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Shape Defense | 1 INTRODUCTION . Deep neural networks ( LeCun et al. , 2015 ) remain the state of the art across many areas and are employed in a wide range of applications . They also provide the leading model of biological neural networks , especially in visual processing ( Kriegeskorte , 2015 ) . Despite the unprecedented success ,... | This paper aims to improve adversarial robustness considering the information about the object shape details with the means of edge maps. Two different strategies are proposed to increase model robustness using the edge maps: i) conduct the adversarial training on the input images, which are concatenated with its corre... | SP:aba0fd37465ee59982d617e32243307543cb0cb0 |
Shape Defense | 1 INTRODUCTION . Deep neural networks ( LeCun et al. , 2015 ) remain the state of the art across many areas and are employed in a wide range of applications . They also provide the leading model of biological neural networks , especially in visual processing ( Kriegeskorte , 2015 ) . Despite the unprecedented success ,... | This paper investigates incorporating shape information in deep neural networks to improve their adversarial robustness. It proposes two methods: the first one is to augment the input with the corresponding edge and then adversarially train a CNN on the augmented input. The second idea is to train a conditional GAN to ... | SP:aba0fd37465ee59982d617e32243307543cb0cb0 |
Shape Defense | 1 INTRODUCTION . Deep neural networks ( LeCun et al. , 2015 ) remain the state of the art across many areas and are employed in a wide range of applications . They also provide the leading model of biological neural networks , especially in visual processing ( Kriegeskorte , 2015 ) . Despite the unprecedented success ,... | This paper studies how to incorporate shape (particularly depth map) into CNN for more robust models. The study focuses on image classification. Specifically, this paper proposes two depth-map-based defense: 1) Edge-guided Adversarial Training (EAT), which use depth map as an additional input 2) GAN-based Shape Defense... | SP:aba0fd37465ee59982d617e32243307543cb0cb0 |
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space | 1 INTRODUCTION . Individualization proposes to leverage omni-channel data to meet individual needs . Individualized decision making plays a vital role in a wide variety of applications . Examples include customized pricing strategy in economics ( Qiang & Bayati , 2016 ; Turvey , 2017 ) , individualized treatment regime... | The main contribution of this paper is a new algorithm to learn the expected reward function for a given target policy using the historical data generated by a different behavior policy in continuous action domains. All current Offline-Policy Evaluation (OPE) methods for handling continuous action domains use a kernel... | SP:9b5ab25b377e76d0e9aa753c7f043952724b5451 |
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space | 1 INTRODUCTION . Individualization proposes to leverage omni-channel data to meet individual needs . Individualized decision making plays a vital role in a wide variety of applications . Examples include customized pricing strategy in economics ( Qiang & Bayati , 2016 ; Turvey , 2017 ) , individualized treatment regime... | This paper proposes a new method for offline evaluation when the action space is continuous, one dimensional. This overcomes the drawbacks of the kernel based method, which cannot be applied to non-smooth Q functions and requires heavy computation to optimize the bandwidth. The proposed method can be applied to discont... | SP:9b5ab25b377e76d0e9aa753c7f043952724b5451 |
Deep Jump Q-Evaluation for Offline Policy Evaluation in Continuous Action Space | 1 INTRODUCTION . Individualization proposes to leverage omni-channel data to meet individual needs . Individualized decision making plays a vital role in a wide variety of applications . Examples include customized pricing strategy in economics ( Qiang & Bayati , 2016 ; Turvey , 2017 ) , individualized treatment regime... | This paper considers the problem of off-policy evaluation with continuous actions. The main idea is to first using multi-scale change point detection to discretize the action space and then apply traditional IPW or DR methods to estimate the value. The DJQE method is theoretically analyzed under both the cases that the... | SP:9b5ab25b377e76d0e9aa753c7f043952724b5451 |
Achieving Explainability in a Visual Hard Attention Model through Content Prediction | 1 INTRODUCTION . Though deep convolution networks achieve state of the art performance on the image classification task , it is difficult to explain which input regions affected the output . A technique called visual hard attention provides this explanation by design . The hard attention model sequentially attends smal... | This paper proposed a new hard attention model for the image classification. They designed hard attention mechanism as a bayesian optimal experimental setting. Compare to other hard attention model, the policies of proposed hard attention can be explainable and differentiable, which is non-parametric. They evaluated th... | SP:131084bc72c0513e72f8514d48e27b0bf2cd66d1 |
Achieving Explainability in a Visual Hard Attention Model through Content Prediction | 1 INTRODUCTION . Though deep convolution networks achieve state of the art performance on the image classification task , it is difficult to explain which input regions affected the output . A technique called visual hard attention provides this explanation by design . The hard attention model sequentially attends smal... | This paper follows a less explored strategy for achieving explainability via hard attention. They proposed a recurrent architecture which sequentially observe regions (glimpse) from an image. To decide where to look next, the model maintains a hidden state and use it to estimate the full image (or features of the image... | SP:131084bc72c0513e72f8514d48e27b0bf2cd66d1 |
Achieving Explainability in a Visual Hard Attention Model through Content Prediction | 1 INTRODUCTION . Though deep convolution networks achieve state of the art performance on the image classification task , it is difficult to explain which input regions affected the output . A technique called visual hard attention provides this explanation by design . The hard attention model sequentially attends smal... | This paper presents a visual hard-attention image classification model. The difference to standard classification methods such as CNN is that the model provides an explainable inner structure by default, that can be inspected to see what the model focused on. The difference to other state-or-the-art hard-attention mode... | SP:131084bc72c0513e72f8514d48e27b0bf2cd66d1 |
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) ( Rumelhart et al. , 1986 ) are among the most frequently employed methods to build machine learning models on temporal data . Despite its ubiquitous application ( Baldi et al. , 1999 ; Graves & Schmidhuber , 2009 ; Graves , 2013 ; Graves et al. , 2013 ; Graves & Jait... | This paper studies approximation and optimization of linear RNNs for learning linear functions, from the perspective of the memory-properties of the temporal sequence. It shows that linear functionals can be approximated by a linear RNN, with the rate of approximation depending on the long-term memory of the process. ... | SP:2757a1c9fc4d7c81c497024bd6f3eec65027e352 |
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) ( Rumelhart et al. , 1986 ) are among the most frequently employed methods to build machine learning models on temporal data . Despite its ubiquitous application ( Baldi et al. , 1999 ; Graves & Schmidhuber , 2009 ; Graves , 2013 ; Graves et al. , 2013 ; Graves & Jait... | The paper provides a theoretical examination of the challenge of fitting recurrent neural networks (RNNs) to fit processes with long memory (or long-range dependence). Dubbed the “curse of memory”, the author(s) restrict to the case of linear activation functions, and show that for processes with increased spatial depe... | SP:2757a1c9fc4d7c81c497024bd6f3eec65027e352 |
On the Curse of Memory in Recurrent Neural Networks: Approximation and Optimization Analysis | 1 INTRODUCTION . Recurrent neural networks ( RNNs ) ( Rumelhart et al. , 1986 ) are among the most frequently employed methods to build machine learning models on temporal data . Despite its ubiquitous application ( Baldi et al. , 1999 ; Graves & Schmidhuber , 2009 ; Graves , 2013 ; Graves et al. , 2013 ; Graves & Jait... | This paper reports a mathematical study of approximation properties of linear RNNs. The first part reports a universal approximation theorem, and presents an analysis of how efficient the approximation is. In particular, it is shown that approximating a slowly decaying, power-law temporal filter requires a large numbe... | SP:2757a1c9fc4d7c81c497024bd6f3eec65027e352 |
Deep Graph Neural Networks with Shallow Subgraph Samplers | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have now become the state-of-the-art models for graph mining ( Wu et al. , 2020 ; Hamilton et al. , 2017b ; Zhang et al. , 2019 ) , facilitating applications such as social recommendation ( Monti et al. , 2017 ; Ying et al. , 2018 ; Pal et al. , 2020 ) , knowledge underst... | To address the oversmoothing problem and reduce the computational cost of GNNs, this paper proposes to train deep GNNs with shallow subgraph samplers. The following two theoretical proofs provide insightful motivations of Shadow-GNN: (1 )Obtaining node embeddings within shallow subgraphs can avoid oversmoothing; (2)Dee... | SP:add3ccfc58941a3bf72517666a38ca1d473b278d |
Deep Graph Neural Networks with Shallow Subgraph Samplers | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have now become the state-of-the-art models for graph mining ( Wu et al. , 2020 ; Hamilton et al. , 2017b ; Zhang et al. , 2019 ) , facilitating applications such as social recommendation ( Monti et al. , 2017 ; Ying et al. , 2018 ; Pal et al. , 2020 ) , knowledge underst... | This paper proposes a new extension of GNNs to deep GNNs, which use subgraphs to keep the computational costs low for training large graphs. It addresses the two main reasons that GNNs have not previously been extended to deep GNNs: expressivity and computational cost. Increasing the number of layers in a GNN leads to ... | SP:add3ccfc58941a3bf72517666a38ca1d473b278d |
Deep Graph Neural Networks with Shallow Subgraph Samplers | 1 INTRODUCTION . Graph Neural Networks ( GNNs ) have now become the state-of-the-art models for graph mining ( Wu et al. , 2020 ; Hamilton et al. , 2017b ; Zhang et al. , 2019 ) , facilitating applications such as social recommendation ( Monti et al. , 2017 ; Ying et al. , 2018 ; Pal et al. , 2020 ) , knowledge underst... | The paper proposes a simple but interesting new graph sampling method for graph neural networks, called “deep GNN, shallow sampler”. Centered on the target nodes, they only sample shallow subgraphs within $L_0$-hop neighborhood and then run an $L$-layer GNN ($L>L_0$) on these subgraphs and aggregate their embeddings. ... | SP:add3ccfc58941a3bf72517666a38ca1d473b278d |
Attention Based Joint Learning for Supervised Electrocardiogram Arrhythmia Differentiation with Unsupervised Abnormal Beat Segmentation | 1 INTRODUCTION . Arrhythmia in electrocardiogram ( ECG ) is a reflection of heart conduction abnormality and occurs randomly among normal beats . Deep learning based methods have demonstrated strong power in classifying different types of arrhythmia . There are plenty of works on classifying a single beat , involving c... | This paper proposes a deep neural network for Premature Ventricular Contraction (PVC) differentiation and segmentation from electrocardiogram (ECG) signals. The network is jointly trained as a segmenter and a classifier with a multitask learning manner. Differentiation is achieved by the classifier, and segmentation is... | SP:0bc3eb9022a39f1bf9770699468b667c3f09d4d3 |
Attention Based Joint Learning for Supervised Electrocardiogram Arrhythmia Differentiation with Unsupervised Abnormal Beat Segmentation | 1 INTRODUCTION . Arrhythmia in electrocardiogram ( ECG ) is a reflection of heart conduction abnormality and occurs randomly among normal beats . Deep learning based methods have demonstrated strong power in classifying different types of arrhythmia . There are plenty of works on classifying a single beat , involving c... | The paper proposes a framework for the classification of arrhythmias in electrocardiogram (ECG) data. The proposed approach performs segmentation and classification of the ECG signal. The segmenter performs segmentation of the signal (also called attention map) even though the term segmentation is not quite correct. Th... | SP:0bc3eb9022a39f1bf9770699468b667c3f09d4d3 |
Attention Based Joint Learning for Supervised Electrocardiogram Arrhythmia Differentiation with Unsupervised Abnormal Beat Segmentation | 1 INTRODUCTION . Arrhythmia in electrocardiogram ( ECG ) is a reflection of heart conduction abnormality and occurs randomly among normal beats . Deep learning based methods have demonstrated strong power in classifying different types of arrhythmia . There are plenty of works on classifying a single beat , involving c... | This manuscript contributes a neural architecture to classify arrhythmia type from ECG data. The signal treated as 1D, and the architecture performs joint segmentation-classification detecting the abnormal beats and then classifying them as a function of their origine. It uses U-nets for segmentation and, for classific... | SP:0bc3eb9022a39f1bf9770699468b667c3f09d4d3 |
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | 1 INTRODUCTION . Over the last five years , many Graph Neural Networks ( GNNs ) have been proposed in the literature of geometric deep learning ( Veličković et al. , 2018 ; Gilmer et al. , 2017 ; Bronstein et al. , 2017 ; Battaglia et al. , 2018 ) , in order to generalize the very efficient deep learning paradigm int... | This paper shows an equivalence framework between Graph Neural Networks (GNNs) defined in the spatial domain (based on local node neighbourhoods updates) and spectral domain (based on filters defined on eigenvalues of the graph Laplacian). Using this framework, the paper derives the spectral equivalent of common spati... | SP:fb7d909ac287383723943fb71bbbb5abb9dee7a1 |
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | 1 INTRODUCTION . Over the last five years , many Graph Neural Networks ( GNNs ) have been proposed in the literature of geometric deep learning ( Veličković et al. , 2018 ; Gilmer et al. , 2017 ; Bronstein et al. , 2017 ; Battaglia et al. , 2018 ) , in order to generalize the very efficient deep learning paradigm int... | This work studies the performance of different Graph Neural Network (GNN) from a spectral perspective. In particular, it shows the kernel for all kinds of proposed GNN models can be expressed in a general form with a specific frequency response definition, which indicates the spectral property (spectrum) of the kernel.... | SP:fb7d909ac287383723943fb71bbbb5abb9dee7a1 |
Analyzing the Expressive Power of Graph Neural Networks in a Spectral Perspective | 1 INTRODUCTION . Over the last five years , many Graph Neural Networks ( GNNs ) have been proposed in the literature of geometric deep learning ( Veličković et al. , 2018 ; Gilmer et al. , 2017 ; Bronstein et al. , 2017 ; Battaglia et al. , 2018 ) , in order to generalize the very efficient deep learning paradigm int... | In this paper, the authors propose a spectral-based analysis method to analyze the modeling abilities of major GNNs. Specifically, the first use the concept of convolution support to unite the ideas of spatial-based methods and spectral-based methods. By further identifying the frequency profile of different models, th... | SP:fb7d909ac287383723943fb71bbbb5abb9dee7a1 |
Towards Understanding the Cause of Error in Few-Shot Learning | 1 INTRODUCTION . Learning novel concepts from few samples is one of the most important ability in human cognition system ( Chen et al . ( 2018 ) ; Dhillon et al . ( 2019 ) ; Wang et al . ( 2020 ) ) . By contrast , massive achievements of modern artificial intelligent systems are dependent upon lots of data and annotati... | This paper seeks to understand theoretically the current bottleneck in few-shot learning and address it with a new way of training the embedding. The authors find that the key issue in few-shot learning is not the separability of the novel classes but the discrepancy between classifiers trained on large datasets and fe... | SP:68adb3cef88d96379ca9e717817a25d6ba545ef9 |
Towards Understanding the Cause of Error in Few-Shot Learning | 1 INTRODUCTION . Learning novel concepts from few samples is one of the most important ability in human cognition system ( Chen et al . ( 2018 ) ; Dhillon et al . ( 2019 ) ; Wang et al . ( 2020 ) ) . By contrast , massive achievements of modern artificial intelligent systems are dependent upon lots of data and annotati... | This paper analyzes the upper bound of error rate on novel classes in few-shot learning theoretically. It derives that the upper bound is decided by feature separability and classifier discrepancy and shows that classification error is mainly caused by classifier discrepancy in few-shot scenarios. In addition, this pap... | SP:68adb3cef88d96379ca9e717817a25d6ba545ef9 |
Towards Understanding the Cause of Error in Few-Shot Learning | 1 INTRODUCTION . Learning novel concepts from few samples is one of the most important ability in human cognition system ( Chen et al . ( 2018 ) ; Dhillon et al . ( 2019 ) ; Wang et al . ( 2020 ) ) . By contrast , massive achievements of modern artificial intelligent systems are dependent upon lots of data and annotati... | This paper aims to understand the cause of error in few-shot classification. The authors are particularly interested in the upper-bound of the error rate, which they break down into linear separability in the feature space of the meta-train classes and classifier discrepancy on the meta-train classes, among other terms... | SP:68adb3cef88d96379ca9e717817a25d6ba545ef9 |
Learning Spatiotemporal Features via Video and Text Pair Discrimination | 1 INTRODUCTION . Deep learning has made a remarkable progress for visual recognition in both image and video domain ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Carreira & Zisserman , 2017 ; Feichtenhofer et al. , 2018 ) by training powerful neural networks on large-scale manually annotated datasets ( e.g. , ImageNe... | The paper proposes an approach to learn a video feature backbone in an unsupervised manner through the use of video titles (text modality) associated with user generated content from Youtube or Instagram. The key idea is to use a contrastive loss that increases the similarity score between a positive pair vs. a negativ... | SP:c33e067199692acdfb5377694f8b4945415d0321 |
Learning Spatiotemporal Features via Video and Text Pair Discrimination | 1 INTRODUCTION . Deep learning has made a remarkable progress for visual recognition in both image and video domain ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Carreira & Zisserman , 2017 ; Feichtenhofer et al. , 2018 ) by training powerful neural networks on large-scale manually annotated datasets ( e.g. , ImageNe... | This paper concerns the problem of learning video representation from paired video-text pairs. The proposed framework is weakly-supervised as the text associated with videos comes from user-provided YouTube titles or Instagram captions. The proposed method uses standard visual encoder and textual encoder and similarity... | SP:c33e067199692acdfb5377694f8b4945415d0321 |
Learning Spatiotemporal Features via Video and Text Pair Discrimination | 1 INTRODUCTION . Deep learning has made a remarkable progress for visual recognition in both image and video domain ( Krizhevsky et al. , 2012 ; He et al. , 2016 ; Carreira & Zisserman , 2017 ; Feichtenhofer et al. , 2018 ) by training powerful neural networks on large-scale manually annotated datasets ( e.g. , ImageNe... | The paper proposes a weakly supervised method for learning spatiotemporal features by video and text pair discrimination, namely cross-modal pair discrimination (CPD). This can be considered as an extension of (Wu et al. 2018) to video and text. On technical perspective, the original method Wu et al. is applied on imag... | SP:c33e067199692acdfb5377694f8b4945415d0321 |
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory | 1 INTRODUCTION . Bayesian Neural Networks ( BNNs ) offer a probabilistic interpretation of deep learning by inferring distributions over the model ’ s weights ( Neal , 1996 ) . With the potential of combining the scalability and performance of neural networks ( NNs ) with a framework for uncertainty quantification , BN... | One of the main issues that BNNs have to face is the choice of good informative priors in order to provide precise information about the uncertainty of predictions. The present work connects BNNs and PAC theory to formulate a new system to obtain a general-purpose approach for obtaining significative priors for Bayesia... | SP:90cfa7a84021909d38e23a7013a29f990f8f8f02 |
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory | 1 INTRODUCTION . Bayesian Neural Networks ( BNNs ) offer a probabilistic interpretation of deep learning by inferring distributions over the model ’ s weights ( Neal , 1996 ) . With the potential of combining the scalability and performance of neural networks ( NNs ) with a framework for uncertainty quantification , BN... | The paper proposes a method for learning BNN priors based on optimising a PAC-Bayes bound for meta-learning, which they call PACOH-NN. This extends previous work on optimising PAC-Bayes bounds (PACOH), and is attractive for being principled in construction and effective in practice. The main claims are that fewer tasks... | SP:90cfa7a84021909d38e23a7013a29f990f8f8f02 |
Meta-Learning Bayesian Neural Network Priors Based on PAC-Bayesian Theory | 1 INTRODUCTION . Bayesian Neural Networks ( BNNs ) offer a probabilistic interpretation of deep learning by inferring distributions over the model ’ s weights ( Neal , 1996 ) . With the potential of combining the scalability and performance of neural networks ( NNs ) with a framework for uncertainty quantification , BN... | The paper addresses the problem of learning data-driven priors for Bayesian neural networks. Assuming zero-centered Gaussian priors for BNNs often results in poor generalization and uncertainty quantification, whereas choosing informative priors is challenging due to limited interpretability of network weights. To be a... | SP:90cfa7a84021909d38e23a7013a29f990f8f8f02 |
Improving Tail Label Prediction for Extreme Multi-label Learning | Extreme multi-label learning ( XML ) works to annotate objects with relevant labels from an extremely large label set . Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels , while the performance is severely deteriorated for tail labels . However , it is often... | This paper considers the setting of extreme multi-label classification, where labels typically follow a power-law distribution with many infrequently-observed labels (so-called tail labels). In this setting it often happens that multi-label classifiers more often predict frequent labels as positive than infrequent labe... | SP:25a306a19b267dfcdcd927fa9e65f3d8f7487918 |
Improving Tail Label Prediction for Extreme Multi-label Learning | Extreme multi-label learning ( XML ) works to annotate objects with relevant labels from an extremely large label set . Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels , while the performance is severely deteriorated for tail labels . However , it is often... | In prediction problems with millions of labels also known as Extreme Multi-label Learning (XML) problems, e.g., recommender systems, the model predictions are not as good for the tail (rarer) labels. This paper proposes two models for this problem. The first model is re-ranking-based, that is, it reranks the prediction... | SP:25a306a19b267dfcdcd927fa9e65f3d8f7487918 |
Improving Tail Label Prediction for Extreme Multi-label Learning | Extreme multi-label learning ( XML ) works to annotate objects with relevant labels from an extremely large label set . Many previous methods treat labels uniformly such that the learned model tends to perform better on head labels , while the performance is severely deteriorated for tail labels . However , it is often... | The paper presents a method for improving tail-label performance in extreme multi-label learning setup where the number of target labels can be extremely large. It is based on the finding that the distribution of the norms of the learnt weight vectors also follows a power-law as does the distribution of the samples amo... | SP:25a306a19b267dfcdcd927fa9e65f3d8f7487918 |
Semantic Re-tuning with Contrastive Tension | 1 INTRODUCTION . Representation learning concerns the pursuit of automatically learning representations of data that are useful for future extraction of information ( Bengio et al. , 2013 ) . Recent work has predominantly been focused on training and extracting such representations from various deep neural architecture... | This paper proposes Contrastive Tension, a self-supervised method to improve sentence representations from pre-trained language models for Semantic Textual Similarity tasks. This work is motivated by the observation by previous work that the final layers of pre-trained model are often biased towards token-level pre-tra... | SP:b5dbddb2672f4567426094a6b52f84fcdce01d50 |
Semantic Re-tuning with Contrastive Tension | 1 INTRODUCTION . Representation learning concerns the pursuit of automatically learning representations of data that are useful for future extraction of information ( Bengio et al. , 2013 ) . Recent work has predominantly been focused on training and extracting such representations from various deep neural architecture... | The paper studies the problem of finding effective representation for semantic text similarity (STS). The paper first investigates the effectiveness of pre-trained masked language models (e.g. BERT) in the STS task. They found different layers of BERT have different performance when employed in STS task -- in particula... | SP:b5dbddb2672f4567426094a6b52f84fcdce01d50 |
Semantic Re-tuning with Contrastive Tension | 1 INTRODUCTION . Representation learning concerns the pursuit of automatically learning representations of data that are useful for future extraction of information ( Bengio et al. , 2013 ) . Recent work has predominantly been focused on training and extracting such representations from various deep neural architecture... | The paper investigates a new training objective, contrastive tension (CT), for obtaining unsupervised sentence embeddings. The objective operates by initializing two models with identical weights and then training the models to produce similar sentence embeddings to each other for identical sentences and dissimilar rep... | SP:b5dbddb2672f4567426094a6b52f84fcdce01d50 |
Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale | 1 INTRODUCTION . Generalization in over-parameterized neural networks trained using Stochastic Gradient Descent ( SGD ) is not well understood . Such networks typically have sufficient capacity to memorize their training set ( Zhang et al. , 2017 ) which naturally leads to the question : Among all the maps that are con... | The paper gives algorithms to test the Coherent Gradients Hypothesis (CHG) for larger models and larger datasets. CGH is a recently proposed hypothesis to explain generalization of neural networks using algorithmic stability and other empirical observations for deep learning. It claims that there are strong gradient di... | SP:02bc759eabcd069bc67c0f50daa6fe99f82779f0 |
Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale | 1 INTRODUCTION . Generalization in over-parameterized neural networks trained using Stochastic Gradient Descent ( SGD ) is not well understood . Such networks typically have sufficient capacity to memorize their training set ( Zhang et al. , 2017 ) which naturally leads to the question : Among all the maps that are con... | The paper aims at providing experimental evidence to support the Coherent Gradients Hypothesis (CGH), that was published previously. The hypothesis suggests that the ability of large neural networks to generalise comes from the aligned gradients of the examples in the dataset. Once SGD follows common gradients, ignorin... | SP:02bc759eabcd069bc67c0f50daa6fe99f82779f0 |
Weak and Strong Gradient Directions: Explaining Memorization, Generalization, and Hardness of Examples at Scale | 1 INTRODUCTION . Generalization in over-parameterized neural networks trained using Stochastic Gradient Descent ( SGD ) is not well understood . Such networks typically have sufficient capacity to memorize their training set ( Zhang et al. , 2017 ) which naturally leads to the question : Among all the maps that are con... | of paper:** Builds on and tests the "coherent gradients hypothesis" (CGH), which proposes that SGD is able to generalize well because of 'coherence' (similar direction) of gradients. The methodology of CGH involved comparing gradient directions on individual examples, infeasible for large datasets, so this work propos... | SP:02bc759eabcd069bc67c0f50daa6fe99f82779f0 |
Unsupervised Hierarchical Concept Learning | 1 INTRODUCTION . Consider a video ( Figure 1 ) that demonstrates how to cook an egg . Humans subconsciously learn concepts ( such as boiling water ) that describe different concepts ( or skills ) in such demonstrations Pammi et al . ( 2004 ) . These learned skills can be composed and reused in different ways to learn n... | This paper addresses the problem of extracting a hierarchy of concepts in an unsupervised way from demonstration data. The authors present a Transformer-based concept abstraction architecture called UNHCLE and show how it discovers meaningful hierarchies using datasets from Chess and Cooking domains. In particular, the... | SP:bfdb68759a70c3ced66ef2952a05cbcb0dd2aca7 |
Unsupervised Hierarchical Concept Learning | 1 INTRODUCTION . Consider a video ( Figure 1 ) that demonstrates how to cook an egg . Humans subconsciously learn concepts ( such as boiling water ) that describe different concepts ( or skills ) in such demonstrations Pammi et al . ( 2004 ) . These learned skills can be composed and reused in different ways to learn n... | This paper addresses a relatively new topic to learn the hierarchical concepts in videos and commentary in an unsupervised manner. The authors proposed a hierarchical and transversing encoder-decoder network architecture to tackle the problem, where the pre-trained ResNet32 and BERT are used as feature extractors, tran... | SP:bfdb68759a70c3ced66ef2952a05cbcb0dd2aca7 |
Unsupervised Hierarchical Concept Learning | 1 INTRODUCTION . Consider a video ( Figure 1 ) that demonstrates how to cook an egg . Humans subconsciously learn concepts ( such as boiling water ) that describe different concepts ( or skills ) in such demonstrations Pammi et al . ( 2004 ) . These learned skills can be composed and reused in different ways to learn n... | The paper introduces the solution of an important task: hierarchical concept learning(or temporal abstractions) from demonstration data. Specifically, this paper considers 1) unsupervised setting; 2) the hierarchy of concepts, and conducts experiments in two datasets. However, there are some points in the experiment se... | SP:bfdb68759a70c3ced66ef2952a05cbcb0dd2aca7 |
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study | 1 INTRODUCTION . Label smoothing ( Szegedy et al. , 2016 ) and knowledge distillation ( Hinton et al. , 2015 ) are two commonly recognized techniques in training deep neural networks and have been applied in many state-of-the-art models , such as language translation ( Vaswani et al. , 2017 ; Tan et al. , 2019 ; Zhou e... | The authors re-analyze and re-confirm the relationship between label-smoothing and knowledge distillation, which is firstly argued by Muller et al. (“When Does Label Smoothing Help?“, NerurIPS 2019.). This paper shows that the previous argument, "label smoothing is not helpful for knowledge distillation", does not alwa... | SP:8ed1e265fd31cf19ada7cd9e2e3cbde7eeaef578 |
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study | 1 INTRODUCTION . Label smoothing ( Szegedy et al. , 2016 ) and knowledge distillation ( Hinton et al. , 2015 ) are two commonly recognized techniques in training deep neural networks and have been applied in many state-of-the-art models , such as language translation ( Vaswani et al. , 2017 ; Tan et al. , 2019 ; Zhou e... | Recent literature proposed that even label smoothing improves the teacher model, it will hurt the distillation training of student models due to the information erasing. Although this idea dominated more and more literature, this paper argued that this observation is not entirely correct. In order to clarify this idea,... | SP:8ed1e265fd31cf19ada7cd9e2e3cbde7eeaef578 |
Is Label Smoothing Truly Incompatible with Knowledge Distillation: An Empirical Study | 1 INTRODUCTION . Label smoothing ( Szegedy et al. , 2016 ) and knowledge distillation ( Hinton et al. , 2015 ) are two commonly recognized techniques in training deep neural networks and have been applied in many state-of-the-art models , such as language translation ( Vaswani et al. , 2017 ; Tan et al. , 2019 ; Zhou e... | This paper is mainly based on the prior work by Muller et al., which suggests that label smoothing is incompatible with knowledge distillation. Firstly, this paper provides an explanation of this incompatibility---label smooth tends to erase relative information among different classes, and provide a way to qualitative... | SP:8ed1e265fd31cf19ada7cd9e2e3cbde7eeaef578 |
Self-Supervised Variational Auto-Encoders | Density estimation , compression , and data generation are crucial tasks in artificial intelligence . Variational Auto-Encoders ( VAEs ) constitute a single framework to achieve these goals . Here , we present a novel class of generative models , called self-supervised Variational Auto-Encoder ( selfVAE ) , that utiliz... | This paper focuses on the task of generating high-quality data with generative models. To be specific, the authors proposed a variant of variational autoencoder (VAE) model, named self-supervised VAE. The intuition behind this model is that by breaking down the complex generation task into simpler/smaller ones, complex... | SP:0b8ee1b00665d1bfec7342a1eefb0caf5521bbae |
Self-Supervised Variational Auto-Encoders | Density estimation , compression , and data generation are crucial tasks in artificial intelligence . Variational Auto-Encoders ( VAEs ) constitute a single framework to achieve these goals . Here , we present a novel class of generative models , called self-supervised Variational Auto-Encoder ( selfVAE ) , that utiliz... | $\bullet$ VAEs can ignore some dimensions of the latent code. Enforcing the posterior distributions to consider desired factors of variations in the input can be fulfilled by either making it more structured (i.e., quantization as in VQ-VAE-2) or introducing additional constraints. This paper tackles this problem by ap... | SP:0b8ee1b00665d1bfec7342a1eefb0caf5521bbae |
Self-Supervised Variational Auto-Encoders | Density estimation , compression , and data generation are crucial tasks in artificial intelligence . Variational Auto-Encoders ( VAEs ) constitute a single framework to achieve these goals . Here , we present a novel class of generative models , called self-supervised Variational Auto-Encoder ( selfVAE ) , that utiliz... | This paper targets richer and higher-quality generation with VAE. Two techniques are adopted to achieve the goal: 1). bijective model to enrich data generation with flexible prior. 2). presenting compressed variants of the input data, i.e. self -supervision as additional condition $y$, for reconstruction. The two techn... | SP:0b8ee1b00665d1bfec7342a1eefb0caf5521bbae |
On the Dynamics of Training Attention Models | 1 INTRODUCTION . Attention-based neural networks have been broadly adopted in many natural language models for machine translation ( Bahdanau et al. , 2014 ; Luong et al. , 2015 ) , sentiment classification ( Wang et al. , 2016 ) , image caption generation ( Xu et al. , 2015 ) , and the unsupervised representation lear... | This paper aims to prove and illustrate that attention components are defined during training by gradients that mutually amplify the embedding and score associated with crucial features. In particular, a word embedding with a high magnitude increases the gradient following the attention score for the same word, while a... | SP:58c220b21af74c85e23004a9bad82e4d0dd7333c |
On the Dynamics of Training Attention Models | 1 INTRODUCTION . Attention-based neural networks have been broadly adopted in many natural language models for machine translation ( Bahdanau et al. , 2014 ; Luong et al. , 2015 ) , sentiment classification ( Wang et al. , 2016 ) , image caption generation ( Xu et al. , 2015 ) , and the unsupervised representation lear... | The paper investigates the dynamics of attention mechanism by configurating a controlled experiment on a simple topic classification task and training via gradient descent. Each random sentence in the training data is synthesized to include only one topic word among many. Then the authors try to find an intrinsic mecha... | SP:58c220b21af74c85e23004a9bad82e4d0dd7333c |
On the Dynamics of Training Attention Models | 1 INTRODUCTION . Attention-based neural networks have been broadly adopted in many natural language models for machine translation ( Bahdanau et al. , 2014 ; Luong et al. , 2015 ) , sentiment classification ( Wang et al. , 2016 ) , image caption generation ( Xu et al. , 2015 ) , and the unsupervised representation lear... | This paper studies the dynamics of attention in a task of simplified topic modeling, over the course of training for a specific model, where the context vector is the sum over words in a sentence of their embedding weighted by the exponential of the dot-product their key embedding with a global query vector, normalized... | SP:58c220b21af74c85e23004a9bad82e4d0dd7333c |
Sample-Efficient Automated Deep Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) algorithms are often sensitive to the choice of internal hyperparameters ( Jaderberg et al. , 2017 ; Mahmood et al. , 2018 ) , and the hyperparameters of the neural network architecture ( Islam et al. , 2017 ; Henderson et al. , 2018 ) , hindering them from being appl... | Motivated by the sensitivity of RL algorithms to the choice of hyperparameters and the data efficiency issue in training RL agents, the authors propose a population-based automated RL framework which can be applied to any off-policy RL algorithms. In the framework, they optimise hyperparameters together with neural arc... | SP:d58e5f01c1c68e9c2fca423be935d790ef5346ee |
Sample-Efficient Automated Deep Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) algorithms are often sensitive to the choice of internal hyperparameters ( Jaderberg et al. , 2017 ; Mahmood et al. , 2018 ) , and the hyperparameters of the neural network architecture ( Islam et al. , 2017 ; Henderson et al. , 2018 ) , hindering them from being appl... | This paper propose a population-based AutoRL framework for hyperparameter optimization of off-policy RL algorithms. The framework optimizes both the hyperparameters and also the neural architecture in a one-shot manner, e.g., search and train at the same time. A shared experience replay buffer is used across the popula... | SP:d58e5f01c1c68e9c2fca423be935d790ef5346ee |
Sample-Efficient Automated Deep Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( RL ) algorithms are often sensitive to the choice of internal hyperparameters ( Jaderberg et al. , 2017 ; Mahmood et al. , 2018 ) , and the hyperparameters of the neural network architecture ( Islam et al. , 2017 ; Henderson et al. , 2018 ) , hindering them from being appl... | In this paper, the authors intend to propose an efficient automated reinforcement learning (RL) framework. To achieve this goal, they integrate three technologies, i.e., evolutionary RL for hyperparameter search, evolvable neural network for policy network design, and shared experience replay for improving data usage. ... | SP:d58e5f01c1c68e9c2fca423be935d790ef5346ee |
GenQu: A Hybrid System for Learning Classical Data in Quantum States | 1 INTRODUCTION . In the past decade , machine learning and artificial intelligence powered applications dramatically changed our daily life . Many novel algorithms and models achieve widespread practical successes in a variety of domains such as autonomous cars , healthcare , manufacturing , etc . Despite the wide adop... | The authors identify that “classical” learning is running into limitations due to power and scale of computing systems. The authors suggest “quantum” learning might supplant classical learning and solve these fundamental challenges. And, the authors suggest a method that is efficient in the number of QuBits, which can... | SP:783b8a3cf723a02ef4c26d1c254ad4e97efeafba |
GenQu: A Hybrid System for Learning Classical Data in Quantum States | 1 INTRODUCTION . In the past decade , machine learning and artificial intelligence powered applications dramatically changed our daily life . Many novel algorithms and models achieve widespread practical successes in a variety of domains such as autonomous cars , healthcare , manufacturing , etc . Despite the wide adop... | This paper presents GenQu, a hybrid and general-purpose quantum framework for learning classical data through quantum states. By encoding two dimensions of data per one qubit. they demonstrate the effectiveness of their framework via two classical classification tasks, where 1 and 2 qubits are used, respectively. This ... | SP:783b8a3cf723a02ef4c26d1c254ad4e97efeafba |
GenQu: A Hybrid System for Learning Classical Data in Quantum States | 1 INTRODUCTION . In the past decade , machine learning and artificial intelligence powered applications dramatically changed our daily life . Many novel algorithms and models achieve widespread practical successes in a variety of domains such as autonomous cars , healthcare , manufacturing , etc . Despite the wide adop... | The paper claims to introduce a new quantum machine learning framework called GenQu. However, the description of the framework very vague (using classical computers to optimize the parameters of a fixed quantum circuit), and hardly novel. In fact, the same basic ideas are so well-known in the community that they are de... | SP:783b8a3cf723a02ef4c26d1c254ad4e97efeafba |
Double Q-learning: New Analysis and Sharper Finite-time Bound | Double Q-learning ( Hasselt , 2010 ) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning . However , theoretical understanding of double Q-learning is rather limited and the only existing finite-time analysis was recently established in Xiong et al . ... | This paper provides a sharper analysis for the finite time convergence rate of the double Q learning algorithm. The authors provides bounds for the synchronous and asynchronous settings and uses a more refined learning rate of $a/(b+t)$. It is shown that with such step size rule, a sharper convergence rate than (Xiong ... | SP:5aefbc73adb97f8ad3aa65edab1b38e96b9e8f3b |
Double Q-learning: New Analysis and Sharper Finite-time Bound | Double Q-learning ( Hasselt , 2010 ) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning . However , theoretical understanding of double Q-learning is rather limited and the only existing finite-time analysis was recently established in Xiong et al . ... | This paper provides a new theoretical analysis of double Q-learning in the tabular case. The analysis improves over previous result of Xiong et al. which assumes polynomial learning rate. This paper considers rescaled linear learning rate and the sample complexity has better dependency on 1/eps. The improvement comes f... | SP:5aefbc73adb97f8ad3aa65edab1b38e96b9e8f3b |
Double Q-learning: New Analysis and Sharper Finite-time Bound | Double Q-learning ( Hasselt , 2010 ) has gained significant success in practice due to its effectiveness in overcoming the overestimation issue of Q-learning . However , theoretical understanding of double Q-learning is rather limited and the only existing finite-time analysis was recently established in Xiong et al . ... | This paper studies the convergence rate of double Q learning under the tabular setting. Both the synchronous and asynchronous double Q learning algorithms are studied and analyzed. The technical novelty of this paper seems limited -- possibly a direct combination of [Wainwright 2019a] and [Xiong et al 2020]. Moreover, ... | SP:5aefbc73adb97f8ad3aa65edab1b38e96b9e8f3b |
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP | 1 INTRODUCTION . Self-supervised learning in the form of pretrained language models ( LM ) has been the driving force in developing state-of-the-art natural language processing ( NLP ) systems in recent years . These methods typically follow two basic steps , where a supervised task-specific fine-tuning follows a large... | The paper presents a data augmentation framework for zero-shot cross-lingual transfer learning. The framework uses different types of data (labeled source data, unlabeled source data, automatically generated augmented data) for training a model for the target language. Experiments are conducted on three different task... | SP:937d3a7858616e03c7d95fed51082dff234198f4 |
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP | 1 INTRODUCTION . Self-supervised learning in the form of pretrained language models ( LM ) has been the driving force in developing state-of-the-art natural language processing ( NLP ) systems in recent years . These methods typically follow two basic steps , where a supervised task-specific fine-tuning follows a large... | The authors present an unsupervised data augmentation framework for cross-lingual NLP. Their method, called XLA, combines self-learning with co-learning and filtering. They generate additional synthetic examples by replacing words with predictions from pretrained multilingual masked LM (taken from XLM Conneau 2020). Th... | SP:937d3a7858616e03c7d95fed51082dff234198f4 |
XLA: A Robust Unsupervised Data Augmentation Framework for Cross-Lingual NLP | 1 INTRODUCTION . Self-supervised learning in the form of pretrained language models ( LM ) has been the driving force in developing state-of-the-art natural language processing ( NLP ) systems in recent years . These methods typically follow two basic steps , where a supervised task-specific fine-tuning follows a large... | This paper proposed a new data augmentation framework for low-resourse (and zero resource) cross-lingual task adaptation by combining several methods (entropy regluarized training, self-training). The authors conducted extensive experiments on three cross-lingual tasks, demonstrating the effectiveness of XLA. In additi... | SP:937d3a7858616e03c7d95fed51082dff234198f4 |
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks | 1 INTRODUCTION . Standard practice in machine learning has long been to only address carefully circumscribed , often very related tasks . For example , we might train a single classifier to label an image as containing objects from a certain predefined set , or to label the words of a sentence with their semantic roles... | This paper posits a very interesting question about provable multi-task learning by neural nets. The idea is quite interesting to encode the objectives as task codes and then to write a smooth approximation to the predictor function as a weighted sum of indicators and the try training a net to learn this smoothening. B... | SP:451837cd17de7cdc4a059f4cc0cedd10b2eb136d |
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks | 1 INTRODUCTION . Standard practice in machine learning has long been to only address carefully circumscribed , often very related tasks . For example , we might train a single classifier to label an image as containing objects from a certain predefined set , or to label the words of a sentence with their semantic roles... | This paper sets out to show that multiple tasks can be encoded in a neural network that that does not have explicit modular construction for each tasks, which is in contrast with the work of [Bakker and Heskes, JMLR 2003], and [Jocabs, Jordan, Nowlan and Hinton, Neural Computation 1991]. The premise of the paper is tha... | SP:451837cd17de7cdc4a059f4cc0cedd10b2eb136d |
One Network Fits All? Modular versus Monolithic Task Formulations in Neural Networks | 1 INTRODUCTION . Standard practice in machine learning has long been to only address carefully circumscribed , often very related tasks . For example , we might train a single classifier to label an image as containing objects from a certain predefined set , or to label the words of a sentence with their semantic roles... | This paper takes an interesting theoretical dive on the learnability of multiple tasks as one task, while the tasks are constructed with special structures of the cluster, decision tree, or simple program. This paper provides sample complexities analysis, showing that wide two-layer neural networks with standard activa... | SP:451837cd17de7cdc4a059f4cc0cedd10b2eb136d |
Increasing the Coverage and Balance of Robustness Benchmarks by Using Non-Overlapping Corruptions | 1 INTRODUCTION . Neural Networks perform poorly when they deal with images that are drawn from a different distribution than their training samples . Indeed , neural networks are sensitive to adversarial examples ( Szegedy et al. , 2014 ) , background changes ( Xiao et al. , 2020 ) , and common corruptions ( Hendrycks ... | This paper points out that ImageNet-C, the de facto standard for measuring robustness to natural corruptions for ImageNet classification models, contains correlated corruptions, so a mean robustness score over the ImageNet-C corruptions is biased in favor of certain classes of corruptions. It proposes two metrics: robu... | SP:c5862fb1ef5d251216f75033ad7fbad6fa446323 |
Increasing the Coverage and Balance of Robustness Benchmarks by Using Non-Overlapping Corruptions | 1 INTRODUCTION . Neural Networks perform poorly when they deal with images that are drawn from a different distribution than their training samples . Indeed , neural networks are sensitive to adversarial examples ( Szegedy et al. , 2014 ) , background changes ( Xiao et al. , 2020 ) , and common corruptions ( Hendrycks ... | The paper considers the problem of measuring the robustness of image classification models to common image perturbations. Datasets of corrupted images, such as ImageNet-C, have been created for this purpose. However, these datasets have been created from an ad-hoc, heuristic selection of perturbations. The present pape... | SP:c5862fb1ef5d251216f75033ad7fbad6fa446323 |
Increasing the Coverage and Balance of Robustness Benchmarks by Using Non-Overlapping Corruptions | 1 INTRODUCTION . Neural Networks perform poorly when they deal with images that are drawn from a different distribution than their training samples . Indeed , neural networks are sensitive to adversarial examples ( Szegedy et al. , 2014 ) , background changes ( Xiao et al. , 2020 ) , and common corruptions ( Hendrycks ... | This paper proposes a new dataset for estimating robustness to distribution shift, in particular corruption robustness. They accomplish this by proposing an alternative to ImageNet-C, ImageNet-NOC, which uses different corruptions. They consider corruptions not in ImageNet-C, and they argue that their dataset is superi... | SP:c5862fb1ef5d251216f75033ad7fbad6fa446323 |
Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection | 1 INTRODUCTION . GANs ( Goodfellow et al. , 2014 ) are an effective way to learn complex and high-dimensional distributions , leading to state-of-the-art models for image synthesis in both unconditional ( Karras et al. , 2019 ) and conditional settings ( Brock et al. , 2019 ) . However , it is well-known that a single ... | The paper proposes a method for an improvement of generative adversarial models via post-processing its latent variable distribution. To be more precise, the method proposes to train an additional neural network that outputs an important weight for each point of the latent space, thus reweighting the final distribution... | SP:42059920072ac2c09c17ad97e79303e5bee38534 |
Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection | 1 INTRODUCTION . GANs ( Goodfellow et al. , 2014 ) are an effective way to learn complex and high-dimensional distributions , leading to state-of-the-art models for image synthesis in both unconditional ( Karras et al. , 2019 ) and conditional settings ( Brock et al. , 2019 ) . However , it is well-known that a single ... | This work aims at improving the sample quality of generative models through better sampling, which is a relevant problem and has brought about a line of work [1,2,3,4,5], to name a few. By leveraging the idea of importance sampling, the authors train an additional network. The latter uses the information contained in t... | SP:42059920072ac2c09c17ad97e79303e5bee38534 |
Learning Disconnected Manifolds: Avoiding The No Gan's Land by Latent Rejection | 1 INTRODUCTION . GANs ( Goodfellow et al. , 2014 ) are an effective way to learn complex and high-dimensional distributions , leading to state-of-the-art models for image synthesis in both unconditional ( Karras et al. , 2019 ) and conditional settings ( Brock et al. , 2019 ) . However , it is well-known that a single ... | The paper proposes a new algorithm for improved sampling of GANs. Since GANs are continuous functions that act on a connected latent space, they will have trouble learning distributions whose support is disconnected (for e.g., clustered data). The proposed method tries to fix this issue and is motivated by rejected sam... | SP:42059920072ac2c09c17ad97e79303e5bee38534 |
Revisiting Explicit Regularization in Neural Networks for Reliable Predictive Probability | 1 INTRODUCTION . As deep learning models have become pervasive in real-world decision-systems , the importance of producing a reliable predictive probability is increasing . In this paper , we call predictive probability reliable if it is well-calibrated and precisely represents uncertainty about its predictions . The ... | There has been an ongoing debate about the role and importance of explicit and implicit regularization in deep learning. This paper attempts to address this issue by arguing that explicit regularization is required for the generalization of predictive probabilities, which may not be observed under the 0-1 loss. The pap... | SP:b2ed74153a3a56f3ad5511eeb3990e068d412192 |
Revisiting Explicit Regularization in Neural Networks for Reliable Predictive Probability | 1 INTRODUCTION . As deep learning models have become pervasive in real-world decision-systems , the importance of producing a reliable predictive probability is increasing . In this paper , we call predictive probability reliable if it is well-calibrated and precisely represents uncertainty about its predictions . The ... | The main contribution of this paper is to propose new regularization methods in deep neural networks that produce well-calibrated probability scores. The authors argue that regularization is better than post-processing, such as temperature scaling, because temperature scaling would require a separate dataset for calibr... | SP:b2ed74153a3a56f3ad5511eeb3990e068d412192 |
Revisiting Explicit Regularization in Neural Networks for Reliable Predictive Probability | 1 INTRODUCTION . As deep learning models have become pervasive in real-world decision-systems , the importance of producing a reliable predictive probability is increasing . In this paper , we call predictive probability reliable if it is well-calibrated and precisely represents uncertainty about its predictions . The ... | The paper studies how explicit regularization affects the reliable predictive probability (calibration) of classification tasks. An analysis of the log-likelihood is presented which motivates the use of explicit regularization. Next, two regularization terms are proposed to improve the predictive probability, and exper... | SP:b2ed74153a3a56f3ad5511eeb3990e068d412192 |
Sample weighting as an explanation for mode collapse in generative adversarial networks | 1 INTRODUCTION . Generative adversarial networks have come a long way since their introduction ( Goodfellow et al. , 2014 ) and are currently state of the art for some tasks , such as generating images . A combination of deep learning developments , GAN specific advances and vast improvements in data sets and computati... | This work proposes that many common issues with GAN methods are based on the weighting of the samples given to the generator’s objective function. They focus on a study of the original GAN objective proposed in Goodfellow et al. where the generator’s objective is the negative of the discriminators objective. The GAN co... | SP:ed625d9db2079f50e38537787c0e5c9e2ea79419 |
Sample weighting as an explanation for mode collapse in generative adversarial networks | 1 INTRODUCTION . Generative adversarial networks have come a long way since their introduction ( Goodfellow et al. , 2014 ) and are currently state of the art for some tasks , such as generating images . A combination of deep learning developments , GAN specific advances and vast improvements in data sets and computati... | This paper reexamines the original (MM) and the non-saturating (NS) GAN objective. The authors show that the gradients of the respective objectives just differ from a scaling factor depending on the discriminator's output for generated samples. While the scaling factor for the MM gradient is responsible for the well k... | SP:ed625d9db2079f50e38537787c0e5c9e2ea79419 |
Sample weighting as an explanation for mode collapse in generative adversarial networks | 1 INTRODUCTION . Generative adversarial networks have come a long way since their introduction ( Goodfellow et al. , 2014 ) and are currently state of the art for some tasks , such as generating images . A combination of deep learning developments , GAN specific advances and vast improvements in data sets and computati... | This paper proposes an explanation for mode collapse in the original GAN with the log -D objective for the generator (dubbed the non-saturating GAN or NS-GAN for short). The paper takes the approach of comparing the gradient of the generator objective for the original GAN with cross-entropy loss (dubbed the minimax GAN... | SP:ed625d9db2079f50e38537787c0e5c9e2ea79419 |
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit | 1 INTRODUCTION . Since Recurrent Neural Networks ( RNN ) have been introduced Williams et al . ( 1986 ) , they have become one of the reference methods to process sequences . A typical architecture is the Long-ShortTerm-Memory neural network ( LSTM ) which allowed improvement in natural language processing such as larg... | This submission presents an extension of SkipRNN, Skip-Window, that splits input sequences into windows of length L from which only K samples can be used. This guarantees that the computational budget is never exceeded. Skip-Window implemented this inductive bias by predicting L updating probabilities in parallel at th... | SP:099cb12ac8ffe1e09ba4ff99a263194e7372c137 |
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit | 1 INTRODUCTION . Since Recurrent Neural Networks ( RNN ) have been introduced Williams et al . ( 1986 ) , they have become one of the reference methods to process sequences . A typical architecture is the Long-ShortTerm-Memory neural network ( LSTM ) which allowed improvement in natural language processing such as larg... | The paper proposes Skip-Window or SkipW an abstraction encapsulating RNN cells to actively skip updates similar to some earlier works like Skip-RNN, Skim-RNN, and ThrRNN. The novelty of the method comes is in having control over the total updates to control the overall computational budget compared to previous methods ... | SP:099cb12ac8ffe1e09ba4ff99a263194e7372c137 |
SkipW: Resource Adaptable RNN with Strict Upper Computational Limit | 1 INTRODUCTION . Since Recurrent Neural Networks ( RNN ) have been introduced Williams et al . ( 1986 ) , they have become one of the reference methods to process sequences . A typical architecture is the Long-ShortTerm-Memory neural network ( LSTM ) which allowed improvement in natural language processing such as larg... | This work introduces Skip-Window (SkipW), an approach that allows RNNS to have improved computational efficiency at the cost of accuracy. SkipW adds a procedure to existing RNN cells that allows them to process fewer inputs while remaining in a strict computational budget. This work demonstrates the benefits of SkipW t... | SP:099cb12ac8ffe1e09ba4ff99a263194e7372c137 |
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection | 1 INTRODUCTION . Anomaly detection ( AD ) is the identification task of the rarely happened events or items that differ from the majority of the data . In the real world , there are many applications , such as the medial diagnosis ( Baur et al. , 2018 ; Zimmerer et al. , 2019a ) , defect detection in the factories ( Ma... | This paper presents a method for contrastive anomaly detection (AD) using an iterative masked conditional autoencoder inpainting approach. An autoencoder network is trained using an adversarial approach to reconstruct a randomly masked part of the input image. At test time a mask is derived from the generated anomaly m... | SP:3f2ca182ccafb5084013ee07613aaaa3bbbee930 |
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection | 1 INTRODUCTION . Anomaly detection ( AD ) is the identification task of the rarely happened events or items that differ from the majority of the data . In the real world , there are many applications , such as the medial diagnosis ( Baur et al. , 2018 ; Zimmerer et al. , 2019a ) , defect detection in the factories ( Ma... | This work proposed a novel learning strategy for unsupervisedly anomaly detection. Particularly, authors propose to use an iterative mask generation process based on image impainting and reduction of a structural similarity metric (SSMI) between the input image and its reconstructed version. For evaluation purposes, au... | SP:3f2ca182ccafb5084013ee07613aaaa3bbbee930 |
Iterative Image Inpainting with Structural Similarity Mask for Anomaly Detection | 1 INTRODUCTION . Anomaly detection ( AD ) is the identification task of the rarely happened events or items that differ from the majority of the data . In the real world , there are many applications , such as the medial diagnosis ( Baur et al. , 2018 ; Zimmerer et al. , 2019a ) , defect detection in the factories ( Ma... | This paper presents an impainting-based method for anomaly localization on images. In the training time, a conditional GAN-based generative modeling approach is adopted. In the test time, a mask matrix is adaptively estimated by thresholding the structural similarity index measure (SSIM) between the original images an... | SP:3f2ca182ccafb5084013ee07613aaaa3bbbee930 |
Robust Reinforcement Learning using Adversarial Populations | 1 INTRODUCTION . Developing controllers that work effectively across a wide range of potential deployment environments is one of the core challenges in engineering . The complexity of the physical world means that the models used to design controllers are often inaccurate . Optimization based control design approaches ... | This paper proposes to improve robustness in reinforcement learning via a population of diverse adversaries, where previous works mainly focus on the use a single adversary to mitigate the problem that the trained policy could be highly exploitable by the adversary. Specifically, at each iteration, it randomly selects ... | SP:bd8c89f5faf1695ca9f25e7e112cdb795db83864 |
Robust Reinforcement Learning using Adversarial Populations | 1 INTRODUCTION . Developing controllers that work effectively across a wide range of potential deployment environments is one of the core challenges in engineering . The complexity of the physical world means that the models used to design controllers are often inaccurate . Optimization based control design approaches ... | The authors present a scheme that can be used to train agents to be robust against a population of adversarial policies, in which adversaries can perturb actions via an additive perturbation. Motivated by the observation that agents trained against a single policy may overfit to that policy and hence will lack robustn... | SP:bd8c89f5faf1695ca9f25e7e112cdb795db83864 |
Robust Reinforcement Learning using Adversarial Populations | 1 INTRODUCTION . Developing controllers that work effectively across a wide range of potential deployment environments is one of the core challenges in engineering . The complexity of the physical world means that the models used to design controllers are often inaccurate . Optimization based control design approaches ... | This paper proposes an algorithm to improve the robustness of reinforcement learning. The algorithm , RAP, combines ideas from domain randomization and adversarial training. Specifically, during learning, it trains an ensemble of adversary to attack the learner, with the hope that the learner can be robust to various s... | SP:bd8c89f5faf1695ca9f25e7e112cdb795db83864 |
Meta-Learning with Implicit Processes | 1 INTRODUCTION . Few-shot learning ( also known as meta-learning ) is a defining characteristic of human intelligence . Its goal is to leverage the experiences from previous tasks to form a model ( represented by metaparameters ) that can rapidly adapt to a new task using only a limited quantity of its training data . ... | This paper proposes an efficient meta-learning approach using implicit processes. Specifically, authors represent each task as a continuous latent vector and use expectation-maximization algorithm to perform meta-learning. The E step performs task adaption using stochastic gradient Hamiltonian Monte Carlo sampling meth... | SP:3a95eb3f0187add9fb6cc59398f744250daf1434 |
Meta-Learning with Implicit Processes | 1 INTRODUCTION . Few-shot learning ( also known as meta-learning ) is a defining characteristic of human intelligence . Its goal is to leverage the experiences from previous tasks to form a model ( represented by metaparameters ) that can rapidly adapt to a new task using only a limited quantity of its training data . ... | The paper proposes a meta-learning method based on implicit process (IP) framework in which each task is represented by a latent vector. The IP setup for meta-learning seems identical to that of Neural processes [1]. In that, the key challenge for adaptation to a task based on a context/support set $(X_c, Y_c)$ is infe... | SP:3a95eb3f0187add9fb6cc59398f744250daf1434 |
Meta-Learning with Implicit Processes | 1 INTRODUCTION . Few-shot learning ( also known as meta-learning ) is a defining characteristic of human intelligence . Its goal is to leverage the experiences from previous tasks to form a model ( represented by metaparameters ) that can rapidly adapt to a new task using only a limited quantity of its training data . ... | This paper proposes Implicit Process Meta-Learning (IPML) where each task is represented as a continuous latent vector $\mathbf{z}$, and corresponding data points are described as function values evaluated at an implicit process conditioned on the task latent vector $\mathbf{z}$. To conduct the intractable inference, a... | SP:3a95eb3f0187add9fb6cc59398f744250daf1434 |
Momentum Contrastive Autoencoder: Using Contrastive Learning for Latent Space Distribution Matching in WAE | 1 INTRODUCTION . The main goal of generative modeling is to learn a good approximation of the underlying data distribution from finite data samples , while facilitating an efficient way to draw samples . Popular algorithms such as variational autoencoders ( VAE , Kingma & Welling ( 2013 ) ; Rezende et al . ( 2014 ) ) a... | In this paper, the authors propose to use contrastive learning for matching in latent space in the Wasserstein autoencoder (WAE). In addition, they employ techniques such as momentum contrast in contrastive learning. Experimental results show that the proposed method, MoCA, is more stable and converges faster than exis... | SP:98c84435bfea0ef2beb3b63b51a0a464b5ec620a |
That Escalated Quickly: Compounding Complexity by Editing Levels at the Frontier of Agent Capabilities | 1 INTRODUCTION . Reinforcement Learning ( RL , Sutton & Barto ( 1998 ) ) considers the problem of an agent learning from experience in an environment to maximize total ( discounted ) of reward . The past decade has seen a surge of interest in RL , with high profile successes in games ( Vinyals et al. , 2019 ; Berner et... | This paper introduces Adversarially Compounding Complexity by Editing Levels (ACCEL). ACCEL is an Unsupervised Environment Design (UED) algorithm, a method of generating a curriculum of environments so as to train agents that generalize well to either a training distribution of environments or off-distribution environm... | SP:8197f4e8e9cb5c37a297dca06bf3955da0cdcc93 |
Evaluating the Robustness of Time Series Anomaly and Intrusion Detection Methods against Adversarial Attacks | Time series anomaly and intrusion detection are extensively studied in statistics , economics , and computer science . Over the years , numerous methods have been proposed for time series anomaly and intrusion detection using deep learningbased methods . Many of these methods demonstrate state-of-the-art performance on... | The paper tackles the problem of adversarial attacks against time-series-based ML applications devoted to intrusion detection. The paper is relatively simple: they use existing adversarial ML strategies (white-box attacks) to thwart a similar ML system. The main contribution is the fact that few efforts investigated ad... | SP:4964917854c4f203cd0a464df3ab989d448b4236 |
Stochastic Projective Splitting: Solving Saddle-Point Problems with Multiple Regularizers | We present a new , stochastic variant of the projective splitting ( PS ) family of algorithms for monotone inclusion problems . It can solve min-max and noncooperative game formulations arising in applications such as robust ML without the convergence issues associated with gradient descent-ascent , the current de fact... | The paper focuses on the stochastic variant of the projective splitting (PS) algorithm. With a specific focus on monotone inclusion problems, the authors propose a novel separable algorithm featured by the ability to handle multiple constraints and non-smooth regularizers. Compared with similar approaches on variationa... | SP:e417981b6a5065733cf298169044570548654483 |
Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification | 1 INTRODUCTION . One of the most challenging problems for Time Series Classification ( TSC ) tasks is how to tell models in what time scales 1 to extract features . Time series ( TS ) data is a series of data points ordered by time or other meaningful sequences such as frequency . Due to the variety of information sour... | The paper is on the receptive field of CNNs for 1D time series classification. It proposes an elegant decomposition of receptive fields based on the Goldbach conjecture that any number can be represented by a sum of primes. The paper thus puts RFs with prime numbers in CNN layers, and by having multiple layers, these ... | SP:cd1397f08a6712e350b8a41ac9a6682e6fab4baf |
Adversarial Distributions Against Out-of-Distribution Detectors | Out-of-distribution ( OOD ) detection is the task of determining whether an input lies outside the training data distribution . As an outlier may deviate from the training distribution in unexpected ways , an ideal OOD detector should be able to detect all types of outliers . However , current evaluation protocols test... | The paper proposes a novel evaluation framework for out-of-distribution (OOD) detection under worst-case scenarios. While existing benchmarks use real samples from datasets outside the training distribution, the authors propose instead to learn an adversarial outlier distribution against OOD detectors using an autoenco... | SP:f12be73fab934b3d9c1917d05faad062d64d05e7 |
FROB: Few-shot ROBust Model for Classification with Out-of-Distribution Detection | 1 INTRODUCTION . In real-world settings , it is crucial to robustly perform classification and OoD detection with high levels of confidence . The problem of detecting whether a sample is in-distribution , from the training distribution , or OoD is critical for adversarial attacks . This is crucial nowadays in many appl... | The paper addresses an important issue of Out-of-Distribution detection in a few-shot setting. The authors propose to generate negative samples in an adversarial way to increase the OoD performance. Additionally they augment the loss function with additional term for OoD samples. They perform experiments of 2 benchmark... | SP:11561980998d0ff0b9a327c512fa1c918173d476 |
Learning Synthetic Environments and Reward Networks for Reinforcement Learning | We introduce Synthetic Environments ( SEs ) and Reward Networks ( RNs ) , represented by neural networks , as proxy environment models for training Reinforcement Learning ( RL ) agents . We show that an agent , after being trained exclusively on the SE , is able to solve the corresponding real environment . While an SE... | This paper aims to learn proxy environments (synthetic environments or SEs) and reward functions (reward networks or RNs), parameterized as neural networks, such that these proxy models provide beneficial transitions to make it more sample-efficient to learn a policy for a fixed target environment (referred to as the r... | SP:67e9394a528b943091462ecae83f3202d40bce57 |
Do What Nature Did To Us: Evolving Plastic Recurrent Neural Networks For Generalized Tasks | While artificial neural networks ( ANNs ) have been widely adopted in machine learning , the gaps between ANNs and biological neural networks ( BNNs ) are receiving increasing concern . In this paper , we propose a framework named as Evolutionary Plastic Recurrent Neural Networks ( EPRNN ) . Inspired by BNN , EPRNN com... | In this work, the authors use evolutionary strategies to train recurrent neural networks with Hebbian plasticity rules. They test the system on two tasks, sequence prediction and a simple RL tasks that involve robot navigation. The approach is compared against previous work that uses plasticity but without recurrent co... | SP:b09a4981cd0ca1f72068fb57104f3142c81bb92d |
Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis | 1 INTRODUCTION . The standard reinforcement learning ( RL ) setup consists of an agent interacting with an environment ( Sutton & Barto , 2018 ) . At each step of the interaction , the agent determines an action based on its policy and its current state , gets a reward , and makes a transition to the next state . An ai... | -The authors prove that a discrete approximation of a class of optimal control problems can be recasted as an RL MDP. -The authors prove that the original NAF formulation of the Q-function cannot approximately solve the MDP defined above, and hence propose a new quadratic formulation of the Q-value. They apply their ne... | SP:767e7f52dbf4778ed9a304c587178b1781ce5a42 |
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