linked a... | SP:b2e738e35c3c6739200f87341678897be89771f7 |
AutoBayes: Automated Bayesian Graph Exploration for Nuisance-Robust Inference | 1 INTRODUCTION . The great advancement of deep learning techniques based on deep neural networks ( DNN ) has enabled more practical design of human-machine interfaces ( HMI ) through the analysis of the user ’ s physiological data ( Faust et al. , 2018 ) , such as electroencephalogram ( EEG ) ( Lawhern et al. , 2018 ) ... | The paper presents AutoBayes: a new approach for nuisance-robust deep learning which explores different Bayesian graph models to search for the best inference strategy. It automatically builds connections between classifier, encoder, decoder, nuisance estimator and adversary DNN blocks. The approach also enables disent... | SP:b2e738e35c3c6739200f87341678897be89771f7 |
Delay-Tolerant Local SGD for Efficient Distributed Training | 1 INTRODUCTION Data-parallel synchronous SGD is currently the workhorse algorithm for large-scale distributed deep learning tasks with many workers ( e.g . GPUs ) , where each worker calculates the stochastic gradient on local data and synchronizes with the other workers in one training iteration ( Goyal et al. , 2017 ... | This paper proposes OLCO3, a new delay-tolerant SGD communication scheme and training framework for distributed deep neural network training. OLCO3 combines the existing ideas of Stale synchronous Parallel, batching the communication of doing multiple iterations, and gradient compression to achieve more communication e... | SP:7ca5ba13170227684a45a4fef71675925b752f87 |
Delay-Tolerant Local SGD for Efficient Distributed Training | 1 INTRODUCTION Data-parallel synchronous SGD is currently the workhorse algorithm for large-scale distributed deep learning tasks with many workers ( e.g . GPUs ) , where each worker calculates the stochastic gradient on local data and synchronizes with the other workers in one training iteration ( Goyal et al. , 2017 ... | Review: This paper studies distributed training of neural networks. The major obstacles in distributed training are communication costs and communication delays. In the literature there exists different methods which attempt to overcome these two issues but, as far as the authors claim, none of the existing algorithms ... | SP:7ca5ba13170227684a45a4fef71675925b752f87 |
Tradeoffs in Data Augmentation: An Empirical Study | 1 INTRODUCTION . Models that achieve state-of-the-art in image classification often use heavy data augmentation strategies . The best techniques use various transforms applied sequentially and stochastically . Though the effectiveness of this is well-established , the mechanism through which these transformations work ... | This paper empirically investigates two crucial factors: affinity and diversity in useful data augmentation strategies. Through extensive experiments on existing image augmentation methods, it demonstrates that a good augmentation practice should bring high affinity and diversity for validation and training data. Speci... | SP:2112a176f4ade9d808d78e1795701da15a5c146a |
Tradeoffs in Data Augmentation: An Empirical Study | 1 INTRODUCTION . Models that achieve state-of-the-art in image classification often use heavy data augmentation strategies . The best techniques use various transforms applied sequentially and stochastically . Though the effectiveness of this is well-established , the mechanism through which these transformations work ... | This paper studies the problem of data augmentation that obtains new training examples by modifying existing ones. Data augmentation is popular in machine learning and artificial intelligence since it enhances the number of training examples. However, its effect on model performance remains unknown in practice. An augm... | SP:2112a176f4ade9d808d78e1795701da15a5c146a |
Graph Structural Aggregation for Explainable Learning | 1 INTRODUCTION . Convolution neural networks ( LeCun et al. , 1995 ) have proven to be very efficient at learning meaningful patterns for many articificial intelligence tasks . They convey the ability to learn hierarchical information in data with Euclidean grid-like structures such as images and text . Convolutional N... | This paper proposed the StructAgg, an aggregation algorithm in convolutional graph neural network that learns the structural roles for nodes in the graph embedding. In this algorithm, a structural representation of node is constructed through concatenation of latest p layers of node presentation in graph neural network... | SP:9281a2478824e6b7b300fa7f11d61a5e5d6c1679 |
Graph Structural Aggregation for Explainable Learning | 1 INTRODUCTION . Convolution neural networks ( LeCun et al. , 1995 ) have proven to be very efficient at learning meaningful patterns for many articificial intelligence tasks . They convey the ability to learn hierarchical information in data with Euclidean grid-like structures such as images and text . Convolutional N... | This paper focuses on deriving explainable features for use in graph classification. To that end, they propose StructAgg that is essentially an aggregation process based on the structural roles of nodes that is then used in an end-to-end model. Experiments demonstrate the effectiveness of the proposed approach as it pr... | SP:9281a2478824e6b7b300fa7f11d61a5e5d6c1679 |
Watching the World Go By: Representation Learning from Unlabeled Videos | 1 INTRODUCTION . The world seen through our eyes is constantly changing . As we move through the world , we see much more than a single static image : objects rotate revealing occluded regions , deform , the surroundings change , and we ourselves move . Our internal visual systems are constantly seeing temporally coher... | The idea of learning representations from video rather than single images is an appealing one with many favorable properties to allow a system to get direct signal on appearance of objects under various natural transformations (occlusion, lighting, etc). Combining instance discrimination ideas of loss based on unlabell... | SP:07bb9747aaae728b6e29880610dc5173af0f9e01 |
Watching the World Go By: Representation Learning from Unlabeled Videos | 1 INTRODUCTION . The world seen through our eyes is constantly changing . As we move through the world , we see much more than a single static image : objects rotate revealing occluded regions , deform , the surroundings change , and we ourselves move . Our internal visual systems are constantly seeing temporally coher... | This paper incorporate the popular contrastive with unsupervised learning from video. Specifically, multiple frames from the same video is used as positive pairs and frames from different videos is viewed as negative pair. The author also proposed a simple and effective ways to collect class-balanced and diverse video... | SP:07bb9747aaae728b6e29880610dc5173af0f9e01 |
PanRep: Universal node embeddings for heterogeneous graphs | 1 INTRODUCTION . Learning node representations from heterogeneous graph data powers the success of many downstream machine learning tasks such as node classification ( Kipf & Welling , 2017 ) , and link prediction ( Wang et al. , 2017 ) . Graph neural networks ( GNNs ) learn node embeddings by applying a sequence of no... | This paper proposes a universal and unsupervised GNN-based representation learning (node embedding pretraining) model named PanRep for heterogeneous graphs, which benefits a variety of downstream tasks such as node classification and link prediction. More specifically, employing an encoder similar to R-GCN, PanRep util... | SP:d3804a2538416b73935cbece4344fa8ad9d4bbe9 |
PanRep: Universal node embeddings for heterogeneous graphs | 1 INTRODUCTION . Learning node representations from heterogeneous graph data powers the success of many downstream machine learning tasks such as node classification ( Kipf & Welling , 2017 ) , and link prediction ( Wang et al. , 2017 ) . Graph neural networks ( GNNs ) learn node embeddings by applying a sequence of no... | This paper proposed introduces a problem formulation of universal unsupervised learning. They develop an unsupervised node representation learning method by combining four signals: (1) cluster and recover supervision, (2) motif supervision, (3) metapath random walk supervision, and (4) heterogeneous information maximiz... | SP:d3804a2538416b73935cbece4344fa8ad9d4bbe9 |
Correcting experience replay for multi-agent communication | 1 INTRODUCTION . Since the introduction of deep Q-learning ( Mnih et al. , 2013 ) , it has become very common to use previous online experience , for instance stored in a replay buffer , to train agents in an offline manner . An obvious difficulty with doing this is that the information concerned may be out of date , l... | The paper considers a multi-agent reinforcement learning (MARL) scenario where agents take actions based on the current observation alone. The paper proposes a communication correction mechanism where, during the centralized training, messages there were received in the past from other agents are reevaluated according ... | SP:deb1448308aa429b8a2f2cf2d27f98f87e367c83 |
Correcting experience replay for multi-agent communication | 1 INTRODUCTION . Since the introduction of deep Q-learning ( Mnih et al. , 2013 ) , it has become very common to use previous online experience , for instance stored in a replay buffer , to train agents in an offline manner . An obvious difficulty with doing this is that the information concerned may be out of date , l... | This paper considers communication games when agents use experience replay. The agents' communication protocol may change over time, leaving outdated symbols in the replay buffer which are then trained on. This paper proposes replacing the old communication actions with up-to-date actions as the transitions are sampled... | SP:deb1448308aa429b8a2f2cf2d27f98f87e367c83 |
Conditional Coverage Estimation for High-quality Prediction Intervals | Deep learning has achieved state-of-the-art performance to generate high-quality prediction intervals ( PIs ) for uncertainty quantification in regression tasks . The high-quality criterion requires PIs to be as narrow as possible , whilst maintaining a pre-specified level of data ( marginal ) coverage . However , most... | In the submitted paper, the authors study high-quality prediction intervals (PIs). The paper proposes a novel design of loss functions to generate PIs and conditional coverage estimates. The theoretical justification for using the conditional coverage error (in Ca-module) is presented and the numerical experiments with... | SP:c966fd016af0edb014beaaee492f136ea57c77aa |
Conditional Coverage Estimation for High-quality Prediction Intervals | Deep learning has achieved state-of-the-art performance to generate high-quality prediction intervals ( PIs ) for uncertainty quantification in regression tasks . The high-quality criterion requires PIs to be as narrow as possible , whilst maintaining a pre-specified level of data ( marginal ) coverage . However , most... | In the paper, the author addresses a calibration-based conditional coverage error in order to avoid the difficulty of conditional coverage, which provides a middle ground between marginal (no conditional information) and conditional coverage (high computational cost). The author generates the idea building on prior wor... | SP:c966fd016af0edb014beaaee492f136ea57c77aa |
EXPLORING VULNERABILITIES OF BERT-BASED APIS | 1 INTRODUCTION . The emergence of Bidirectional Encoder Representations from Transformers ( BERT ) ( Devlin et al. , 2018 ) has revolutionised the natural language processing ( NLP ) field , leading to state-of-the-art performance on a wide range of NLP tasks with minimal task-specific supervision . In the meantime , w... | This paper is studying the vulnerabilities of modern BERT-based classifiers, which a service provider is hosting using a black-box inference API. Consistent with prior work [2], the authors succeed in extracting high performing copies of the APIs, by training models using the outputs of the API to queries (akin to dist... | SP:38f7fc675b764f1d88f08c6eaaa0daa4b2351d37 |
EXPLORING VULNERABILITIES OF BERT-BASED APIS | 1 INTRODUCTION . The emergence of Bidirectional Encoder Representations from Transformers ( BERT ) ( Devlin et al. , 2018 ) has revolutionised the natural language processing ( NLP ) field , leading to state-of-the-art performance on a wide range of NLP tasks with minimal task-specific supervision . In the meantime , w... | The paper is motivated by a challenging problem in deploying a neural network-based model for sensitive domain and research in this direction is essential for making such model usable for sensitive domains. The paper presents a model extraction attack, where the adversary can steal a BERT- based API (i.e. the victim mo... | SP:38f7fc675b764f1d88f08c6eaaa0daa4b2351d37 |
Sample efficient Quality Diversity for neural continuous control | 1 INTRODUCTION . Natural evolution has the fascinating ability to produce organisms that are all high-performing in their respective niche . Inspired by this ability to produce a tremendous diversity of living systems within one run , Quality-Diversity ( QD ) is a new family of optimization algorithms that aim at searc... | This paper is addressing the problem of hard exploration / escaping local minima in continuous control, by optimizing a population of agents for both environment reward and diversity, using both off-policy RL and Quality-Diversity (QD) optimization. Each agent is individually optimized using off-policy RL for either en... | SP:3a035de1d4b8f02e2896e2376965b6a259ac9867 |
Sample efficient Quality Diversity for neural continuous control | 1 INTRODUCTION . Natural evolution has the fascinating ability to produce organisms that are all high-performing in their respective niche . Inspired by this ability to produce a tremendous diversity of living systems within one run , Quality-Diversity ( QD ) is a new family of optimization algorithms that aim at searc... | The authors describe a QD-RL algorithm to solve continuous control problems with neural controllers. The authors state that they maximize diversity within the population and ” the return of each individual agent”. Furthermore, the authors state that QD-RL selects agents from a Pareto front or from a Map-Elites grid. T... | SP:3a035de1d4b8f02e2896e2376965b6a259ac9867 |
AUL is a better optimization metric in PU learning | 1 INTRODUCTION . Classic binary classification tasks in machine learning usually assume that all data are fully labeled as positive or negative ( PN learning ) . However , in real-world applications , dataset is usually nonideal and only a small fraction of positive data are labeled . Training a model from such partial... | The paper argues that AUL is a better metric than AUC under the PU (positive and unlabeled data) learning setup in the sense that it leads to an unbiased estimator in this setting, which is not the case for the commonly used and known metric - AUC. It is also argued that it leads to better performance than those method... | SP:36bf1ac338d0c4184cad1369aabbe0662734a9aa |
AUL is a better optimization metric in PU learning | 1 INTRODUCTION . Classic binary classification tasks in machine learning usually assume that all data are fully labeled as positive or negative ( PN learning ) . However , in real-world applications , dataset is usually nonideal and only a small fraction of positive data are labeled . Training a model from such partial... | In this paper, the author proposed to use Area Under Lift chart (AUL) as a new optimization metric for positive unlabeled (PU) learning. The proposed AUL can be estimated unbiasedly from PU data, without the need to estimate the mixture proportions. Experiments on several datasets show that the proposed method outperfo... | SP:36bf1ac338d0c4184cad1369aabbe0662734a9aa |
Model-based Asynchronous Hyperparameter and Neural Architecture Search | 1 INTRODUCTION . The goal of hyperparameter and neural architecture search ( HNAS ) is to automate the process of finding the right architecture or hyperparameters x ? ∈ argminx∈X f ( x ) of a deep neural network by minimizing the validation loss f ( x ) , observed through noise : yi = f ( xi ) + i , i ∼ N ( 0 , σ2 ) ,... | The paper proposes a model-based asynchronous multi-fidelity method to optimize hyperparameters and perform a neural architecture search (NAS). The paper begins by addressing the differences between synchronous and asynchronous scheduling in Successive Halvining (SH) and its variants. It also analyzes the different sto... | SP:91933924103285d6fcac3649cf407b7021371625 |
Model-based Asynchronous Hyperparameter and Neural Architecture Search | 1 INTRODUCTION . The goal of hyperparameter and neural architecture search ( HNAS ) is to automate the process of finding the right architecture or hyperparameters x ? ∈ argminx∈X f ( x ) of a deep neural network by minimizing the validation loss f ( x ) , observed through noise : yi = f ( xi ) + i , i ∼ N ( 0 , σ2 ) ,... | This paper has proposed to exploit a GP model to represent the correlation between configuration-rung tuples (as is typical in multi-fidelity BO) in asynchronous successive halving (ASHA) (Li et al. 2018), which has resulted in performance improvement over the state of the art, as shown in the experimental results. The... | SP:91933924103285d6fcac3649cf407b7021371625 |
ATOM3D: Tasks On Molecules in Three Dimensions | 1 INTRODUCTION . A molecule ’ s three-dimensional ( 3D ) shape is critical to understanding its physical mechanisms of action , and can be used to answer a number of questions relating to drug discovery , molecular design , and fundamental biology . A molecule ’ s atoms often adopt specific 3D configurations that minim... | This paper presents a large benchmark of machine learning tasks for molecules represented by the 3D coordinates of their atoms. The benchmark is a combination of existing data sets and newly created ones, and covers a variety of applications and tasks, from small molecules to RNA or protein structures, and including cl... | SP:9bc80503d9771b780501b2dacac2cc37e4f5cd95 |
ATOM3D: Tasks On Molecules in Three Dimensions | 1 INTRODUCTION . A molecule ’ s three-dimensional ( 3D ) shape is critical to understanding its physical mechanisms of action , and can be used to answer a number of questions relating to drug discovery , molecular design , and fundamental biology . A molecule ’ s atoms often adopt specific 3D configurations that minim... | In this paper, the authors introduce a repository of datasets for several atomistic learning tasks. These datasets are processed into a simple and standardized format. A systematic benchmark with atomistic learning methods is presented, showcasing the value of using 3D atom-level data instead of 1D or 2D features. The ... | SP:9bc80503d9771b780501b2dacac2cc37e4f5cd95 |
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum | 1 Introduction . T-distributed stochastic neighbor embedding ( t-SNE ) ( van der Maaten & Hinton , 2008 ) is arguably among the most popular methods for low-dimensional visualizations of complex high-dimensional datasets . It defines pairwise similarities called affinities between points in the high-dimensional space a... | the authors study a number of neighbor embedding methods in terms of attraction-repulsion forces. The authors show that t-SNE, UMAP, FA2, and LE can be (approximately) unified as a common approach that use different levels of tradeoff between these two terms. They also discuss the increased attraction in UMAP as a resu... | SP:d616f1a6c241f03f2ddf2d171ecfb7689d61857d |
A Unifying Perspective on Neighbor Embeddings along the Attraction-Repulsion Spectrum | 1 Introduction . T-distributed stochastic neighbor embedding ( t-SNE ) ( van der Maaten & Hinton , 2008 ) is arguably among the most popular methods for low-dimensional visualizations of complex high-dimensional datasets . It defines pairwise similarities called affinities between points in the high-dimensional space a... | In this paper, a unified view of embedding methods for visualization is presented. The main message is that, Laplacian eigenmaps and t-SNE are governed by a single formula, and the difference of them can be seen as a difference of a hyperparameter value. We can also approximately recover two different embedding methods... | SP:d616f1a6c241f03f2ddf2d171ecfb7689d61857d |
Leveraging affinity cycle consistency to isolate factors of variation in learned representations | Identifying the dominant factors of variation across a dataset is a central goal of representation learning . Generative approaches lead to descriptions that are rich enough to recreate the data , but often only a partial description is needed to complete downstream tasks or to gain insights about the dataset . In this... | This paper applies a weakly-supervised learning approach to identify factors of object postures in an image dataset. The core idea is to introduce two sets of images. The first set is the reference data set with grouped objects of different active/inactive posture constraints. This set is used to provide weak supervisi... | SP:b65c6ca0d33243de3419efafb5f102512960d994 |
Leveraging affinity cycle consistency to isolate factors of variation in learned representations | Identifying the dominant factors of variation across a dataset is a central goal of representation learning . Generative approaches lead to descriptions that are rich enough to recreate the data , but often only a partial description is needed to complete downstream tasks or to gain insights about the dataset . In this... | The paper presents an approach to isolate factors of variation using weak supervision in the form of group labels. The proposed method Affinity Cycle Consistency (ACC) claims to work with these group labels, which are weaker than the more common, one factor per group type labeling. An important aspect of this approac... | SP:b65c6ca0d33243de3419efafb5f102512960d994 |
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback | 1 INTRODUCTION . Recent advances in user interfaces have enabled people with sensorimotor impairments to more effectively communicate their intent to machines . For example , Ward et al . ( 2000 ) enable users to type characters using an eye gaze tracker instead of a keyboard , and Willett et al . ( 2020 ) enable a par... | This work presents a method for online learning of an assistive typing user interface (XT2) with implicit user feedback. User inputs for such an assistive typing interface are assumed to be in the form of eye gaze or handwritten characters. However, the implicit human feedback is assumed to be backspaces typed on a key... | SP:75fbb95d000d888615a32a695fd6c673055b3678 |
X2T: Training an X-to-Text Typing Interface with Online Learning from User Feedback | 1 INTRODUCTION . Recent advances in user interfaces have enabled people with sensorimotor impairments to more effectively communicate their intent to machines . For example , Ward et al . ( 2000 ) enable users to type characters using an eye gaze tracker instead of a keyboard , and Willett et al . ( 2020 ) enable a par... | The authors propose a simple algorithm for using online learning from implicit human feedback to improve systems that operate in the contextual bandit setting. The main idea is to capture the presence/absence of corrective actions and use this information to infer a reward signal that the system can use to make decisio... | SP:75fbb95d000d888615a32a695fd6c673055b3678 |
Learning to Use Future Information in Simultaneous Translation | 1 INTRODUCTION . Neural machine translation ( NMT ) is an important task for the machine learning community and many advanced models have been designed ( Sutskever et al. , 2014 ; Bahdanau et al. , 2014 ; Vaswani et al. , 2017 ) . In this work , we work on a more challenging task in NMT , simultaneous translation ( als... | This paper proposes a new training method for wait-k simultaneous translation. Rather than training on prefix pairs where the target prefix lags the source by k tokens, it uses an RL controller to determine an optimal lag for each sentence pair. The controller uses a small set of features intended to capture training p... | SP:28a8d17fa8de3d51a3837f4e306facaafd416768 |
Learning to Use Future Information in Simultaneous Translation | 1 INTRODUCTION . Neural machine translation ( NMT ) is an important task for the machine learning community and many advanced models have been designed ( Sutskever et al. , 2014 ; Bahdanau et al. , 2014 ; Vaswani et al. , 2017 ) . In this work , we work on a more challenging task in NMT , simultaneous translation ( als... | This paper proposes a training strategy for simultaneous translation to choose appropriate amount of look-ahead information for each decoding. Based on the observation that the wait-k method can be improved by training with longer information, the method introduces a function to determine its length given the current e... | SP:28a8d17fa8de3d51a3837f4e306facaafd416768 |
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data | 1 INTRODUCTION . Inferring causal relations in observational time-series is central to many fields of scientific inquiry ( Berzuini et al. , 2012 ; Spirtes et al. , 2000 ) . Suppose you want to analyze fMRI data , which measures the activity of different brain regions over time — how can you infer the ( causal ) influe... | The authors proposed a framework called Amortized Causal Discovery (ACD) for recovering causal relationships in time series where samples are generated from models with different underlying causal graphs but shared dynamics. This framework is applicable in settings such as modeling neural spiking trains where the dynam... | SP:3cda613e93b67aa8b562cd2564f4d3583fe9f2e8 |
Amortized Causal Discovery: Learning to Infer Causal Graphs from Time-Series Data | 1 INTRODUCTION . Inferring causal relations in observational time-series is central to many fields of scientific inquiry ( Berzuini et al. , 2012 ; Spirtes et al. , 2000 ) . Suppose you want to analyze fMRI data , which measures the activity of different brain regions over time — how can you infer the ( causal ) influe... | The paper makes an observation that signal dynamics common to a class of causal systems may contain strong information to enable the use of the encoder of the Neural Relational Inference (2018) for extracting (Granger) causal graphs. It minimally extends the NRI model with an empty edge type and demonstrates that the o... | SP:3cda613e93b67aa8b562cd2564f4d3583fe9f2e8 |
Physics-aware, probabilistic model order reduction with guaranteed stability | Given ( small amounts of ) time-series ’ data from a high-dimensional , fine-grained , multiscale dynamical system , we propose a generative framework for learning an effective , lower-dimensional , coarse-grained dynamical model that is predictive of the fine-grained system ’ s long-term evolution but also of its beha... | The paper presents a generative approach to modeling physical systems with high-dimensional, nonlinear dynamical systems such as those found in fluid mechanics. The authors provide a physics-motivated hierarchical model for high-dimensional time series and a variational inference method for inferring latent variables a... | SP:7f210a3382b6840f84b182f0c72b9de2e89b0fe0 |
Physics-aware, probabilistic model order reduction with guaranteed stability | Given ( small amounts of ) time-series ’ data from a high-dimensional , fine-grained , multiscale dynamical system , we propose a generative framework for learning an effective , lower-dimensional , coarse-grained dynamical model that is predictive of the fine-grained system ’ s long-term evolution but also of its beha... | The paper proposes a generative model for learning a low-dimensional representation of a dynamical system from high-dimensional observations. The novelty of the approach is to introduce two latent spaces, one representing the standard physics-agnostic latent space learned from the data and one representing physics-moti... | SP:7f210a3382b6840f84b182f0c72b9de2e89b0fe0 |
Learning Disentangled Representations for Image Translation | 1 INTRODUCTION . Learning disentangled representations from a set of observations is a fundamental problem in machine learning . Such representations can facilitate generalization to downstream discriminative and generative tasks as well as improving interpretability ( Hsu et al. , 2017 ) , reasoning ( van Steenkiste e... | The paper presents a principled approach to style transfer by disentangling class-specific attributes from common (eq. class-independent) attributes. In order to do so, the paper leverages the formulation of a recently proposed disentangling approach called "LORD". The proposed approach is called OverLORD, and includes... | SP:e1b66646c8acdfa00bdfc0ec8740458d7e8b2d83 |
Learning Disentangled Representations for Image Translation | 1 INTRODUCTION . Learning disentangled representations from a set of observations is a fundamental problem in machine learning . Such representations can facilitate generalization to downstream discriminative and generative tasks as well as improving interpretability ( Hsu et al. , 2017 ) , reasoning ( van Steenkiste e... | This paper proposes a novel approach named OverLORD to learn disentangled representations for image class and attributes. To tackle the problem of previous methods that the learned content and class are often entangled, the authors propose to disentangle image representations to class and attributes, and further disent... | SP:e1b66646c8acdfa00bdfc0ec8740458d7e8b2d83 |
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies | 1 INTRODUCTION . Most machine learning models , including neural networks , operate on vector spaces . Therefore , when working with discrete objects such as text , we must define a method of converting objects into vectors . The standard way to map objects to continuous representations involves : 1 ) defining the voca... | In this paper, the authors proposed a method to learn efficient representations of discrete tokens. They took a two step approach: in step 1, they learn "full fledged" embeddings for a subset of anchor tokens. In step 2, they learn a sparse matrix that is used to relate all tokens to the set of chosen anchors. This two... | SP:111b19c01327c2eb1211e8ce7861378e76a64877 |
Anchor & Transform: Learning Sparse Embeddings for Large Vocabularies | 1 INTRODUCTION . Most machine learning models , including neural networks , operate on vector spaces . Therefore , when working with discrete objects such as text , we must define a method of converting objects into vectors . The standard way to map objects to continuous representations involves : 1 ) defining the voca... | This paper proposes ANT to solve the problem of learning Sparse embeddings instead of dense counterparts for tasks like Text Classification, Language Modeling and Recommendation Systems. When the vocabulary size |V| runs into several 100Ks or millions, it is impractical to store one dense vector per label. Hence the pa... | SP:111b19c01327c2eb1211e8ce7861378e76a64877 |
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning | 1 INTRODUCTION . With the success of RL in the single-agent domain ( Mnih et al . ( 2015 ) ; Lillicrap et al . ( 2015 ) ) , MARL is being actively studied and applied to real-world problems such as traffic control systems and connected self-driving cars , which can be modeled as multi-agent systems requiring coordinate... | - This paper proposes a Maximum Mutual Information framework for cooperative MARL. Following the insight that mutual information of agents’ policies is the indicator of coordination, this paper proposes VM3-AC, an MA-AC algorithm that optimizes long-term reward as well as a variational lower bound of mutual information... | SP:dc4dbc42defdc5f34bdfb2288fb33986ba348f8c |
A Maximum Mutual Information Framework for Multi-Agent Reinforcement Learning | 1 INTRODUCTION . With the success of RL in the single-agent domain ( Mnih et al . ( 2015 ) ; Lillicrap et al . ( 2015 ) ) , MARL is being actively studied and applied to real-world problems such as traffic control systems and connected self-driving cars , which can be modeled as multi-agent systems requiring coordinate... | The authors propose to include the mutual information between agents' simultaneous actions in the objective to encourage coordinated behaviour. To induce positive mutual information, the authors relax the assumption that the joint policy can be decomposed as the product of each agent's policy, independent of each other... | SP:dc4dbc42defdc5f34bdfb2288fb33986ba348f8c |
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) have achieved tremendous success in a variety of tasks involving sequential ( time series ) inputs and outputs , ranging from speech recognition to computer vision and natural language processing , among others . However , it is well known that training RNNs to proces... | This paper proposes a new continuous-time formulation for modeling recurrent units. The particular form of the recurrent unit is motivated by a system of coupled oscillators. These systems are well studied and widely used in the physical, engineering and biological sciences. Establishing this connection has the potenti... | SP:0a51115327ce08990aa3517ae1d20e88e80d6d65 |
Coupled Oscillatory Recurrent Neural Network (coRNN): An accurate and (gradient) stable architecture for learning long time dependencies | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) have achieved tremendous success in a variety of tasks involving sequential ( time series ) inputs and outputs , ranging from speech recognition to computer vision and natural language processing , among others . However , it is well known that training RNNs to proces... | Firstly, this paper conducts the rigorous analysis of the coRNN via the formula deduction to verify the bound. Then the coRNN is proved to mitigate the exploding and vanishing gradient problem and this is also validated in a series of experiments. Also, the performance of the coRNN is comparable or better compared to s... | SP:0a51115327ce08990aa3517ae1d20e88e80d6d65 |
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks | 1 INTRODUCTION . Recently , deep reinforcement learning ( RL ) has achieved a large number of breakthroughs in many domains including video games ( Mnih et al. , 2015 ; Vinyals et al. , 2019 ) , and board games ( Silver et al. , 2017 ) . Nonetheless , a central challenge in reinforcement learning ( RL ) is the sample e... | This paper proposes a new constraint for constrained MDP, based on k-shortest path, which helps improve sample efficiency for (model-free) RL algorithms in sparse-reward MDP, while theoretically proving that the constraint retains the same optimal policy in the original MDP. Intuitively, for sparse (positive) reward se... | SP:0f0d7119df7043ccea815c96e8896114210290f0 |
Shortest-Path Constrained Reinforcement Learning for Sparse Reward Tasks | 1 INTRODUCTION . Recently , deep reinforcement learning ( RL ) has achieved a large number of breakthroughs in many domains including video games ( Mnih et al. , 2015 ; Vinyals et al. , 2019 ) , and board games ( Silver et al. , 2017 ) . Nonetheless , a central challenge in reinforcement learning ( RL ) is the sample e... | This paper proposes the k-Shortest-Path (k-SP) constraint to restrict the agent’s trajectory to avoid redundant exploration and thus improves sample efficiency in sparse-reward MDPs. Specifically, k-SP constraint is applied to a trajectory rolled out by a policy where all of its sub-path of length k is required to be a... | SP:0f0d7119df7043ccea815c96e8896114210290f0 |
CPT: Efficient Deep Neural Network Training via Cyclic Precision | 1 INTRODUCTION . The record-breaking performance of modern deep neural networks ( DNNs ) comes at a prohibitive training cost due to the required massive training data and parameters , limiting the development of the highly demanded DNN-powered intelligent solutions for numerous applications ( Liu et al. , 2018 ; Wu et... | The authors proposed an interesting low-precision training method using a dynamic precision schedule. Their proposed Cyclic Precision Training (CPT) cyclically varies the precision during the training and the boundary of precision values is determined by a precision range test (PRT). As shown in their empirical results... | SP:df423ffa99360482fdfa31ad2c7e2ecedfa8bf5a |
CPT: Efficient Deep Neural Network Training via Cyclic Precision | 1 INTRODUCTION . The record-breaking performance of modern deep neural networks ( DNNs ) comes at a prohibitive training cost due to the required massive training data and parameters , limiting the development of the highly demanded DNN-powered intelligent solutions for numerous applications ( Liu et al. , 2018 ; Wu et... | The authors propose cyclic precision training (CPT), a method to train integer-quantized neural networks with high precision while saving bit operations. CPT alternates the numerical precision of the network during training between low (2-3 bits) and high (the final desired precision, e.g. 8 bits). A total of 32 cycles... | SP:df423ffa99360482fdfa31ad2c7e2ecedfa8bf5a |
Interpreting and Boosting Dropout from a Game-Theoretic View | 1 INTRODUCTION . Deep neural networks ( DNNs ) have exhibited significant success in various tasks , but the overfitting problem is still a considerable challenge for deep learning . Dropout is usually considered as an effective operation to alleviate the over-fitting problem of DNNs . Hinton et al . ( 2012 ) ; Srivast... | This paper analyzes the effect of dropout on interaction between units in a neural network. The strength of the interaction is measured using a metric that is used in game theory to quantify interaction between players in a co-operative game. The paper shows that dropout reduces high-order interaction (as measured by t... | SP:5f5615d414a232aeaec93033053471ce6bb09fc4 |
Interpreting and Boosting Dropout from a Game-Theoretic View | 1 INTRODUCTION . Deep neural networks ( DNNs ) have exhibited significant success in various tasks , but the overfitting problem is still a considerable challenge for deep learning . Dropout is usually considered as an effective operation to alleviate the over-fitting problem of DNNs . Hinton et al . ( 2012 ) ; Srivast... | This paper aims to explain dropout from the lens of game theoretic interactions. Let x denote the input of a deep neural net (DNN), intuitively, the interaction between two variables x_i and x_j quantifies how much the presence/absence of the j-th variable affects the contribution of the i-th variable to the output of ... | SP:5f5615d414a232aeaec93033053471ce6bb09fc4 |
Once Quantized for All: Progressively Searching for Quantized Compact Models | Automatic search of Quantized Neural Networks ( QNN ) has attracted a lot of attention . However , the existing quantization-aware Neural Architecture Search ( NAS ) approaches inherit a two-stage search-retrain schema , which is not only time-consuming but also adversely affected by the unreliable ranking of architect... | This paper presents a new method to search for quantized neural networks. This method is different from others that it results in quantized weights which can be deployed without post-process such as fine-tuning. Proposed method first trains a 4-bit quantized supernet, and search for the best performance sub-net using t... | SP:51a5349be44696d07c4bb9c6f94f2447022ceca3 |
Once Quantized for All: Progressively Searching for Quantized Compact Models | Automatic search of Quantized Neural Networks ( QNN ) has attracted a lot of attention . However , the existing quantization-aware Neural Architecture Search ( NAS ) approaches inherit a two-stage search-retrain schema , which is not only time-consuming but also adversely affected by the unreliable ranking of architect... | This paper proposed a method to train quantized supernets which can be directly deployed without retraining. The motivation is to have a supernet with a given quantization bit-width which only train once and can be deployed with different architectures (under different FLOPs budget). This paper made a bunch of experime... | SP:51a5349be44696d07c4bb9c6f94f2447022ceca3 |
Coping with Label Shift via Distributionally Robust Optimisation | 1 INTRODUCTION . Classical supervised learning involves learning a model from a training distribution that generalises well on test samples drawn from the same distribution . While the assumption of identical train and test distributions has given rise to useful methods , it is often violated in many practical settings... | This paper attacks the issue of mismatch in distribution of labels between train and test samples. The authors propose a DRO-based approach which amounts to solving a modified ERM problem. Compared to classical approaches, the proposed method doesn’t entail fitting many different models: just a single model. The method... | SP:f43350cd5ad80de839715bb0def9efaf1bed39fb |
Coping with Label Shift via Distributionally Robust Optimisation | 1 INTRODUCTION . Classical supervised learning involves learning a model from a training distribution that generalises well on test samples drawn from the same distribution . While the assumption of identical train and test distributions has given rise to useful methods , it is often violated in many practical settings... | This paper tackles label shift in supervised learning via distributionally robust optimization. The main idea is to train by solving a min-max problem, where the max problem searches for the worst-case label shift in an Kullback-Leibler divergence ambiguity set. The KL ambiguity set will generate some form of adversari... | SP:f43350cd5ad80de839715bb0def9efaf1bed39fb |
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning | 1 INTRODUCTION . Coronavirus disease 2019 ( COVID-19 ) has resulted in an ongoing pandemic in the world . To control the sources of infection and cut off the channels of transmission , rapid testing and detection are of vital importance . The reverse transcription polymerase chain reaction ( RT-PCR ) is a widely-used s... | The paper is an application of GCN with good features on chest CT scan images for Covid-19 diagnosis and prognosis. First of all this is a relevant and appreciated effort when the world is fighting the pandemic. Hence some bonus points is directed towards that. As a whole, to the representation learning community, it a... | SP:6dffc48a1e859d4fea90c951e8995ee38207819c |
Beyond COVID-19 Diagnosis: Prognosis with Hierarchical Graph Representation Learning | 1 INTRODUCTION . Coronavirus disease 2019 ( COVID-19 ) has resulted in an ongoing pandemic in the world . To control the sources of infection and cut off the channels of transmission , rapid testing and detection are of vital importance . The reverse transcription polymerase chain reaction ( RT-PCR ) is a widely-used s... | The manuscript proposes a distance aware pooling method to use in graph convolutional neural for predicting whether a subject is infected with Covid-19 (diagnosis) and progression of the disease (prognosis). Experiments were conducted on CT images from three groups: Covid-19 group, common pneumonia group, and heathy ... | SP:6dffc48a1e859d4fea90c951e8995ee38207819c |
Imitation with Neural Density Models | 1 Introduction . Imitation Learning ( IL ) algorithms aim to learn optimal behavior by mimicking expert demonstrations . Perhaps the simplest IL method is Behavioral Cloning ( BC ) ( Pomerleau , 1991 ) which ignores the dynamics of the underlying Markov Decision Process ( MDP ) that generated the demonstrations , and t... | This paper introduces an approach for imitation learning based on density estimation. The approach uses the previously introduced idea of minimizing some divergence between policy and expert occupancy measures, state-action distributions induced by these policies. The authors propose to first estimate expert occupancy ... | SP:4f12fdfd15f31f1a0ff9b57a8de53319750c3eec |
Imitation with Neural Density Models | 1 Introduction . Imitation Learning ( IL ) algorithms aim to learn optimal behavior by mimicking expert demonstrations . Perhaps the simplest IL method is Behavioral Cloning ( BC ) ( Pomerleau , 1991 ) which ignores the dynamics of the underlying Markov Decision Process ( MDP ) that generated the demonstrations , and t... | This work proposes a novel density matching method for learning from demonstration, which achieves state-of-the-art demonstration efficiency. Prior density matching methods utilize the adversarial methods suffers from the instability of optimization. To overcome this issue, this work proposes to separate the imitation ... | SP:4f12fdfd15f31f1a0ff9b57a8de53319750c3eec |
Incremental few-shot learning via vector quantization in deep embedded space | The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting . This challenge becomes greater when novel tasks contain very few labelled training samples . Currently , most methods are dedicated to class-incremental learning and rely on sufficien... | This paper proposes a nonparametric method in deep embedded space to address incremental few-shot learning problems. By compressing the learned tasks into a small number of reference vectors, the method could add more reference vectors to the model for each novel task, which could alleviate catastrophic forgetting and ... | SP:adf60fe287e9fcb67401f89701ba88b199df8700 |
Incremental few-shot learning via vector quantization in deep embedded space | The capability of incrementally learning new tasks without forgetting old ones is a challenging problem due to catastrophic forgetting . This challenge becomes greater when novel tasks contain very few labelled training samples . Currently , most methods are dedicated to class-incremental learning and rely on sufficien... | This paper suggests to use a generative model to address the problem of 'few shot' incremental learning. The idea is to classify input data by maintaining a population of prototypes and measuring the distance of the examples to be classified from these prototypes. As, each prototype represents a class, an example to be... | SP:adf60fe287e9fcb67401f89701ba88b199df8700 |
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift | 1 INTRODUCTION . The standard assumption in empirical risk minimization ( ERM ) is that the data distribution at test time will match the distribution at training time . When this assumption does not hold , the performance of standard ERM methods typically deteriorates rapidly , and this setting is commonly referred to... | This paper studies domain adaptation under the assumption that only unlabeled target data is available in training and the domain shift follows a special group shift. The main idea for the proposed method is having an adaptation model that takes only the unlabeled data in and output updated parameters. The proposed met... | SP:3f2e132cbd2eaf710316773a3f38c84c24f23b63 |
Adaptive Risk Minimization: A Meta-Learning Approach for Tackling Group Shift | 1 INTRODUCTION . The standard assumption in empirical risk minimization ( ERM ) is that the data distribution at test time will match the distribution at training time . When this assumption does not hold , the performance of standard ERM methods typically deteriorates rapidly , and this setting is commonly referred to... | The authors try to tackle the *distribution shift* problem with a meta learning approach. The algorithm, namely ARM, is proposed. Following regular meta learning regime, ARM uses an updated version of parameter $\theta'$ to calculate the loss for back propagation. Several specific implementations are put forward, i.e. ... | SP:3f2e132cbd2eaf710316773a3f38c84c24f23b63 |
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity | 1 Introduction . Deep neural networks have been applied to a wide range of artificial intelligence tasks such as computer vision , natural language processing , and signal processing with remarkable performance ( Ren et al. , 2015 ; Devlin et al. , 2018 ; Oord et al. , 2016 ) . However , it has been shown that neural n... | This paper proposes a new batch mixup method, co-mixup, to improve the networks’ generalization performance and robustness. It formulates the construction of a batch of mixup data by maximizing the data saliency measure of each individual mixup data and the supermodular diversity among the constructed mixup data. An it... | SP:a9be89f746c794d25c46d2da1feb6d06f93eb056 |
Co-Mixup: Saliency Guided Joint Mixup with Supermodular Diversity | 1 Introduction . Deep neural networks have been applied to a wide range of artificial intelligence tasks such as computer vision , natural language processing , and signal processing with remarkable performance ( Ren et al. , 2015 ; Devlin et al. , 2018 ; Oord et al. , 2016 ) . However , it has been shown that neural n... | This paper proposes a new mixup method that encourages diversity among the samples mixed from a minibatch of data in addition to saliency of each mixed sample. The authors formulate two objectives: 1. a BP set function (submodular + supermodular), and 2. a submodular relaxation obtained by modularizing the supermodular... | SP:a9be89f746c794d25c46d2da1feb6d06f93eb056 |
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems | 1 INTRODUCTION . The Large-Scale Vehicle Routing Problems ( VRPs ) is an important combinatorial optimization problem defined upon an enormous distribution of customer nodes , usually more than a thousand . An efficient and high-quality solution to large-scale VRPs is critical to many real-world applications . Meanwhil... | The paper presents a hierarchical reinforcement learning approach to solve large-scale vehicle routing problems (VRPs). A “rewriting agent” is responsible for dividing the customers into regions while a “generating agent” is responsible for computing the vehicle routes in each region, independently. The rewriting agent... | SP:cd167a1412b5c09594275811b7efebc358e2d121 |
Rewriting by Generating: Learn Heuristics for Large-scale Vehicle Routing Problems | 1 INTRODUCTION . The Large-Scale Vehicle Routing Problems ( VRPs ) is an important combinatorial optimization problem defined upon an enormous distribution of customer nodes , usually more than a thousand . An efficient and high-quality solution to large-scale VRPs is critical to many real-world applications . Meanwhil... | An RL based method, called Rewriting-by Generating (RBG), is proposed to solve large-scale VRPs. It borrows the idea of the hierarchical RL agent, which consists of two parts: "Generator" and "Rewriter". In the generation process, the graph is divided into several sections and in each section, an RL algorithm runs to g... | SP:cd167a1412b5c09594275811b7efebc358e2d121 |
A teacher-student framework to distill future trajectories | 1 INTRODUCTION . The ability to learn models of the world has long been argued to be an important ability of intelligent agents . An open and actively researched question is how to learn world models at the right level of abstraction . This paper argues , as others have before , that model-based and model-free methods ... | This paper proposes a learning framework for predicting the labels of dynamic systems. Unlike existing model-based approaches and model-free approaches, the proposed model takes a middle ground and uses a knowledge distillation-based framework. It uses a teacher model to learn to interpret a trajectory of the dynamic s... | SP:f4ff50a3da561f589df8ba890626f51efb5dcd1d |
A teacher-student framework to distill future trajectories | 1 INTRODUCTION . The ability to learn models of the world has long been argued to be an important ability of intelligent agents . An open and actively researched question is how to learn world models at the right level of abstraction . This paper argues , as others have before , that model-based and model-free methods ... | This paper proposes a teacher-student training scheme to incorporate the useful information of trajectory to improve the predictive performance of model-free methods. The teacher network tries to "guide" the student network at the training stage by presenting an interpretation of the trajectory. The guidance is impleme... | SP:f4ff50a3da561f589df8ba890626f51efb5dcd1d |
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks | 1 INTRODUCTION . Stochastic Gradient Descent ( SGD ) is a key algorithm in modern Machine Learning and optimization ( Amari , 1993 ) . To support distributed data as well as reduce training time , Zinkevich et al . ( 2010 ) introduced a distributed form of SGD . Traditionally , distributed SGD is run within a huband-sp... | This paper proposes a new variant of local SGD algorithm to make it be more realistic. In particular, (1) it allows workers to perform different number of local steps, depending on their computational resources; (2) workers are organized in a multi-level structure. Workers connected to one central hub can synchronize f... | SP:d2a8d90ecc5c406db6ffcd61e45dba647295a898 |
Multi-Level Local SGD: Distributed SGD for Heterogeneous Hierarchical Networks | 1 INTRODUCTION . Stochastic Gradient Descent ( SGD ) is a key algorithm in modern Machine Learning and optimization ( Amari , 1993 ) . To support distributed data as well as reduce training time , Zinkevich et al . ( 2010 ) introduced a distributed form of SGD . Traditionally , distributed SGD is run within a huband-sp... | This paper extends (Wang & Joshi, 2018) and proposes MLL-SGD for training models in hierarchic networks, where the network consists multiple sub-networks, and each sub-network contains multiple workers. In the level of sub-networks, models can be averaged. In the level of workers, the local copies of models can be aver... | SP:d2a8d90ecc5c406db6ffcd61e45dba647295a898 |
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization | 1 INTRODUCTION . In real-world machine learning applications , even well-curated training datasets have various types of heterogeneity . Two main types of heterogeneity are : ( 1 ) data imbalance : the input or label distribution often has a long-tailed density , and ( 2 ) heteroskedasticity : the labels given inputs h... | This paper proposed an adaptive regularization method to handle heteroskedastic and imbalanced datasets, which are closer to real-world large-scale settings. The framework applies a Lipschitz regularizer with varying regularization strength depending on the particular data point. The authors first theoretically study t... | SP:7be27202a84037a62bdc651fc24a8450325e0fd6 |
Heteroskedastic and Imbalanced Deep Learning with Adaptive Regularization | 1 INTRODUCTION . In real-world machine learning applications , even well-curated training datasets have various types of heterogeneity . Two main types of heterogeneity are : ( 1 ) data imbalance : the input or label distribution often has a long-tailed density , and ( 2 ) heteroskedasticity : the labels given inputs h... | The authors propose a novel regularization approach aimed at addressing issues of class imbalance and heteroskedasticity. This adaptive approach uses a Lipschitz regularizer with varying strength in different parts of the input space, regularizing harder in cases of rare and noisy examples. The authors derive the optio... | SP:7be27202a84037a62bdc651fc24a8450325e0fd6 |
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes | 1 INTRODUCTION . Deep models , formed by stacking together many simple layers , give rise to extremely powerful machine learning algorithms , from deep neural networks ( DNNs ) to deep Gaussian processes ( DGPs ) ( Damianou & Lawrence , 2013 ) . One approach to reason about uncertainty in these models is to use variati... | The paper proposed a new way of doing Bayesian deep learning in which the optimal conditional posterior for the last layer weights could be reached if the inducing input $Z_0$ is chosen to be the input data $X$ and the pseudo-observation for the last layer $V^L$ is the observation $Y$. Instead of factorizing the induci... | SP:cda2c05c55cce270fdb88ee63ad828dc7f91bc7a |
Global inducing point variational posteriors for Bayesian neural networks and deep Gaussian processes | 1 INTRODUCTION . Deep models , formed by stacking together many simple layers , give rise to extremely powerful machine learning algorithms , from deep neural networks ( DNNs ) to deep Gaussian processes ( DGPs ) ( Damianou & Lawrence , 2013 ) . One approach to reason about uncertainty in these models is to use variati... | This paper proposes a posterior approximation for BNN that models correlations between the layers weights. The paper begins by pointing out that, for any posterior distribution approximation, the optimal conditional posterior distribution over the top-layer weights given the weights of the previous layers has a closed-... | SP:cda2c05c55cce270fdb88ee63ad828dc7f91bc7a |
Learning Manifold Patch-Based Representations of Man-Made Shapes | Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications . Focusing on piecewise-smooth man-made shapes , we propose a new representation that is usable in conventional CAD modeling pipelines and can also be learned by deep neural networks . We demonstrate its... | This paper presents a method that leverages parametric surface patches as the fundamental representation in the task of shape modeling and reconstruction. This method requires a pre-generated template for each shape category. Several losses are specially designed to regularize the generation of the surface patches. Emp... | SP:71cba50055f4eaa6e1cc1f3cc40789788f26d60d |
Learning Manifold Patch-Based Representations of Man-Made Shapes | Choosing the right representation for geometry is crucial for making 3D models compatible with existing applications . Focusing on piecewise-smooth man-made shapes , we propose a new representation that is usable in conventional CAD modeling pipelines and can also be learned by deep neural networks . We demonstrate its... | The paper proposes a self-supervised method to fit a template (represented as a union of Coons patches) to a certain 2D sketch. It derives a way to build a proper template, uses a network to predict the patches' parameters, and proposes a union of different losses. The qualitative results of the method are shown in sev... | SP:71cba50055f4eaa6e1cc1f3cc40789788f26d60d |
A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference | 1 INTRODUCTION . The inference-time computational demands of deep neural networks ( DNNs ) are increasing , owing to the “ going deeper '' ( Szegedy et al. , 2015 ) strategy for improving accuracy : as a DNN gets deeper , it progressively gains the ability to learn higher-level , complex representations . This strategy... | This paper studies a new category of adversarial attacks, i.e., attackers that try to slow-down multi-exit DNNs using adversarial examples. The paper extended adversarial attacks to perform the slow-down attack and showed that the attacks could slow-down multi-exit DNNs by 1.5x - 5.0x. Additionally, the paper experimen... | SP:cb32c18a6a766894aa23e1f84ea9c38ef21fe023 |
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