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Rethinking Convolution: Towards an Optimal Efficiency | In this paper , we present our recent research about the computational efficiency in convolution . Convolution operation is the most critical component in recent surge of deep learning research . Conventional 2D convolution takes O ( C2K2HW ) to calculate , where C is the channel size , K is the kernel size , while H a... | This paper proposes a novel analysis for optimal separable convolution considering the number of parameters and FLOPs. The idea is to constraint the input information consumed stationery throughout the optimization of the parameters for separable convolutions. More specifically, this paper proposes the notion of volume... | SP:d50fb34a105a8212a0766593cc4f7edaea560f9f |
Meta Adversarial Training | 1 INTRODUCTION . Deep learning is currently the most promising method for open-world perception tasks such as in automated driving and robotics . However , the use in safety-critical domains is questionable , since a lack of robustness of deep learning-based perception has been demonstrated ( Szegedy et al. , 2014 ; Go... | This paper studies the use of meta learning and adversarial training to defend against universal perturbations. This approach tries to learn a set of perturbations through meta-learning and train the model to defend against such attacks. Experimental results on Tiny ImageNet and the Bosch small traffic lights dataset s... | SP:01813ebeda8ca73bca2bb3f50a3afefd1a04643b |
Meta Adversarial Training | 1 INTRODUCTION . Deep learning is currently the most promising method for open-world perception tasks such as in automated driving and robotics . However , the use in safety-critical domains is questionable , since a lack of robustness of deep learning-based perception has been demonstrated ( Szegedy et al. , 2014 ; Go... | The authors propose a novel meta adversarial training method. In particular, to improve the robustness against digital domain attacks, the proposed meta adversarial training (MAT) combines adversarial training and meta-learning, which reduces the computing cost compared with adversarial training by generating a set of ... | SP:01813ebeda8ca73bca2bb3f50a3afefd1a04643b |
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks | 1 INTRODUCTION . In the past decade , the emergence of practical deep neural networks arguably has had disruptive impact on applications of machine learning . Depth as such appears to be key to expressive models ( Raghu et al. , 2017 ) . However , depth also comes with challenges concerning training stability . Theoret... | The papers proposes an interesting analysis that links several aspects of architectural design in Deep NNs to the spectral analysis and observed roughness. Different activations functions are considered in the study, mainly centered on deep CNN with or without skip connections (in the framework of ResNet v1 and v2). Th... | SP:98db562b40246e689a13aa290e72031ef2bcea8d |
Ringing ReLUs: Harmonic Distortion Analysis of Nonlinear Feedforward Networks | 1 INTRODUCTION . In the past decade , the emergence of practical deep neural networks arguably has had disruptive impact on applications of machine learning . Depth as such appears to be key to expressive models ( Raghu et al. , 2017 ) . However , depth also comes with challenges concerning training stability . Theoret... | This paper proposes a new approach for how to analyze the ruggedness of the surface of the neural network loss. Specifically, the paper proposes to apply harmonic distortion on the weight-to-output (w-o) maps. That is, the method casts the w-o functions in the Fourier domain and then aggregate the surface characteristi... | SP:98db562b40246e689a13aa290e72031ef2bcea8d |
Regularization Cocktails for Tabular Datasets | 1 INTRODUCTION . In most supervised learning application domains , the available data for training predictive models is both limited and noisy with respect to the target variable . Therefore , it is paramount to regularize machine learning models for generalizing the predictive performance on future unseen data . The c... | This work takes a step towards understanding the effect of automated selection of regularisation techniques and analyses the results across 42 structured datasets. It defines a search space over 13 regularisation techniques and employs one flavour of Bayesian Optimisation + Hyperband approach to find an optimal combina... | SP:7b69dc66e28d4bfe787c40ee087749f27ffe6b98 |
Regularization Cocktails for Tabular Datasets | 1 INTRODUCTION . In most supervised learning application domains , the available data for training predictive models is both limited and noisy with respect to the target variable . Therefore , it is paramount to regularize machine learning models for generalizing the predictive performance on future unseen data . The c... | This paper provides an empirical study of combining different regularizers. Fourteen regularizers including batch norm, weight decay, etc. are considered. The authors use BOHB (Falkner et al. 2018) to optimize for whether each regularizer is active, and additional regularizer-specific hyperparameters. Using 40 tabular ... | SP:7b69dc66e28d4bfe787c40ee087749f27ffe6b98 |
Cross-Domain Few-Shot Learning by Representation Fusion | 1 INTRODUCTION . Currently , deep learning is criticized because it is data hungry , has limited capacity for transfer , insufficiently integrates prior knowledge , and presumes a largely stable world ( Marcus , 2018 ) . In particular , these problems appear after a domain shift , that is , a change of the input-target... | In this paper, the authors focus on cross-domain few-shot learning in the case of large source-target domain shifts. In particular, a new Cross-domain Hebbian Ensemble Few-shot (CHEF) learning method is proposed that performs representation fusion using an ensemble of Hebbian learners on different layers of a DNN train... | SP:b336aead05ceba504836cebfbf4f36516c94ca09 |
Cross-Domain Few-Shot Learning by Representation Fusion | 1 INTRODUCTION . Currently , deep learning is criticized because it is data hungry , has limited capacity for transfer , insufficiently integrates prior knowledge , and presumes a largely stable world ( Marcus , 2018 ) . In particular , these problems appear after a domain shift , that is , a change of the input-target... | This paper primarily deals with cross-domain few-shot learning. Under this setting, there is a large shift in domain going from the meta-train dataset to the few-shot datasets. Inspired by previous work, the authors argue that high-level concepts might not be useful in this setting but low-level concepts like edges, te... | SP:b336aead05ceba504836cebfbf4f36516c94ca09 |
Discrete Graph Structure Learning for Forecasting Multiple Time Series | 1 INTRODUCTION . Time series data are widely studied in science and engineering that involve temporal measurements . Time series forecasting is concerned with the prediction of future values based on observed ones in the past . It has played important roles in climate studies , market analysis , traffic control , and e... | The paper considers learning both graph structures and NNs for time series data, similar to the idea of LDS (Franceschi et al., 2019). Observing the computation and scalability issues with LDS, authors propose a unilevel optimization form wrt. the mean performance over the graph distribution. This is done via NNs, with... | SP:de1248456feddd9a8f2617e49f913e6585a9951f |
Discrete Graph Structure Learning for Forecasting Multiple Time Series | 1 INTRODUCTION . Time series data are widely studied in science and engineering that involve temporal measurements . Time series forecasting is concerned with the prediction of future values based on observed ones in the past . It has played important roles in climate studies , market analysis , traffic control , and e... | This paper proposes an approach for time series forecasting that learns the graph structure among multiple (multivariate) time series simultaneously with the parameters of a Graph Neural Network (GNN). The problem is formulated as learning a probabilistic graphical model by optimizing the expectation over the graph dis... | SP:de1248456feddd9a8f2617e49f913e6585a9951f |
Closing the Generalization Gap in One-Shot Object Detection | Search Task Generalization to Novel CategoriesA B 1 INTRODUCTION . It ’ s January 2021 and your long awaited household robot finally arrives . Equipped with the latest “ Deep Learning Technology ” , it can recognize over 21,000 objects . Your initial excitement quickly vanishes as you realize that your casserole is not... | This paper provides a variety of studies to understand the generalization gap between known and novel classes in one-shot object detection. The studies are carried out by using siamese Faster R-CNN framework on four benchmark datasets. The most notable observation was that it was more important to increase the number o... | SP:91139c0d3614e87c45be3e66110fb01813492e06 |
Closing the Generalization Gap in One-Shot Object Detection | Search Task Generalization to Novel CategoriesA B 1 INTRODUCTION . It ’ s January 2021 and your long awaited household robot finally arrives . Equipped with the latest “ Deep Learning Technology ” , it can recognize over 21,000 objects . Your initial excitement quickly vanishes as you realize that your casserole is not... | The paper suggests that a major factor for increasing few-shot performance in the few-shot object detection task is the number of categories in the base training set used to pre-train the few-shot model on a large set of data before it is adapted to novel categories using only a few (or even 1) examples. This effect is... | SP:91139c0d3614e87c45be3e66110fb01813492e06 |
Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data | 1 INTRODUCTION . This contribution focuses Gaussian Mixture Models ( GMMs ) , which represent a probabilistic unsupervised model for clustering and density estimation and allowing sampling and outlier detection . GMMs have been used in a wide range of scenarios , e.g. , Melnykov & Maitra ( 2010 ) . Commonly , free para... | A major concern about the paper is related to the unsupported claims and contribution throughout the paper. For example, the way the training copes with distribution shift or alleviate forgetting is not clear or elaborated on. Beyond the abstract and before the empirical validation no theory or justification is provide... | SP:5f9e6a9b02d4ed607e2943e4b78fca65a56edf15 |
Gradient-based training of Gaussian Mixture Models for High-Dimensional Streaming Data | 1 INTRODUCTION . This contribution focuses Gaussian Mixture Models ( GMMs ) , which represent a probabilistic unsupervised model for clustering and density estimation and allowing sampling and outlier detection . GMMs have been used in a wide range of scenarios , e.g. , Melnykov & Maitra ( 2010 ) . Commonly , free para... | This paper presented a stochastic gradient descent approach to learn a non-stationary high-dimensional Gaussian mixture model from online data. The authors identified 3 challenges - local optima, numerical instability, and catastrophic forgetting, and proposed to address these challenges respective with adaptive anneal... | SP:5f9e6a9b02d4ed607e2943e4b78fca65a56edf15 |
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks | 1 INTRODUCTION . As machine learning models are applied to more and more real-world tasks , addressing machine learning safety is becoming an increasingly pressing issue . Deep learning algorithms have been shown to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ; Goodfellow et al. , 2014 ; Papernot et a... | This paper is about generating adversarial examples for some target model and protecting from such attacks. Authors consider a setting when an adversary has access to some "similar to target " domain data, and can use this data to generate a surrogate model. Using this surrogate model an adversary can generate adversa... | SP:d489a94958b9f496aa7713249451c5ffe0c6892c |
Learn2Weight: Weights Transfer Defense against Similar-domain Adversarial Attacks | 1 INTRODUCTION . As machine learning models are applied to more and more real-world tasks , addressing machine learning safety is becoming an increasingly pressing issue . Deep learning algorithms have been shown to be vulnerable to adversarial examples ( Szegedy et al. , 2013 ; Goodfellow et al. , 2014 ; Papernot et a... | The paper considers the adversarial attacks via a surrogate model constructed using data from a different domain. The authors propose a defense from such attacks by a special kind of adversarial training inspired by the idea of domain adaptation. The idea can be useful but raises a lot of questions, especially when loo... | SP:d489a94958b9f496aa7713249451c5ffe0c6892c |
Normalizing Flows for Calibration and Recalibration | In machine learning , due to model misspecification and overfitting , estimates of the aleatoric uncertainty are often inaccurate . One approach to fix this is isotonic regression , in which a monotonic function is fit on a validation set to map the model ’ s CDF to an optimally calibrated CDF . However , this makes it... | The paper proposes to use normalizing flows to improve estimates of aleatoric uncertainty in regression tasks. First, the paper suggests that since normalizing flows can improve the flexibility of output distribution, they can be used to mitigate issues of underfitting. Second, the paper proposes an approach that uses ... | SP:bc770f3d7472b4a89f0773960ac365193f5e0754 |
Normalizing Flows for Calibration and Recalibration | In machine learning , due to model misspecification and overfitting , estimates of the aleatoric uncertainty are often inaccurate . One approach to fix this is isotonic regression , in which a monotonic function is fit on a validation set to map the model ’ s CDF to an optimally calibrated CDF . However , this makes it... | The authors propose an approach to calibrate conditional distribution estimation models. The approach uses normalizing flows to transform an existing model's predictions into a prediction that better matches the empirical quantiles to the theoretical quantiles. After this remapping procedure, the authors introduce a ne... | SP:bc770f3d7472b4a89f0773960ac365193f5e0754 |
Coordinated Multi-Agent Exploration Using Shared Goals | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) is an increasingly important field . Indeed , many real-world problems are naturally modeled using MARL techniques . For instance , tasks from areas as diverse as robot fleet coordination ( Swamy et al. , 2020 ; Hüttenrauch et al. , 2019 ) and au... | The paper introduces an exploration bonus tailored to multi-agent learning in the CTDE (centralized training, decentralizing execution) setting. The bonus works by: 1) dividing up the observation space into subspaces (in this case, corresponding to the entities, pairs of entities, triplets of entities, etc), 2) maintai... | SP:07924615714b3ca8e074692d1c0552c700951574 |
Coordinated Multi-Agent Exploration Using Shared Goals | 1 INTRODUCTION . Cooperative multi-agent reinforcement learning ( MARL ) is an increasingly important field . Indeed , many real-world problems are naturally modeled using MARL techniques . For instance , tasks from areas as diverse as robot fleet coordination ( Swamy et al. , 2020 ; Hüttenrauch et al. , 2019 ) and au... | The paper proposes to improve the exponential sample complexity of finding a coordinated multi-agent strategy by learning an exploration policy for each agent that conditions on a shared goal. The exploration policy is mixed with the normal RL policy according to a parameter alpha, which is scaled down over time. The s... | SP:07924615714b3ca8e074692d1c0552c700951574 |
Learning Continuous-Time Dynamics by Stochastic Differential Networks | 1 INTRODUCTION AND RELATED WORKS . Many real-world systems experience complicated stochastic dynamics over a continuous time period . The challenges on modeling the stochastic dynamics mainly come from two sources . First , the underlying state transitions of many systems are often uncertain , as they are placed in unp... | This paper aims to model the complicated continuous time-series by using SDE for modeling the latent state trajectories. The authors claim that using SDE instead of ODE for the latent states has higher flexibility to capture more complex dynamics. Then they propose a continuous-time versions of variational evidence low... | SP:a5b2b3f420b135829267204092e681f2b10d04fe |
Learning Continuous-Time Dynamics by Stochastic Differential Networks | 1 INTRODUCTION AND RELATED WORKS . Many real-world systems experience complicated stochastic dynamics over a continuous time period . The challenges on modeling the stochastic dynamics mainly come from two sources . First , the underlying state transitions of many systems are often uncertain , as they are placed in unp... | This paper introduces a latent variable model for high dimensional stochastic time-series. The model is akin to a VAE with RNNs that incorporate time-series data. The authors introduce two variants of the model, one which only contains a feedforward RNN (filtering) and another that contains feedforward and feedback RNN... | SP:a5b2b3f420b135829267204092e681f2b10d04fe |
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways | 1 INTRODUCTION . Imagine you are driving a car on a highway and suddenly an object appears in your visual field , crossing the road . Your initial reaction is to slam on the brakes even before recognizing the object . This highlights a core difference between human vision and current machine learning strategies for obj... | This paper proposed a two-pathway neural network to mimic the interplay between the parvocellular (slow and fine-grained) and magnocellular (fast and course) pathways in neural systems. The two pathways are named as FineNet and CourseNet. During inference, the FineNet received recurrent feedback signals from the Coarse... | SP:727502b110dd9d104b7ae9caa72a6e5f9c119a8d |
Vision at A Glance: Interplay between Fine and Coarse Information Processing Pathways | 1 INTRODUCTION . Imagine you are driving a car on a highway and suddenly an object appears in your visual field , crossing the road . Your initial reaction is to slam on the brakes even before recognizing the object . This highlights a core difference between human vision and current machine learning strategies for obj... | This paper proposes a dual-path CNN architecture with complementary roles (FineNet and CoarseNet) which is inspired by parvocellular and magnocellular pathways in the primate brain. The CoarseNet receives blurred inputs and has large kernels while FineNet received high-resolution input, is deep and has small kernels. I... | SP:727502b110dd9d104b7ae9caa72a6e5f9c119a8d |
Defective Convolutional Networks | 1 INTRODUCTION . Deep learning ( LeCun et al. , 1998 ; 2015 ) , especially deep Convolutional Neural Network ( CNN ) ( Krizhevsky et al. , 2012 ) , has led to state-of-the-art results spanning many machine learning fields ( Girshick , 2015 ; Chen et al. , 2018 ; Luo et al. , 2020 ) . Despite the great success in numero... | Studies suggest that CNNs that overly rely on texture features are more vulnerable to adversarial attacks. The authors of this paper propose a simple yet effective method "defective convolution" that randomly "disables" neurons on the convolution layer. The authors argue that by doing so, the CNN is encouraged to learn... | SP:b7853999d2be6a4e637097094d8088e0229d651e |
Defective Convolutional Networks | 1 INTRODUCTION . Deep learning ( LeCun et al. , 1998 ; 2015 ) , especially deep Convolutional Neural Network ( CNN ) ( Krizhevsky et al. , 2012 ) , has led to state-of-the-art results spanning many machine learning fields ( Girshick , 2015 ; Chen et al. , 2018 ; Luo et al. , 2020 ) . Despite the great success in numero... | The paper proposes a method to improve adversarial robustness of the current convolutional networks. The method is based on dropping outputs of a fraction of neurons. However, unlike in dropout the masks are kept fixed throughout training/inference and applied to the bottom layers of the network. This shifts the focus ... | SP:b7853999d2be6a4e637097094d8088e0229d651e |
Attainability and Optimality: The Equalized-Odds Fairness Revisited | 1 INTRODUCTION . As machine learning models become widespread in automated decision making systems , apart from the efficiency and accuracy of the prediction , their potential social consequence also gains increasing attention . To date , there is ample evidence that machine learning models have resulted in discriminat... | This paper studies the attainability of Equalized Odds fairness criterion in both the classification and regression problems. When the prediction function is deterministic, it shows that Equalized Odds may not be attainable under certain conditions. In contrast, if the prediction function is randomized, then Equalized ... | SP:b89605095e431ae84ab91c9831a03ef9a5843e17 |
Attainability and Optimality: The Equalized-Odds Fairness Revisited | 1 INTRODUCTION . As machine learning models become widespread in automated decision making systems , apart from the efficiency and accuracy of the prediction , their potential social consequence also gains increasing attention . To date , there is ample evidence that machine learning models have resulted in discriminat... | This paper studies under what conditions a classifier can satisfy the condition of equalized odds. The authors first prove an impossibility result which shows that (under linear non-gaussian case) any deterministic classifier cannot achieve equalized odds across the protected groups. This leads to the question of rando... | SP:b89605095e431ae84ab91c9831a03ef9a5843e17 |
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering | 1 INTRODUCTION . Stochastic processes are widely used in many fields to model time series that exhibit a random behaviour . In this work , we focus on processes that can be expressed as solutions of stochastic differential equations ( SDE ) of the form dXt = µ ( t , Xt ) dt+ σ ( t , Xt ) dWt , with certain assumptions ... | This paper introduces Neural Jump Ordinary Differential Equations as a method for learning models of continuous-time stochastic processes sampled at random time epochs. Specifically, the paper studies the problem of estimating the marginal conditional expectation (i.e., the L2 optimal approximation conditional on the a... | SP:c824a0fe491742bf809b2d3a90e47810f0ef6a5e |
Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filtering | 1 INTRODUCTION . Stochastic processes are widely used in many fields to model time series that exhibit a random behaviour . In this work , we focus on processes that can be expressed as solutions of stochastic differential equations ( SDE ) of the form dXt = µ ( t , Xt ) dt+ σ ( t , Xt ) dWt , with certain assumptions ... | The authors propose a method for learning the conditional expectation of stochastic process in an online fashion. The paper bears a considerable theoretical treatment, derived from the stochastic filtering literature, which is present both in the main body of the paper and the appendix. Besides the model, the paper als... | SP:c824a0fe491742bf809b2d3a90e47810f0ef6a5e |
Experience Replay with Likelihood-free Importance Weights | 1 INTRODUCTION . Deep reinforcement learning methods have achieved much success in a wide variety of domains ( Mnih et al. , 2016 ; Lillicrap et al. , 2015 ; Horgan et al. , 2018 ) . While on-policy methods ( Schulman et al. , 2017 ) are effective , using off-policy data often yields better sample efficiency ( Haarnoja... | The paper proposes a generally applicable modification to experience sampling in the context of actor-critic algorithms using a Q function as a critic. The modification is called "Likelihood-free Importance Weights" (LFIW). The authors describe the approach in Appendix A in the form of pseudocode. Comparing to a gener... | SP:06e17ef81ccba1f4406589bdf7b47780f06be8d9 |
Experience Replay with Likelihood-free Importance Weights | 1 INTRODUCTION . Deep reinforcement learning methods have achieved much success in a wide variety of domains ( Mnih et al. , 2016 ; Lillicrap et al. , 2015 ; Horgan et al. , 2018 ) . While on-policy methods ( Schulman et al. , 2017 ) are effective , using off-policy data often yields better sample efficiency ( Haarnoja... | This paper is on an experience replay approach, as applied to deep RL methods, that uses a density ratio between on-policy and off-policy experiences as the prioritization weights. The objective is to find appropriate bias-variance trade-offs for importance sampling from the replay buffer. In particular, there's the bi... | SP:06e17ef81ccba1f4406589bdf7b47780f06be8d9 |
Context-Agnostic Learning Using Synthetic Data | 1 INTRODUCTION . Despite recent advances in deep learning , one central challenge is the large amount of labelled training data required to achieve state-of-the-art performance . Procuring such volumes of high quality , reliably annotated data can be costly or even close to impossible ( e.g. , obtaining data to train a... | This paper proposed a context-agnostic learning approach that combines an object area and a context image (s.t. background image) to generate input synthetic image and train the model as context-independent. The proposed method is made more efficient by including this generation process in the training loop, compared t... | SP:07440014f05a06c121a4e56f6af6e31b2190dc70 |
Context-Agnostic Learning Using Synthetic Data | 1 INTRODUCTION . Despite recent advances in deep learning , one central challenge is the large amount of labelled training data required to achieve state-of-the-art performance . Procuring such volumes of high quality , reliably annotated data can be costly or even close to impossible ( e.g. , obtaining data to train a... | The paper defines the task of context-agnostic learning and proposes an algorithm to solve the problem while assuming the ability to sample objects and contexts independently. They propose to decompose factors contributing to the risk into two, context bias and object error. Based on this interpretation, an algorithm i... | SP:07440014f05a06c121a4e56f6af6e31b2190dc70 |
Compressing gradients in distributed SGD by exploiting their temporal correlation | 1 INTRODUCTION . Distributed optimization has become the norm for training machine learning models on large datasets . With the need to train bigger models on ever-growing datasets , scalability of distributed optimization has become a key focus in the research community . While an obvious solution to growing dataset s... | The authors present a new scheme for compressing gradients for use in distributed training. In addition to the previously proposed techniques of sending the sign of the gradient components along with the scale, and the use of error feedback (each sender tracks the error introduced by quantization, and adjusts future gr... | SP:14c3434519fa9e757c4c5e6869c7c3cb07511f24 |
Compressing gradients in distributed SGD by exploiting their temporal correlation | 1 INTRODUCTION . Distributed optimization has become the norm for training machine learning models on large datasets . With the need to train bigger models on ever-growing datasets , scalability of distributed optimization has become a key focus in the research community . While an obvious solution to growing dataset s... | This paper proposed an extension of blockwise scaled sign compressor in Zheng et al. (2019). The proposed method exploits the temporal correlation between two consecutive gradients. The authors show that one can have a higher compression rate by inserting distortion to the compressed gradient. A tighten bound is provid... | SP:14c3434519fa9e757c4c5e6869c7c3cb07511f24 |
Motif-Driven Contrastive Learning of Graph Representations | 1 INTRODUCTION . Graph-structured data , such as molecules and social networks , is ubiquitous in many scientific research areas and real-world applications . To represent graph characteristics , graph motifs were proposed in Milo et al . ( 2002 ) as significant subgraph patterns occurring frequently in graphs and unco... | This paper proposes to learn the sub-graph patterns from a collection of training graphs. The key idea is to partition each graph into segments and enforce a global clustering of the subgraphs. The partitioning is also guided through contrastive learning, i.e., subgraphs should have a larger similarity with the graph i... | SP:6ebd0f56ad29eeb2a152333873da0c5614607174 |
Motif-Driven Contrastive Learning of Graph Representations | 1 INTRODUCTION . Graph-structured data , such as molecules and social networks , is ubiquitous in many scientific research areas and real-world applications . To represent graph characteristics , graph motifs were proposed in Milo et al . ( 2002 ) as significant subgraph patterns occurring frequently in graphs and unco... | The paper describes a self-supervised framework to extract graph motifs and use them as input for downstream contrastive learning. The framework contains three components: (a) motif guided segmenter to derive node subgraphs, (b) a motif learning - a clustering task among the subgraphs to identify concrete graph motifs ... | SP:6ebd0f56ad29eeb2a152333873da0c5614607174 |
Ballroom Dance Movement Recognition Using a Smart Watch and Representation Learning | 1 INTRODUCTION . Recent work has used low-cost smart watches to track the movement of human body parts . ArmTrak tracks arm movement , assuming that the body and torso are stationary ( Shen et al. , 2016 ) . In this paper , we perform whole body movement recognition using a single smart watch , which is a hard problem ... | Authors propose an approach to perform classification of ballroom dance movements (called figures) captured by the sensing mechanism of a smartwatch and discriminated via different ANN architectures. The sequence of figures are modelled as a Marlov chain, which work in a generative+discriminative fashion to output the... | SP:368767f64b05defbb5e0c479759df4d251597745 |
Ballroom Dance Movement Recognition Using a Smart Watch and Representation Learning | 1 INTRODUCTION . Recent work has used low-cost smart watches to track the movement of human body parts . ArmTrak tracks arm movement , assuming that the body and torso are stationary ( Shen et al. , 2016 ) . In this paper , we perform whole body movement recognition using a single smart watch , which is a hard problem ... | The paper presents some classification results for ballroom dancing movements, as measured by inertial sensors on a smartwatch. The motivation is mixed - as a guide to dancers themselves and as an automatic grading mechanism for competition judges. However the sensors used can only measure a very limited aspect of the... | SP:368767f64b05defbb5e0c479759df4d251597745 |
Importance-based Multimodal Autoencoder | Integrating information from multiple modalities ( e.g. , verbal , acoustic and visual data ) into meaningful representations has seen great progress in recent years . However , two challenges are not sufficiently addressed by current approaches : ( 1 ) computationally efficient training of multimodal autoencoder netwo... | The paper proposes the IMA model, a scalable model that learns modality importances and robust multimodal representations through a novel cross-covariance based loss function. The proposed model performs unimodal inference in absence of modalities and also addresses the problem of detecting important subspaces in each ... | SP:4f201b7e397d5a6e30ca7cea4b379baa1a046899 |
Importance-based Multimodal Autoencoder | Integrating information from multiple modalities ( e.g. , verbal , acoustic and visual data ) into meaningful representations has seen great progress in recent years . However , two challenges are not sufficiently addressed by current approaches : ( 1 ) computationally efficient training of multimodal autoencoder netwo... | This paper presents a multimodal Autoencoder framework that learns the multimodal latent representations alongwith the importance of regions in each modality’s representation space in an unsupervised fashion. Multimodal fusion algorithms either use complex architecture representations or use disentangling joint represe... | SP:4f201b7e397d5a6e30ca7cea4b379baa1a046899 |
SOLAR: Sparse Orthogonal Learned and Random Embeddings | 1 INTRODUCTION . Embedding models have been the mainstay algorithms for several machine learning applications like Information Retrieval ( IR ) ( 8 ; 2 ) and Natural Language Processing ( NLP ) ( 21 ; 16 ; 31 ; 9 ) in the last decade . Embedding models are learned spin-offs from the low-rank approximation and Matrix Fa... | This submission addresses the problem of learning document embeddings for document retrieval/recommendation tasks. Such tasks are characterized by a large number of documents and a large set of semantic class labels. In contrast to the now standard approach of representing documents and their labels as dense low dimens... | SP:b3c08f134af295f65238e3ce338e941733858d7e |
SOLAR: Sparse Orthogonal Learned and Random Embeddings | 1 INTRODUCTION . Embedding models have been the mainstay algorithms for several machine learning applications like Information Retrieval ( IR ) ( 8 ; 2 ) and Natural Language Processing ( NLP ) ( 21 ; 16 ; 31 ; 9 ) in the last decade . Embedding models are learned spin-offs from the low-rank approximation and Matrix Fa... | The paper studies the problem of document retrieval using embedding based models. It argues that performing near-neighbour search on a large number of dense embeddings hurts performance and accuracy. As an alternative, the paper proposes SOLAR (SPARSE ORTHOGONAL LEARNED AND RANDOM EMBEDDINGS), a model which uses high-d... | SP:b3c08f134af295f65238e3ce338e941733858d7e |
Generative Scene Graph Networks | 1 INTRODUCTION . Learning to discover and represent objects purely from observations is at the core of human cognition ( Spelke & Kinzler , 2007 ) . Recent advances in unsupervised object-centric representation learning have enabled decomposition of scenes into objects ( Greff et al. , 2019 ; Lin et al. , 2020b ; Locat... | The paper presents a generative model for scenes that uses tree-structured latent variables to recursively decompose images into objects and parts, without any object or part supervision during training. The model is trained using variational inference. Experiments are performed on two new datasets (2D Shapes and Compo... | SP:643597431db07482ab2de551f78064a102b16c6c |
Generative Scene Graph Networks | 1 INTRODUCTION . Learning to discover and represent objects purely from observations is at the core of human cognition ( Spelke & Kinzler , 2007 ) . Recent advances in unsupervised object-centric representation learning have enabled decomposition of scenes into objects ( Greff et al. , 2019 ; Lin et al. , 2020b ; Locat... | Generative Scene Graph Networks (GSGN) is a variational auto-encoder with the intermediate representation being tree-like scene graphs. The leaf nodes stand for primitive parts and edges stand for poses to compose parts into objects recursively. The experiments are done in two image datasets of single color, simple sha... | SP:643597431db07482ab2de551f78064a102b16c6c |
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer | 1 INTRODUCTION . Computer vision models in the real-world are often frozen and not updated after deployment , yet they may encounter novel data in the environment . Unlike the typical supervised learning setting , class-incremental continual learning challenges the learner to incorporate new information as it sequentia... | This paper proposes class-incremental learning with unlabeled data correlated to labeled data, and a method to tackle it. The task can be considered as a variant of [Lee et al.], which has no assumption on the unlabeled dataset, while this paper assumes the correlation between labeled and unlabeled dataset explicitly. ... | SP:3c564f60a942d2b56589ce292cc233d137560152 |
Memory-Efficient Semi-Supervised Continual Learning: The World is its Own Replay Buffer | 1 INTRODUCTION . Computer vision models in the real-world are often frozen and not updated after deployment , yet they may encounter novel data in the environment . Unlike the typical supervised learning setting , class-incremental continual learning challenges the learner to incorporate new information as it sequentia... | This paper investigates a semi-supervised continual learning (SSCL) setting and proposes a new method called DistillMatch for this setting. The major contributions are: (1) The authors carefully design a realistic SSCL setting where object-object correlations between labeled and unlabeled sets are maintained through a ... | SP:3c564f60a942d2b56589ce292cc233d137560152 |
Can Kernel Transfer Operators Help Flow based Generative Models? | 1 INTRODUCTION . A flow-based generative model refers to a deep generative model composed using a set of invertible transformations . While GANs and VAEs remain the two dominant generative models in the community , flow based formulations have continually evolved and now offer competitive performance in applications in... | The authors build a generator that builds on top of the latent space of a “well-trained auto-encoder”. The generator consists of several steps: 1) sampling an latent element from the spherical latent space, 2) using a kernel Perron-Frobenius operator to embed the sampled latent element 3) selecting latent representatio... | SP:b9fb99a65e598d0e0b1a97bc04dfc80865216541 |
Can Kernel Transfer Operators Help Flow based Generative Models? | 1 INTRODUCTION . A flow-based generative model refers to a deep generative model composed using a set of invertible transformations . While GANs and VAEs remain the two dominant generative models in the community , flow based formulations have continually evolved and now offer competitive performance in applications in... | This paper starts with an autoencoder trained on vision data. Autoencoders aren't generative models, strictly, so in order to make it so, they leverage a simple linear transformation on over RKHSs. The kernel they use is the NTK. In order to generate, the construct a reduced sample version of the Kernel using Nystrom's... | SP:b9fb99a65e598d0e0b1a97bc04dfc80865216541 |
Generalisation Guarantees For Continual Learning With Orthogonal Gradient Descent | 1 INTRODUCTION . Continual Learning is a setting in which an agent is exposed to multiples tasks sequentially ( Kirkpatrick et al. , 2016 ) . The core challenge lies in the ability of the agent to learn the new tasks while retaining the knowledge acquired from previous tasks . Too much plasticity ( Nguyen et al. , 2018... | The authors use a Neural Tangent Kernel (NTK) approximation of wide neural nets to establish generalization bounds for continual learning (CL) using stochastic gradient descent (SGD) and orthogonal gradient descent (OGD). In this regime, the authors prove that OGD does not suffer from catastrophic forgetting of traini... | SP:1b467d99fd9fb26c374247e68873d34596705f75 |
Generalisation Guarantees For Continual Learning With Orthogonal Gradient Descent | 1 INTRODUCTION . Continual Learning is a setting in which an agent is exposed to multiples tasks sequentially ( Kirkpatrick et al. , 2016 ) . The core challenge lies in the ability of the agent to learn the new tasks while retaining the knowledge acquired from previous tasks . Too much plasticity ( Nguyen et al. , 2018... | The paper provides a theoretical analysis on the OGD based continual learning method. The method is in fact proposed by a previous paper (Farajtabar et al. 2019) and the current paper shows a generalization bound for the regression case. The result (Thm 3) compares the generalization bounds between SGD and OGD and show... | SP:1b467d99fd9fb26c374247e68873d34596705f75 |
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation | Polymers appear everywhere in our daily lives – fabrics , plastics , rubbers , etc . – and we could hardly live without them . To make polymers , chemists develop processes that combine smaller building blocks ( monomers ) to form long chains or complex networks ( polymers ) . These processes are called polymerizations... | This paper focuses on polymer retro synthesis problem. This is a novel problem and is very challenging because of the very small amounts of training data available (<100). They use reaction templates collected from small molecule reactions and formulate polymer retrosynthesis as a constrained optimization problem. The ... | SP:11a15c3a8b83e911ab4ce1193871b468656a63ac |
PolyRetro: Few-shot Polymer Retrosynthesis via Domain Adaptation | Polymers appear everywhere in our daily lives – fabrics , plastics , rubbers , etc . – and we could hardly live without them . To make polymers , chemists develop processes that combine smaller building blocks ( monomers ) to form long chains or complex networks ( polymers ) . These processes are called polymerizations... | This paper proposes a method for the retrosynthesis prediction of polymers. A challenge in this problem is the lack of synthetic data for polymers. The method attempts to leverage models for small molecule retrosynthesis predictions (where there is more abundant data), as well as domain specific constraints derived fro... | SP:11a15c3a8b83e911ab4ce1193871b468656a63ac |
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients | 1 INTRODUCTION . At present , the healthcare system is unable to sufficiently leverage the large , unlabelled datasets that it generates on a daily basis . This is partially due to the dependence of deep learning algorithms on high quality labels for good generalization performance . However , arriving at such high qua... | This paper proposes to use contrastive learning to learn representations from cardiac signals (ECGs). The model incorporates ECG domain knowledge, patient-specific, and relationships between multiple leads (channels), in the learning process. The targeted task is very important, enormous ECGs are collected and stored, ... | SP:971a6f6ec230b9804ce5c14fa75eb1a2cf516249 |
CLOCS: Contrastive Learning of Cardiac Signals Across Space, Time, and Patients | 1 INTRODUCTION . At present , the healthcare system is unable to sufficiently leverage the large , unlabelled datasets that it generates on a daily basis . This is partially due to the dependence of deep learning algorithms on high quality labels for good generalization performance . However , arriving at such high qua... | This work presents a new self-supervised training framework for multi-channel ECG signals. The authors use contrastive learning by exploiting the fact that a single patient can generate multiple ECG signals, and there are multiple views (i.e. leads) for the same ECG signals. Compared to popular self-supervised training... | SP:971a6f6ec230b9804ce5c14fa75eb1a2cf516249 |
Generative Learning With Euler Particle Transport | 1 INTRODUCTION . The ability to efficiently sample from complex distributions plays a key role in a variety of prediction and inference tasks in machine learning and statistics ( Salakhutdinov , 2015 ) . The long-standing methodology for learning an underlying distribution relies on an explicit statistical data model ,... | This paper considers generative learning by discretizing a Wasserstein gradient with Euler methods. More precisely, some samples of a target distribution are given and the goal is to pushforward some samples of an initial distribution to the target distribution. The proposed method is obtained by minimizing the f-dive... | SP:701fd2e93907ea7c0c9c6e70d8eac8d91250e023 |
Generative Learning With Euler Particle Transport | 1 INTRODUCTION . The ability to efficiently sample from complex distributions plays a key role in a variety of prediction and inference tasks in machine learning and statistics ( Salakhutdinov , 2015 ) . The long-standing methodology for learning an underlying distribution relies on an explicit statistical data model ,... | This paper tackles generative modeling (sampling, in particular) via finding the push forward functions T (equivalently, the velocity fields v) that iteratively moves particles from a reference distribution toward the target data distribution. The velocity fields are solved by minimizing the f-divergence between the pa... | SP:701fd2e93907ea7c0c9c6e70d8eac8d91250e023 |
Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning | As an important problem of causal inference , we discuss the estimation of treatment effects under the existence of unobserved confounding . By representing the confounder as a latent variable , we propose Counterfactual VAE , a new variant of variational autoencoder , based on recent advances in identifiability of rep... | The present paper introduces Counterfactual VAE (CFVAE), a generative learning method to estimate treatment effects under a latent unconfoundedness assumption. It builds on variational autoencoders (VAE) to learn causal representations. The authors provide identification results using recent results on nonlinear ICA (K... | SP:79570f7ba5c60925f5612cf5e1f8b46a5332d880 |
Identifying Treatment Effects under Unobserved Confounding by Causal Representation Learning | As an important problem of causal inference , we discuss the estimation of treatment effects under the existence of unobserved confounding . By representing the confounder as a latent variable , we propose Counterfactual VAE , a new variant of variational autoencoder , based on recent advances in identifiability of rep... | This paper provides a method for using a VAE with proxy variables to estimate CATE in a model with latent confounding by recovering a conditional distribution over the latent confounders. Building upon results from Khemakhem et.al. 2020, the confounding can be identified if the latent variable is parameterized by an ex... | SP:79570f7ba5c60925f5612cf5e1f8b46a5332d880 |
Contrastive Syn-to-Real Generalization | 1 INTRODUCTION . Deep neural networks have pushed the boundaries of many visual recognition tasks . However , their success often hinges on the availability of both training data and labels . Obtaining data and labels can be difficult or expensive in many applications such as semantic segmentation , correspondence , 3D... | Synthetic-to-real generalization is an important topic of extensive practical interest. This paper motivates its work from an observation of feature diversity difference between synthetic and real training: synthetic training tends to generate less diverse or even collapsed features, whereas models trained on natural i... | SP:4522ae8f5aaec18049aafa53746c3bc337d620db |
Contrastive Syn-to-Real Generalization | 1 INTRODUCTION . Deep neural networks have pushed the boundaries of many visual recognition tasks . However , their success often hinges on the availability of both training data and labels . Obtaining data and labels can be difficult or expensive in many applications such as semantic segmentation , correspondence , 3D... | This paper focuses on the domain generalization problem where the source domain contains synthetic data. An interesting phenomenon is observed in this paper: the diversity of the learned feature embeddings plays an important role in the generalization performance. Then, this paper presents a method to address the syn-t... | SP:4522ae8f5aaec18049aafa53746c3bc337d620db |
Uniform-Precision Neural Network Quantization via Neural Channel Expansion | 1 INTRODUCTION . Deep neural networks ( DNNs ) have reached human-level performance in a wide range of domains including image processing ( He et al . ( 2016 ) ; Tan & Le ( 2019 ) ) , object detection ( Ren et al . ( 2015 ) ; Liu et al . ( 2016 ) ; Tan et al . ( 2020 ) ) , machine translation ( Wu et al . ( 2016 ) ; De... | The authors propose neural channel expansion (NCE), a neural architecture search (NAS) and quantization method. Existing NAS+Q methods typically search for the architecture of the DNN along with the precision at each layer, maximizing accuracy while respecting some kind of hardware constraint. The result is a DNN with ... | SP:4b23ef2262646d8e924f67770af22618f5e83b39 |
Uniform-Precision Neural Network Quantization via Neural Channel Expansion | 1 INTRODUCTION . Deep neural networks ( DNNs ) have reached human-level performance in a wide range of domains including image processing ( He et al . ( 2016 ) ; Tan & Le ( 2019 ) ) , object detection ( Ren et al . ( 2015 ) ; Liu et al . ( 2016 ) ; Tan et al . ( 2020 ) ) , machine translation ( Wu et al . ( 2016 ) ; De... | In this paper, the authors propose neural channel expansion (NCE) to adjust the network structure to compensate for the performance degradation from uniform-precision quantization. Given a hardware constraint, the proposed NCE selectively expands the width for the quantization sensitive layers. Experiments on CIFAR-10 ... | SP:4b23ef2262646d8e924f67770af22618f5e83b39 |
Improved Denoising Diffusion Probabilistic Models | 1 INTRODUCTION . Sohl-Dickstein et al . ( 2015 ) introduced diffusion probabilistic models ( `` diffusion models '' for brevity ) , a class of generative models which match a data distribution by learning to reverse a gradual , multi-step noising process . More recently , Ho et al . ( 2020 ) showed an equivalence betwe... | The paper talks builds upon the recent work from Ho (2020) about generative models that use noise diffusion. The authors suggest that the proposal in Ho can not only be used in good quality sample generation (as already shown by Ho), but also leads to reasonable improvements in likelihood. Overall, some of the ideas pr... | SP:30e7bbca17b264e59fc856365ad7dcf6081c7861 |
Improved Denoising Diffusion Probabilistic Models | 1 INTRODUCTION . Sohl-Dickstein et al . ( 2015 ) introduced diffusion probabilistic models ( `` diffusion models '' for brevity ) , a class of generative models which match a data distribution by learning to reverse a gradual , multi-step noising process . More recently , Ho et al . ( 2020 ) showed an equivalence betwe... | The paper found several methods to improve log likelihood of diffusion models while maintain their sample quality, including cosine instead of linear noise schedule, using a hybrid objective to learn parameters of the covariance function, and using importance sampling to improve the gradient noise. The authors also exp... | SP:30e7bbca17b264e59fc856365ad7dcf6081c7861 |
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning | 1 INTRODUCTION . The recent extraordinary success of deep learning methods can be mostly attributed to advancements in learning algorithms and the availability of large-scale labeled datasets . However , constructing large labeled datasets for supervised learning tends to be costly and is often infeasible . Several app... | This paper is in defense of simple semi-supervised learning (SSL) with pseudo-labeling (PL): authors demonstrate with experiments on 4 vision datasets (CIFAR-10, CIFAR-100, Pascal VOC and UCF-101) that pseudo-labeling can perform on par with consistency regularization methods. Authors argue that PL doesn't work well be... | SP:2eb629c9ac83f2068ae67b00c10c5c9ba11bbc13 |
In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning | 1 INTRODUCTION . The recent extraordinary success of deep learning methods can be mostly attributed to advancements in learning algorithms and the availability of large-scale labeled datasets . However , constructing large labeled datasets for supervised learning tends to be costly and is often infeasible . Several app... | As noted in (Guo et al., 2017), modern neural networks are often miscalibrated. Pseudo-labeling based Semi-Supervised learning schemes are predicated on high confidence predictions from these neural networks. This paper posits that this miscalibration may lead to inferior results in confidence-based pseudo-labeling app... | SP:2eb629c9ac83f2068ae67b00c10c5c9ba11bbc13 |
Adversarial Synthetic Datasets for Neural Program Synthesis | 1 INTRODUCTION . Program synthesis has long been a key goal of AI research . In particular , researchers have become increasingly interested in the task of programming by example ( PBE ) , where the goal is to generate a program consistent with a given set of input-output ( I/O ) pairs . Recent studies have achieved im... | Synthesis models trained on synthetic datasets of randomly generated programs and corresponding IO pairs often fail to generalize to real-world PBE problems. The models often learn specific aspects of the synthetic data distribution rather than the semantics of the programming language. This work proposes a more princi... | SP:2a886848c0832643ba2bd13804fe3fe8104a33bb |
Adversarial Synthetic Datasets for Neural Program Synthesis | 1 INTRODUCTION . Program synthesis has long been a key goal of AI research . In particular , researchers have become increasingly interested in the task of programming by example ( PBE ) , where the goal is to generate a program consistent with a given set of input-output ( I/O ) pairs . Recent studies have achieved im... | The paper is proposing to evolve datasets of (inputs, outputs) in the context of programming by example (PBE), where the goal is to infer a computer program that is consistent with the association of (inputs, outputs). The justification of the approach is to figure out instances that would allow to learn a model with P... | SP:2a886848c0832643ba2bd13804fe3fe8104a33bb |
Understanding the role of importance weighting for deep learning | 1 INTRODUCTION . Importance weighting is a standard tool for estimating a quantity under a target distribution while only the samples from some source distribution is accessible . It has been drawing extensive attention in the communities of statistics and machine learning . Causal inference for deep learning investiga... | It is now well-understood that when the data are linearly separable, gradient descent over the linear class of functions converges toward the hard margin solution. It highlights the implicit bias of gradient descent. Among all solutions interpolating the dataset, gradient descent selects the one with larger margin, pa... | SP:c36ebda129cbecfd9279410d276bb365cd5676eb |
Understanding the role of importance weighting for deep learning | 1 INTRODUCTION . Importance weighting is a standard tool for estimating a quantity under a target distribution while only the samples from some source distribution is accessible . It has been drawing extensive attention in the communities of statistics and machine learning . Causal inference for deep learning investiga... | This paper studies the inductive bias of gradient descent (GD) on smooth non-linear models when optimizing a weighted ERM. The authors provide several novel results for the linear and non-linear model cases. For linear models and linearly separable data, they show that GD converges to the hard-margin SVM solution and t... | SP:c36ebda129cbecfd9279410d276bb365cd5676eb |
Provable More Data Hurt in High Dimensional Least Squares Estimator | 1 INTRODUCTION . More data hurt refers to the phenomenon that training on more data can hurt the prediction performance of the learned model , especially for some deep learning tasks . Loog et al . ( 2019 ) shows that various standard learners can lead to sample-wise non-monotonicity . Nakkiran et al . ( 2019 ) experim... | In this article, the authors characterized the second-order fluctuation of the prediction risk of the (min-norm) least square estimator, by assuming an underlying noisy teacher model $y_i = \beta^T x_i + \epsilon_i$, in the regime where the data dimension $p$ and the number of training samples $n$ grow large at the sam... | SP:c57d966aea7e3a714f81845afed92f4ffa730626 |
Provable More Data Hurt in High Dimensional Least Squares Estimator | 1 INTRODUCTION . More data hurt refers to the phenomenon that training on more data can hurt the prediction performance of the learned model , especially for some deep learning tasks . Loog et al . ( 2019 ) shows that various standard learners can lead to sample-wise non-monotonicity . Nakkiran et al . ( 2019 ) experim... | This paper investigates the phenomenon of double descent, also referred to as "more data hurts", in high dimensional linear regression using the least square estimator. In the same setup, previous sharp results were already established in the asymptotic regime. Non-asymptotic results are also known but are less precis... | SP:c57d966aea7e3a714f81845afed92f4ffa730626 |
Reducing Class Collapse in Metric Learning with Easy Positive Sampling | 1 INTRODUCTION . Metric learning aims to learn an embedding function to lower dimensional space , in which semantic similarity translates to neighborhood relations in the embedding space ( Lowe , 1995 ) . Deep metric learning approaches achieve promising results in a large variety of tasks such as face identification (... | This paper proposes/adopts a simple positive sampling scheme in metric learning: only sampling the easiest positive for each anchor. Authors give a theoretical analysis of how the proposed sampling scheme can reduce class collapse. Experiments on fine-grain retrieval datasets show the effectiveness of the sampling sche... | SP:152f5c76e34d2b7acdafa37763be4bb51aa9ce6f |
Reducing Class Collapse in Metric Learning with Easy Positive Sampling | 1 INTRODUCTION . Metric learning aims to learn an embedding function to lower dimensional space , in which semantic similarity translates to neighborhood relations in the embedding space ( Lowe , 1995 ) . Deep metric learning approaches achieve promising results in a large variety of tasks such as face identification (... | The authors find that the popular triplet loss will force all same-class instances to a single center in a noisy scenario, which is not optimal to deal with the diverse and distinct sub-classes. After some analyses, the authors propose a simple sampling strategy, EPS, where anchors only pull the most similar instances.... | SP:152f5c76e34d2b7acdafa37763be4bb51aa9ce6f |
On the Reproducibility of Neural Network Predictions | 1 INTRODUCTION . Deep neural networks ( DNNs ) have seen remarkable success in a range of complex tasks , and significant effort has been spent on further improving their predictive accuracy . However , an equally important desideratum of any machine learning system is stability or reproducibility in its predictions . ... | The paper proposes methods to address churn in deep neural networks for classification, defined as the extent of disagreements in predictions of two models trained on the same data with the same algorithm. In addition to an existing measure of churn that is based on exact match of predicted classes, the paper introduce... | SP:23746625f66c6cd7b2a4cc8e0e452d81a948d34b |
On the Reproducibility of Neural Network Predictions | 1 INTRODUCTION . Deep neural networks ( DNNs ) have seen remarkable success in a range of complex tasks , and significant effort has been spent on further improving their predictive accuracy . However , an equally important desideratum of any machine learning system is stability or reproducibility in its predictions . ... | The paper investigates two methods to reduce churn in neural network classification prediction. Churn is when two networks trained on the same data produce outputs that disagree, due to randomness in the training process. The authors identify several sources of randomness, from underlying hardware differences to parame... | SP:23746625f66c6cd7b2a4cc8e0e452d81a948d34b |
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples | 1 INTRODUCTION . While reinforcement learning ( RL ) has been shown to successfully solve problems with careful reward design ( Rajeswaran et al. , 2018 ; OpenAI et al. , 2019 ) , RL in its most general form , with no assumptions on the dynamics or reward function , requires solving a challenging uninformed search prob... | This paper considers the problem of learning a policy for an MDP with unspecified reward, given user-provided goal states. To this end, a reward model and a policy are jointly learned: the reward model is the conditional normalized maximum likelihood (CNML) learned from a training set consisting of the example goal sta... | SP:edf6b1f46c66ca835d3ab608b17a07bed0aeef36 |
Reinforcement Learning with Bayesian Classifiers: Efficient Skill Learning from Outcome Examples | 1 INTRODUCTION . While reinforcement learning ( RL ) has been shown to successfully solve problems with careful reward design ( Rajeswaran et al. , 2018 ; OpenAI et al. , 2019 ) , RL in its most general form , with no assumptions on the dynamics or reward function , requires solving a challenging uninformed search prob... | This paper studies how to solve RL problems with a set of success states instead of a standard reward function. The central idea is to firstly train a Bayesian classifier from both the input success examples and the on-policy sampling using the conditional normalized maximum likelihood (CNML) and then use the learned c... | SP:edf6b1f46c66ca835d3ab608b17a07bed0aeef36 |
Necessary and Sufficient Conditions for Compositional Representations | 1 INTRODUCTION . Humans recognize the world and create imaginations in a supple way by leveraging systematic compositionality to achieve compositional generalization , the algebraic capacity to understand and produce large amount of novel combinations from known components ( Chomsky , 1957 ; Montague , 1970 ) . This is... | The paper attempts to formally explore the necessary and sufficient conditions for compositional representations, leveraging the formal tools from group theory. While the ideas look potentially promising, the presentation is fundamentally flawed, with certain key notions left without formal definitions. As a consequenc... | SP:f4fc78afa84a20e4b7ceee32e9e1d2bf3bf0edb0 |
Necessary and Sufficient Conditions for Compositional Representations | 1 INTRODUCTION . Humans recognize the world and create imaginations in a supple way by leveraging systematic compositionality to achieve compositional generalization , the algebraic capacity to understand and produce large amount of novel combinations from known components ( Chomsky , 1957 ; Montague , 1970 ) . This is... | This paper applies concepts from group theory to help find necessary and sufficient representations on the presence of compositionality in representations and on mappings between them. This topic is very important, as people tend to use "compositional" in many ways, often not explicitly defined. The paper, however, i... | SP:f4fc78afa84a20e4b7ceee32e9e1d2bf3bf0edb0 |
A framework for learned CountSketch | 1 INTRODUCTION . In recent years , we have seen the influence of machine learning extend far beyond the field of artificial intelligence . The underlying paradigm , which assumes that a given algorithm has an input distribution for which algorithm parameters can be optimized , has even been applied to classical algorit... | The authors consider the problem of ``"sketching" – a popular compression technique in machine learning - used for reducing the size of the data enabling one to quickly compute an approximate solution using this compressed input. This paper introduces a general framework for learning and applying sparse sketching matri... | SP:3bec1083d30b42438c1d66b4f382b80d34032b7e |
A framework for learned CountSketch | 1 INTRODUCTION . In recent years , we have seen the influence of machine learning extend far beyond the field of artificial intelligence . The underlying paradigm , which assumes that a given algorithm has an input distribution for which algorithm parameters can be optimized , has even been applied to classical algorit... | The authors consider a specific sketching method, CountSketch, and three objective functions defined over the data design matrix: multiple-response regression (MMR), low-rank approximation (LRA), and k-means clustering. They compare the classical CountSketch with a random choices of the {-1,+1}-valued sketching matrix ... | SP:3bec1083d30b42438c1d66b4f382b80d34032b7e |
Taming GANs with Lookahead-Minmax | Generative Adversarial Networks are notoriously challenging to train . The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field . To tackle these challenges , we propose the Lookahead algorithm for minmax optimiz... | This work extends the recently proposed lookahead optimizer (which was designed for single-objective optimization) to minimax optimization, particularly GAN training. The authors claim that the backtracking step in lookahead optimizer alleviates the notorious rotational behavior in GAN dynamics. Moreover, the authors a... | SP:ebf3053dcae6ca7e0bdbc98e5a71151c55eb384f |
Taming GANs with Lookahead-Minmax | Generative Adversarial Networks are notoriously challenging to train . The underlying minmax optimization is highly susceptible to the variance of the stochastic gradient and the rotational component of the associated game vector field . To tackle these challenges , we propose the Lookahead algorithm for minmax optimiz... | This paper proposes a lookahead-minmax algorithm for optimizing minmax problems such as GANs, which updates the parameters (of both the generator and the discriminator) with the extrapolation. With a bilinear example, the authors show that the use of lookahead-minimax allows for convergence in cases where other methods... | SP:ebf3053dcae6ca7e0bdbc98e5a71151c55eb384f |
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms | 1 INTRODUCTION . Multi-armed bandit ( MAB ) is to maximize cumulative reward of a player throughout a bandit game by choosing different arms at each time step . It is also equivalent to minimizing the regret defined as the difference between the best rewards that can be achieved and the actual reward gained by the play... | This paper contributes to the study of EXP-based algorithms in two aspects. One is on the theoretical aspect: It analyzes the lower and upper bounds of EXP-3 for Gaussian multi-bandit setting for which the reward can be unbounded. The other is on the empirical aspect: It applied EXP4, originally developed for MAB, to R... | SP:6cb547cec2e67bdd5ae1b278d99fabdeac826ed3 |
Regret Bounds and Reinforcement Learning Exploration of EXP-based Algorithms | 1 INTRODUCTION . Multi-armed bandit ( MAB ) is to maximize cumulative reward of a player throughout a bandit game by choosing different arms at each time step . It is also equivalent to minimizing the regret defined as the difference between the best rewards that can be achieved and the actual reward gained by the play... | The authors consider analyzing the EXP3.P algorithm for the case of unbounded reward functions, in the sense that the rewards are governed by a Gaussian distribution. The authors first demonstrate a regret lower bound result on the Gaussian MABs when the time horizon is bounded from above. Then, the authors proceed to ... | SP:6cb547cec2e67bdd5ae1b278d99fabdeac826ed3 |
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks | The learning rate ( LR ) is one of the most important hyper-parameters in stochastic gradient descent ( SGD ) for deep neural networks ( DNN ) training and generalization . However , current hand-designed LR schedules need to manually pre-specify a fixed form , which limits their ability to adapt to non-convex optimiza... | The paper proposes to parameterize learning rate (LR) schedule with an explicit mapping formulation. This learnable structure allowed the proposed meta-trained MLR-SNet to achieve good LR schedules. For validation, the proposed method is evaluated on both image and text classification benchmark with various network arc... | SP:fe4742a7415e0bbb189d999c86edf90781733e46 |
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks | The learning rate ( LR ) is one of the most important hyper-parameters in stochastic gradient descent ( SGD ) for deep neural networks ( DNN ) training and generalization . However , current hand-designed LR schedules need to manually pre-specify a fixed form , which limits their ability to adapt to non-convex optimiza... | In this work, the authors use an LSTM to meta-learn learning rate schedules. This LSTM depends only on the validation loss at time t. They train this LSTM for some tasks and they show that it gives good performance when compared with baselines. Then they transfer one of these trained LSTMs to different tasks and gives ... | SP:fe4742a7415e0bbb189d999c86edf90781733e46 |
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity | 1 INTRODUCTION . In reinforcement learning ( RL ) , an agent interacts with a stochastic environment following a certain policy and receives some reward , and it aims to learn an optimal policy that yields the maximum accumulated reward Sutton & Barto ( 2018 ) . In particular , many RL algorithms have been developed to... | This paper combines a widely used variance reduction technique SVRG with the greedy-GQ. It provides a finite-time analysis of the proposed algorithm in the off-policy and Markovian sampling setting (convergence to the stationary point) and improves the sample complexity from the order $\epsilon^{-3}$ to $\epsilon^{-2}... | SP:3447c4f7cb227a9eba660c6c43fcbddb3d866567 |
Greedy-GQ with Variance Reduction: Finite-time Analysis and Improved Complexity | 1 INTRODUCTION . In reinforcement learning ( RL ) , an agent interacts with a stochastic environment following a certain policy and receives some reward , and it aims to learn an optimal policy that yields the maximum accumulated reward Sutton & Barto ( 2018 ) . In particular , many RL algorithms have been developed to... | Greedy-GQ is an RL algorithm for a control problem that extends on GTD, which is a prediction algorithm. While Greedy-GQ asymptotically converges to a stationary point, it does so with high sample complexity. The authors reduce the variance of Greedy-GQ by incorporating SVRG variance reduction scheme to both the time-s... | SP:3447c4f7cb227a9eba660c6c43fcbddb3d866567 |
Federated Mixture of Experts | 1 INTRODUCTION An ever-increasing amount of devices are being connected to the internet , sensing their environment , and generating vast amounts of data . The term federated learning ( FL ) has been established to describe the scenario where we aim to learn from the data generated by this “ federation ” of devices ( M... | The paper proposes a novel algorithm, which is a federated form on mixture of experts, called Federated Mixture of Experts (FedMix). In FedMix, an ensemble of specialized models is trained instead of a single global model. This strikes a compromise between training a single global model and one model per client. A gati... | SP:4113fc49197e2f7904f2ede5bda27d5d9dd6bc0e |
Federated Mixture of Experts | 1 INTRODUCTION An ever-increasing amount of devices are being connected to the internet , sensing their environment , and generating vast amounts of data . The term federated learning ( FL ) has been established to describe the scenario where we aim to learn from the data generated by this “ federation ” of devices ( M... | The paper proposes a method for federated learning of a mixture of experts model (FedMix). The approach allows training an ensemble of models each of which specializes to a subset of clients with similar data characteristics. The authors argue that this way of training an ensemble reduces the gradient divergence/interf... | SP:4113fc49197e2f7904f2ede5bda27d5d9dd6bc0e |
Learning Accurate Entropy Model with Global Reference for Image Compression | 1 INTRODUCTION . Image compression is a fundamental research topic in computer vision . The goal of image compression is to preserve the critical visual information of the image while reducing the bit-rate for storage or transmission . The state-of-the-art image compression standards , such as JPEG ( Wallace , 1992 ) ,... | This paper propose two methods for improve deep image compression performance: (i) Global Reference Module and (ii) Mean-shifting GDN Module (GSDN). (i) Global Reference Module searches over the decoded latents to find the relevant latents to the target latent for improve accuracy of entropy estimate. Authors extended ... | SP:21d5838371c68811775cd8bcf04b2374d427fe86 |
Learning Accurate Entropy Model with Global Reference for Image Compression | 1 INTRODUCTION . Image compression is a fundamental research topic in computer vision . The goal of image compression is to preserve the critical visual information of the image while reducing the bit-rate for storage or transmission . The state-of-the-art image compression standards , such as JPEG ( Wallace , 1992 ) ,... | The paper presents a learning-based approach for image compression. To reduce the compression rate, it describes two novel extensions, one to take the global context into account and an improved version of the commonly used GDN layer. Their advantage has been shown in a thorough ablation study. Overall, the method achi... | SP:21d5838371c68811775cd8bcf04b2374d427fe86 |
Language Models are Open Knowledge Graphs | 1 INTRODUCTION . Knowledge graphs ( KGs ) are an important resource for both humans and machines . Factual knowledge in KGs is injected into AI applications to imitate important skills possessed by humans , e.g. , reasoning and understanding . KG construction is mainly supervised , requiring humans to handwrite every f... | This paper presents an unsupervised approach for extracting OpenIE style triples from a corpus. The approach leverages the internal attention maps of pretrained transformers to identify paths which correspond to relations between a head entity and a tail entity. The extracted open triples are then mapped, wherever poss... | SP:6e57d34252a302c1f3ce4aaacf6fb59fdbbf12b8 |
Language Models are Open Knowledge Graphs | 1 INTRODUCTION . Knowledge graphs ( KGs ) are an important resource for both humans and machines . Factual knowledge in KGs is injected into AI applications to imitate important skills possessed by humans , e.g. , reasoning and understanding . KG construction is mainly supervised , requiring humans to handwrite every f... | Paper summary: The paper introduces an unsupervised method that utilizes an off-the-shelf BERT (without any fine-tuning) to create an information extraction system without any training data. It first creates ungrounded triples (a.k.a. OpenIE) from raw text by looking into the attention weights between words and finding... | SP:6e57d34252a302c1f3ce4aaacf6fb59fdbbf12b8 |
The Risks of Invariant Risk Minimization | 1 INTRODUCTION . Prediction algorithms are evaluated by their performance on unseen test data . In classical machine learning , it is common to assume that such data are drawn i.i.d . from the same distribution as the data set on which the learning algorithm was trained—in the real world , however , this is often not t... | * The work gives extended theoretical analysis on the effect of invariant risk minimization scheme, which is an increasingly popular framework for robust prediction. The work considerably extends the results in the original IRM paper. The results seem reasonable, and clarify implausible beliefs on the framework. The in... | SP:c4d8b135b7625ac0b52bdc4b9753c0db61b4d777 |
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