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A Generalized Weighted Optimization Method for Computational Learning and Inversion | 1 INTRODUCTION . Given N data pairs { xj , yj } Nj=1 , where xj ∈ R , yj ∈ C , j = 1 , . . . , N , we are interested in learning a random Fourier feature ( RFF ) model ( Rahimi & Recht , 2008 ; Liao et al. , 2020 ; Xie et al. , 2020 ) fθ ( x ) = P−1∑ k=0 θke ikx , x ∈ [ 0 , 2π ] , ( 1 ) where P ∈ N is a given positive ... | The paper follows and extends the work by Belkin, Hsu, and Xu (2020) and Liang and Rakhlin (2018) which studies the bias-variance trade-off of the regression/interpolation problem in the under/over-parametrization regions. In particular, this paper follows the random Fourier model setting in Xie et al. (2020) and an... | SP:35355126f30b88391404bdea921a944e9e9da117 |
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning | Federated learning ( FL ) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data . However , recent studies have shown that FL is vulnerable to availability poisoning attacks , integrity backdoor attacks and inference ... | Federated learning (FL) has been shown to be vulnerable to weight poisoning attacks. An attacker who controls malicious clients can poison the clients’ model weights such that a backdoor to perform availability poisoning attacks, integrity backdoor attacks and inference attacks. In this work, the authors proposed a new... | SP:77cbeaffd1cf539e8793dcf0e95f5bb9186cf973 |
FedDiscrete: A Secure Federated Learning Algorithm Against Weight Poisoning | Federated learning ( FL ) is a privacy-aware collaborative learning paradigm that allows multiple parties to jointly train a machine learning model without sharing their private data . However , recent studies have shown that FL is vulnerable to availability poisoning attacks , integrity backdoor attacks and inference ... | This paper proposes a secure federated learning framework against weight poisoning. The key component in the framework is the discretization mechanism which works well with a sufficient number of clients. Theoretical analysis of the robustness and convergence is provided. Lastly, numerical analysis is provided to verif... | SP:77cbeaffd1cf539e8793dcf0e95f5bb9186cf973 |
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers | 1 INTRODUCTION Thanks to many architectural and algorithmic innovations , the recent decade has witnessed the unprecedented success of deep learning in various high-profile challenges , e.g. , the ImageNet recognition task ( Krizhevsky et al. , 2012 ) , the challenging board game of Go ( Silver et al. , 2017 ) and huma... | This paper studied the problem of DNN training and generalization in vanilla architecture (without BN and Skip Connections in ResNets). It follows NTK theory and the approach of applying certain transformations to the activation functions. This work improves an existing work DKS, and solves its incompatibility to ReLU ... | SP:a8e70c04bc1fdbb5fa33e164c705074b8221a12f |
Deep Learning without Shortcuts: Shaping the Kernel with Tailored Rectifiers | 1 INTRODUCTION Thanks to many architectural and algorithmic innovations , the recent decade has witnessed the unprecedented success of deep learning in various high-profile challenges , e.g. , the ImageNet recognition task ( Krizhevsky et al. , 2012 ) , the challenging board game of Go ( Silver et al. , 2017 ) and huma... | This paper mainly discusses the training of neural networks without residual connections. To close the gap between residual-free and regular models, an activation transformation technique named "Tailored Activation Transformation (TAT)" is introduced. Compared to the state-of-art method DKS, the proposed TAT can yiel... | SP:a8e70c04bc1fdbb5fa33e164c705074b8221a12f |
Self-Distribution Distillation: Efficient Uncertainty Estimation | 1 INTRODUCTION . Neural networks ( NNs ) have enjoyed much success in recent years achieving state-of-the-art performance on a large number of tasks within domains such as natural language processing ( Vaswani et al. , 2017 ) , speech recognition ( Hinton et al. , 2012 ) and computer vision ( Krizhevsky et al. , 2012 )... | The paper proposes an ensemble distillation approach, in which one network is used as feature extractor and "two heads" (two networks representing teacher and student) are added to the network for self-distillation. The multiple teacher predictions can be generated through by adding multiplicative Gaussian noise. The d... | SP:7819ce9440a684398e2958e908a55022ac70b890 |
Self-Distribution Distillation: Efficient Uncertainty Estimation | 1 INTRODUCTION . Neural networks ( NNs ) have enjoyed much success in recent years achieving state-of-the-art performance on a large number of tasks within domains such as natural language processing ( Vaswani et al. , 2017 ) , speech recognition ( Hinton et al. , 2012 ) and computer vision ( Krizhevsky et al. , 2012 )... | This paper contributes to neural network classifier training and uncertainty prediction. It proposes a self-distribution distillation method that can train a single model to estimate uncertainties in an integrated training phase. Also, it is flexible to be extended to build ensembles of models in the training phase a... | SP:7819ce9440a684398e2958e908a55022ac70b890 |
Robust Cross-Modal Semi-supervised Few Shot Learning | 1 INTRODUCTION . Despite the impressive success of deep learning models , frequently it requires massive amount of training data to fully demonstrate the potential of the model . In contrast , human is capable of learning new concepts given limited data . Consequently , few-shot learning gathers extensive research inte... | The paper presents a cross-modal semi-supervised few-shot learning approach for image classification. The idea is to train variational auto-encoder (VAE) with both image and text data for learning a feature representation. Then features are extracted from a test sample and assigned to the class of the closest prototype... | SP:9de6cee2ba08db0f6086702122bb484aa48532f9 |
Robust Cross-Modal Semi-supervised Few Shot Learning | 1 INTRODUCTION . Despite the impressive success of deep learning models , frequently it requires massive amount of training data to fully demonstrate the potential of the model . In contrast , human is capable of learning new concepts given limited data . Consequently , few-shot learning gathers extensive research inte... | This paper focuses on semi-supervised few-shot learning with multi-modality data. The authors introduce an uncertainty prior of an infinite Gaussian model, integrating multi-modality information from image and text data into a heterogenous variational autoencoder. Meanwhile, a new variational lower bound is derived for... | SP:9de6cee2ba08db0f6086702122bb484aa48532f9 |
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning | Video understanding relies on perceiving the overall global content and modeling its internal connections ( e.g. , causality , movement , and spatio-temporal correspondence ) . To learn these interactions , we apply a mask-then-predict pre-training task on the discretized video tokens generated via VQ-VAE . Unlike lang... | This paper proposes a new video pretraining method by combining masked token prediction and contrastive learning. Off-she-shelf VQ-VAE is used in this paper for discrete video tokens generation. In order to make the masked token prediction more effective, the authors proposed a block mask strategy where spatial-tempora... | SP:133e984aa4736226e82246f3937b0a08cd8beb68 |
VIMPAC: Video Pre-Training via Masked Token Prediction and Contrastive Learning | Video understanding relies on perceiving the overall global content and modeling its internal connections ( e.g. , causality , movement , and spatio-temporal correspondence ) . To learn these interactions , we apply a mask-then-predict pre-training task on the discretized video tokens generated via VQ-VAE . Unlike lang... | The paper proposes a few new techniques: 1) A new video modeling architecture that uses a pre-trained VQ-VAE to tokenize frames, followed by a transformer encoder that aggregates the features and produces the final action class label. 2) Pre-training such an architecture using self-supervision by a) Masked prediction: ... | SP:133e984aa4736226e82246f3937b0a08cd8beb68 |
Neural Temporal Logic Programming | 1 INTRODUCTION . Complex time series data is present across many data modalities such as sensors , records , audio , and video data . Typically there are composite events of interest in these time series which are composed of other atomic events in a certain order ( Liu et al. , 1999 ; Chakravarthy et al. , 1994 ; Hinz... | The paper proposes an end-to-end differentiable strategy (called neural TLP) to learn unknown temporal relations between atomic events (like after(miss, swing), “miss occurs after swing” in the baseball example), subsequently used to predict composite events (like strike). The strategy consists of a cascade of a smooth... | SP:2babd7819655158cde03167c37188ddf6a9147b2 |
Neural Temporal Logic Programming | 1 INTRODUCTION . Complex time series data is present across many data modalities such as sensors , records , audio , and video data . Typically there are composite events of interest in these time series which are composed of other atomic events in a certain order ( Liu et al. , 1999 ; Chakravarthy et al. , 1994 ; Hinz... | This paper presents an approach for learning temporal rules from data. The idea is to extract atomic events, and their temporal relationships between them. Subsequently, composite events are learned using composite event labels for supervision. The whole approach is formulated as an optimisation problem, after which st... | SP:2babd7819655158cde03167c37188ddf6a9147b2 |
Online Coreset Selection for Rehearsal-based Continual Learning | 1 INTRODUCTION . Humans possess the ability to learn a large number of tasks by accumulating knowledge and skills over time . Building a system resembling human learning abilities is a deep-rooted desire since sustainable learning over a long-term period is essential for general artificial intelligence . In light of th... | This paper proposes an Online Coreset Selection method to select the most representative and informative coreset at each iteration and trains them. The proposed method maximizes the model’s adaptation to a target dataset while selecting high-affinity samples to past tasks, which directly inhibits catastrophic forgettin... | SP:34c39b5ae4a943556b4acb7ee4a899c8703f2f21 |
Online Coreset Selection for Rehearsal-based Continual Learning | 1 INTRODUCTION . Humans possess the ability to learn a large number of tasks by accumulating knowledge and skills over time . Building a system resembling human learning abilities is a deep-rooted desire since sustainable learning over a long-term period is essential for general artificial intelligence . In light of th... | The author propose a novel approach for online coreset selection, i.e exemplars used in the rehearsal process of past tasks in a continual learning framework. The proposed method is based on the observation that not all the samples in a dataset are equally valuable, but their quality affects model's effectiveness and e... | SP:34c39b5ae4a943556b4acb7ee4a899c8703f2f21 |
C5T5: Controllable Generation of Organic Molecules with Transformers | 1 INTRODUCTION . Organic molecules are used in countless applications across human society : as medicines , industrial chemicals , fuels , pesticides , plastics , television screens , solar cells , and many others . Traditionally , new molecules are designed for particular tasks by hand , but the space of all possible ... | This paper proposes a method C5T5, a self-supervised pre-training method based on the T5 pre-trained model, which is able to make zero-shot select-and-replace edits to satisfy specific property values. The specific difference of this paper is the IUPAC names (a standardized molecular representation), and the method is ... | SP:49192c4c658b9b74837f6fe36d67289ec82d3e6c |
C5T5: Controllable Generation of Organic Molecules with Transformers | 1 INTRODUCTION . Organic molecules are used in countless applications across human society : as medicines , industrial chemicals , fuels , pesticides , plastics , television screens , solar cells , and many others . Traditionally , new molecules are designed for particular tasks by hand , but the space of all possible ... | **Summary** This paper proposed a way to modify the molecule based on language pretraining techniques. The sequence representation of molecules is based on IUPAC names, which can be more semantically meaningful and much easier to model than the SMILES or graph based molecule representation. The pretraining is done via... | SP:49192c4c658b9b74837f6fe36d67289ec82d3e6c |
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations | 1 INTRODUCTION . Advances in graph neural networks ( GNNs ) have revolutionized molecular representation learning for ( bio ) chemical applications such as high-fidelity property prediction ( Huang et al. , 2021 ; Chuang et al. , 2020 ) , accelerated conformer generation ( Ganea et al. , 2021 ; Mansimov et al. , 2019 ;... | The paper focuses on improving the capacity of GNN models, using chirality identification as the case study. As an important character to represent the geometry in molecules, chirality has a fundamental impact on the molecule properties and downstream applications. It is a major extension of [1]. The distinguishing fea... | SP:a4a47d63cd3c5c0b5c4f7e735f3a4f84528f5d5d |
Learning 3D Representations of Molecular Chirality with Invariance to Bond Rotations | 1 INTRODUCTION . Advances in graph neural networks ( GNNs ) have revolutionized molecular representation learning for ( bio ) chemical applications such as high-fidelity property prediction ( Huang et al. , 2021 ; Chuang et al. , 2020 ) , accelerated conformer generation ( Ganea et al. , 2021 ; Mansimov et al. , 2019 ;... | This paper presents a novel SE(3)-invariant GNN model for predicting 3D geometry-dependent physicochemical properties of molecules. In particular, this method focuses on an important issue of how to handle the chirality of molecules in molecular GNNs. When a molecule takes a 3D shape, we have a degree of freedom to rot... | SP:a4a47d63cd3c5c0b5c4f7e735f3a4f84528f5d5d |
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind | 1 INTRODUCTION . Cooperation is a key component of human society , which enables people to divide labor and achieve common goals that could not be accomplished independently . In particular , humans are able to form an ad-hoc team with partners and communicate cooperatively with one another ( Tomasello , 2014 ) . Cogni... | The paper presents a new method for communication and cooperation in multi-agent settings. The method relies on modelling other agents' intentions and internal states using Theory of Mind based neural nets. The predictions from the ToM model are used to decide how to communicate and coordinate with other agents. The au... | SP:c1bfd8893b1f9e54afbe95410eae68f08eed1f9d |
ToM2C: Target-oriented Multi-agent Communication and Cooperation with Theory of Mind | 1 INTRODUCTION . Cooperation is a key component of human society , which enables people to divide labor and achieve common goals that could not be accomplished independently . In particular , humans are able to form an ad-hoc team with partners and communicate cooperatively with one another ( Tomasello , 2014 ) . Cogni... | This paper proposes a new algorithm called TOM2C to solve the multi-agent reinforcement learning problem. To achieve goals in the MARL problem, communication between agents is important. However, it is often challenging due to scalability and communication costs. To solve this problem, the authors adopt the Theory of M... | SP:c1bfd8893b1f9e54afbe95410eae68f08eed1f9d |
SANE: Specialization-Aware Neural Network Ensemble | 1 INTRODUCTION . Real-world data distribution could be complex in most cases and people usually approximate it by a composition of several simpler distributions ( Xie et al. , 2016 ; Yang et al. , 2016 ; Tsai et al. , 2020 ) . Intuitively , to fit the complex data distribution conveniently , we can divide the complex m... | This paper presents a novel method for ensemble learning. By introducing a anchor scheme and specialization loss , the base learner are forced to be specialized. The method is validated on both tabular and image datasets. | SP:31b86280f4e7863cfa6c70d55695d26e7b65a8bf |
SANE: Specialization-Aware Neural Network Ensemble | 1 INTRODUCTION . Real-world data distribution could be complex in most cases and people usually approximate it by a composition of several simpler distributions ( Xie et al. , 2016 ; Yang et al. , 2016 ; Tsai et al. , 2020 ) . Intuitively , to fit the complex data distribution conveniently , we can divide the complex m... | This paper presents a SANE model to improve the ensemble learning from the perspective of model specialization. In particular, it first gives the analysis on existing ensemble strategies, i.e., random and diversity-driven, via conducting experiments on synthetic data. By this way, it points out the weakness of these st... | SP:31b86280f4e7863cfa6c70d55695d26e7b65a8bf |
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions | 1 INTRODUCTION . The applications of convolutional neural networks ( CNNs ) have demonstrated proven success in various computer vision tasks ( Voulodimos et al. , 2018 ) . However , with modern CNN architectures being increasingly deeper and wider , over-parameterization has become one of the major challenges of deplo... | The proposed work focuses on kernel pruning. The core idea revolves around grouping filters using a similarity criterion and removing common convolutional kernels with the group. The proposed work explores optimal grouping schemes for filters and after pruning unwanted filters, the retained filters are restructured and... | SP:9fa6596a6fa0d1363852455b53f196fbfe956729 |
Revisit Kernel Pruning with Lottery Regulated Grouped Convolutions | 1 INTRODUCTION . The applications of convolutional neural networks ( CNNs ) have demonstrated proven success in various computer vision tasks ( Voulodimos et al. , 2018 ) . However , with modern CNN architectures being increasingly deeper and wider , over-parameterization has become one of the major challenges of deplo... | The authors present a new metric to determine the similarity between different grouped kernels and prune the unimportant $k\times k$ slices out of a 3D filter. They utilize the Lottery Ticket Hypothesis and propose a greedy search strategy to overcome the challenge of a huge search space. The experiment results show th... | SP:9fa6596a6fa0d1363852455b53f196fbfe956729 |
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure | 1 INTRODUCTION & MOTIVATION . The large number of trainable parameters in deep neural networks imposes computational constraints on the information that can be made available to optimization algorithms . Standard machine learning libraries ( Abadi et al. , 2015 ; Paszke et al. , 2019 ) mainly provide access to first-or... | This work proposes a curvature model VIVIT based on generalized Gauss-Newton (GGN) approximation for the training of neural networks with a convex loss function. The low-rank structure of VIVIT allows for efficient eigen-value decomposition, which also gives per-sample directional derivatives and curvatures. To further... | SP:c28b8fa3bb2124845a81b3a8e30c6790b2c10df9 |
ViViT: Curvature access through the generalized Gauss-Newton's low-rank structure | 1 INTRODUCTION & MOTIVATION . The large number of trainable parameters in deep neural networks imposes computational constraints on the information that can be made available to optimization algorithms . Standard machine learning libraries ( Abadi et al. , 2015 ; Paszke et al. , 2019 ) mainly provide access to first-or... | This paper highlights how the low-rank structure of the generalized Gauss-Newton (GGN) approximation of the Hessian can be used as a computationally efficient tool to study the loss landscape of deep neural networks. In particular, authors discuss methods to compute the full spectrum of the GGN and thus providing acces... | SP:c28b8fa3bb2124845a81b3a8e30c6790b2c10df9 |
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods | Heterogeneous treatment effect ( HTE ) estimation with continuous treatment is essential in multiple disciplines , such as the online marketplace and pharmaceutical industry . The existing machine learning ( ML ) methods , like forest-based modeling , either work only for discrete treatments or make partially linear or... | The paper proposes a generalized version of the causal forest for heterogeneous treatment effect estimation for continuous treatment by non-parametric modeling of the dose-response function. To provide non-parametric modeling, the proposed method makes use of kernel-based double/debiased estimators. Experiments of the ... | SP:3e5e45d6810536f4e73cc6a2ba1fbf26992b20d4 |
GCF: Generalized Causal Forest for Heterogeneous Treatment Effect Estimation Using Nonparametric Methods | Heterogeneous treatment effect ( HTE ) estimation with continuous treatment is essential in multiple disciplines , such as the online marketplace and pharmaceutical industry . The existing machine learning ( ML ) methods , like forest-based modeling , either work only for discrete treatments or make partially linear or... | The authors extend the generalized random forest by Athey et al. (2019) from partially linear models to nonparametric ones and combine it with a doubly robust estimation step. The paper contains a small scale simulation study and a small real data analysis. | SP:3e5e45d6810536f4e73cc6a2ba1fbf26992b20d4 |
Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient | 1 INTRODUCTION . Modern infrastructure systems , such as computer routing and electric power networks , are vulnerable to natural disasters and malicious attacks ( Schneider et al. , 2011 ) . Consider the scenario where the abnormality of one power supply station causes other power supply stations to overload , which c... | This paper studies how to improve network resilience by proposing a reinforcement learning-based framework named **ResiNet** and a new GNN architecture called **FireGNN**. The proposed framework is able to directly generalize to unseen graphs. The new GNN architecture applies the graph filtration process, which enhance... | SP:599f2f7249aba31390c85edfeda4e7dd63ec4915 |
Edge Rewiring Goes Neural: Boosting Network Resilience via Policy Gradient | 1 INTRODUCTION . Modern infrastructure systems , such as computer routing and electric power networks , are vulnerable to natural disasters and malicious attacks ( Schneider et al. , 2011 ) . Consider the scenario where the abnormality of one power supply station causes other power supply stations to overload , which c... | The paper proposes a neural approach that increases network resilience by edge wiring. The approach uses a combination of graph neural network (GNN) and policy gradient method to do so. The experiments use a few small networks and several non-neural baselines. | SP:599f2f7249aba31390c85edfeda4e7dd63ec4915 |
On the relation between statistical learning and perceptual distances | 1 INTRODUCTION . The relationship between the internal representations of supervised learning models and biological systems has previously been explored ( Cadieu et al. , 2014 ) , and this connection can be explained by both systems being optimized to perform the same object recognition task . A much less studied area ... | The work presented in this paper aims to analyze the relationships between the probability distribution of the data, perceptual distances, and unsupervised machine learning. Perceptual sensitivity is correlated with the probability of an image in its close neighborhood. The paper also explores the relation between di... | SP:c686503ec773532c596fdf2b90a6bcc4db9044d3 |
On the relation between statistical learning and perceptual distances | 1 INTRODUCTION . The relationship between the internal representations of supervised learning models and biological systems has previously been explored ( Cadieu et al. , 2014 ) , and this connection can be explained by both systems being optimized to perform the same object recognition task . A much less studied area ... | This paper presents a mainly theoretical explication of the relationship between natural image statistics and perceptual distances for small image distortions. The paper presents a number of observations linking distances in natural images, autoencoders, and perceptual similarity for humans. The paper finally explores ... | SP:c686503ec773532c596fdf2b90a6bcc4db9044d3 |
On the Optimal Memorization Power of ReLU Neural Networks | ( √ N ) parameters . Known VC-dimension upper bounds imply that memorizing N samples requires Ω ( √ N ) parameters , and hence our construction is optimal up to logarithmic factors . We also give a generalized construction for networks with depth bounded by 1 ≤ L ≤ √ N , for memorizing N samples using Õ ( N/L ) parame... | This paper studies memorization capacity of deep ReLU networks. For arbitrary $N$ data points in a ball of size $r$ satisfying minimum separation $\delta$, the authors show that there exists a ReLU network of constant width and depth $\tilde O(\sqrt{N})$ that perfectly memorizes the entire dataset (Theorem 3.1). This m... | SP:8be64c1f03d32e6be9088572692563996ab09713 |
On the Optimal Memorization Power of ReLU Neural Networks | ( √ N ) parameters . Known VC-dimension upper bounds imply that memorizing N samples requires Ω ( √ N ) parameters , and hence our construction is optimal up to logarithmic factors . We also give a generalized construction for networks with depth bounded by 1 ≤ L ≤ √ N , for memorizing N samples using Õ ( N/L ) parame... | The paper shows that, under a mild separability condition, the number of connections in a ReLU network required to memorize N data points is of order $\sqrt{N}$ (up to logarithmic factors). This result improves upon the recent work of Park et al. (2021), and matches the previous lower bound. One key proof-technical inn... | SP:8be64c1f03d32e6be9088572692563996ab09713 |
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network | Model-based reinforcement learning usually suffers from a high sample complexity in training the world model , especially for the environments with complex dynamics . To make the training for general physical environments more efficient , we introduce Hamiltonian canonical ordinary differential equations into the learn... | The paper proposes a variant of a Hamiltonian neural network for learning dynamics models for model-based RL. The proposed network architecture uses encoders/decoders to map the system state x = [q, p] from the observations s and vice versa. In the latent state, a standard HNN is used to compute dx/dt to increment the ... | SP:e653f89bc504607362dfa2ba413b0481674f08df |
Model-based Reinforcement Learning with a Hamiltonian Canonical ODE Network | Model-based reinforcement learning usually suffers from a high sample complexity in training the world model , especially for the environments with complex dynamics . To make the training for general physical environments more efficient , we introduce Hamiltonian canonical ordinary differential equations into the learn... | This paper proposes a model of neural ODE auto-encoder, NODA, which incorporates Hamiltonian mechanics to learn a world model. The paper shows theoretical results of transition errors and value errors. NODA is tested on a range of RL tasks. | SP:e653f89bc504607362dfa2ba413b0481674f08df |
Cognitively Inspired Learning of Incremental Drifting Concepts | Humans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences . In contrast , machine learning models perform poorly in a continual learning setting , where input data distribution changes over time . Inspired by the nervous system learnin... | In this paper, the authors propose ICLA, an approach to tackle the problem of incremental and continual learning. By keeping track of a model's "internal" representations of data, they identify and overcome the "drift" issue (as in continual learning). Particularly, they adopt an encoder-decoder style architecture to l... | SP:25274bb362e98c010d0caa4849ac064586e19a0a |
Cognitively Inspired Learning of Incremental Drifting Concepts | Humans continually expand their learned knowledge to new domains and learn new concepts without any interference with past learned experiences . In contrast , machine learning models perform poorly in a continual learning setting , where input data distribution changes over time . Inspired by the nervous system learnin... | This paper proposed an algorithm for learning concepts incrementally taking inspiration from Parallel Distributed Processing and Complementary Learning Systems. The core idea is to use Gaussian Mixture Models and update them incrementally plus exploit a generative model to perform pseudo-rehearsal. The paper presents a... | SP:25274bb362e98c010d0caa4849ac064586e19a0a |
AdaFocal: Calibration-aware Adaptive Focal Loss | 1 INTRODUCTION . Neural networks have found tremendous success in almost every field including computer vision , natural language processing , and speech recognition . Over time , these networks have grown complex and larger in size to achieve state-of-the-art performance and they continue to evolve further in that dir... | This paper studies the calibration of deep learning, which aims to make confidence store accurately describe predictions' correctness probabilities. The authors improve focal loss and propose a calibration-aware focal loss for better calibration. The proposed approach adaptively adjusts the coefficient of focal loss a... | SP:7a78356d71affb20e118d817bb2b0b5f34d8d075 |
AdaFocal: Calibration-aware Adaptive Focal Loss | 1 INTRODUCTION . Neural networks have found tremendous success in almost every field including computer vision , natural language processing , and speech recognition . Over time , these networks have grown complex and larger in size to achieve state-of-the-art performance and they continue to evolve further in that dir... | This paper considers the problem of model calibration. Existing works calibrates the model by post-hoc approaches or objective function tailored for calibration. The authors of the paper propose an adaptive version based on Focal loss, which regularizes the overconfidence of neural networks. They observe that although ... | SP:7a78356d71affb20e118d817bb2b0b5f34d8d075 |
RainNet: A Large-Scale Imagery Dataset for Spatial Precipitation Downscaling | Contemporary deep learning frameworks have been applied to solve meteorolog-1 ical problems ( e.g. , front detection , synthetic radar generation , precipitation now-2 casting , e.t.c . ) and have achieved highly promising results . Spatial precipitation3 downscaling is one of the most important meteorological problems... | This paper proposes a dataset called RainNet for studying precipitation downscaling. RainNet consists of ~62k pairs of low-res/high-res precipitation maps from various meteorological events over the east coast of the US over 17 years. This dataset was created from two existing products: NLDAS and the NCEP's Stage IV 4k... | SP:3fed87e29b3609f6d356dafce16be061a85471ab |
RainNet: A Large-Scale Imagery Dataset for Spatial Precipitation Downscaling | Contemporary deep learning frameworks have been applied to solve meteorolog-1 ical problems ( e.g. , front detection , synthetic radar generation , precipitation now-2 casting , e.t.c . ) and have achieved highly promising results . Spatial precipitation3 downscaling is one of the most important meteorological problems... | The paper provides a baseline method that generates a dataset of high+low res precipitation maps. The paper argues that precision of weather event predictions depend on the imagery resolution and hence having high res maps will lead to better weather prediction. The eventual dataset that is formed contains a variety o... | SP:3fed87e29b3609f6d356dafce16be061a85471ab |
What to expect of hardware metric predictors in NAS | 1 INTRODUCTION . Modern neural network architectures are designed not only considering their primary objective , such as accuracy . While existing architectures can be scaled down to work with the limited available memory and computational power of , e.g. , mobile phones , they are significantly outperformed by specifi... | In neural architecture search (NAS), performance predictors are an important tool, because predicting attributes such as accuracy can save costly measurements. Hardware metric predictors predict metrics such as latency in addition to accuracy. This paper gives an empirical study of 18 different performance predictors o... | SP:d5262e875cc88b3a33653ebdc21d1b418c81d26b |
What to expect of hardware metric predictors in NAS | 1 INTRODUCTION . Modern neural network architectures are designed not only considering their primary objective , such as accuracy . While existing architectures can be scaled down to work with the limited available memory and computational power of , e.g. , mobile phones , they are significantly outperformed by specifi... | This work provides a compressive analysis across multiple (18) different hardware performance predictors by - (1) collecting their performance under different amounts of training data and different input network structures and showing each prediction method’s advantageous/disadvantageous scenarios - (2) analyzing the ... | SP:d5262e875cc88b3a33653ebdc21d1b418c81d26b |
Attention: Self-Expression Is All You Need | 1 INTRODUCTION . Attention , i.e. , the ability to selectively focus on a subset of sensory observations , while ignoring other irrelevant information , is a central component of human perception . For example , only a few words in a sentence may be useful for predicting the next word , or only a small portion of an im... | This paper studies the correlation between the attention mechanism and many prior arts. Heuristically, this paper links the currently hot topic, attention mechanism, with many milestones works in the past decades before the deep learning era, including subspace learning, sparse coding, kernel regression, non-local mean... | SP:c95b7015438d26176ef74968cca54ea98fddab98 |
Attention: Self-Expression Is All You Need | 1 INTRODUCTION . Attention , i.e. , the ability to selectively focus on a subset of sensory observations , while ignoring other irrelevant information , is a central component of human perception . For example , only a few words in a sentence may be useful for predicting the next word , or only a small portion of an im... | This paper surveys several lines of prior work which has connection to the self-attention module in transformers. Specifically, the authors show that self-attention has the similar form with the kernel regression and non-local mean algorithm. They also demonstrate that locally linear embedding and self-expression algor... | SP:c95b7015438d26176ef74968cca54ea98fddab98 |
Multi-scale fusion self attention mechanism | 1 INTRODUCTION . Attention mechanism is a model widely used in natural language processing tasks . Attention determines where the model needs attention by constructing an attention matrix . With the in-depth study of attention model , Vaswani et al . ( 2017 ) proposed a more advanced self attention mechanism . The self... | The manuscript presents a multi-scale self-attention method for NLP tasks. The aim is to better extract phrase- and word-level features. The main contribution of the proposed method is to apply different kernel sizes for feature extraction and multi-scale attention fusion. Additionally, a mechanism called dynamic spars... | SP:ef4369d2452cc6bc680eff611350cbf6f20e2e3b |
Multi-scale fusion self attention mechanism | 1 INTRODUCTION . Attention mechanism is a model widely used in natural language processing tasks . Attention determines where the model needs attention by constructing an attention matrix . With the in-depth study of attention model , Vaswani et al . ( 2017 ) proposed a more advanced self attention mechanism . The self... | This work proposes a multi-scale fusion self-attention module to help extract phase-level at different scales. It utilizes convolution operations with kernels of different sizes to achieve this end. The authors conduct experiments on the relation extraction and GLUE tasks to demonstrate its effectiveness. | SP:ef4369d2452cc6bc680eff611350cbf6f20e2e3b |
Multi-Agent Constrained Policy Optimisation | 1 INTRODUCTION . In recent years , reinforcement learning ( RL ) techniques have achieved remarkable successes on a variety of complex tasks ( Silver et al. , 2016 ; 2017 ; Vinyals et al. , 2019 ) . Powered by deep neural networks , deep RL enables learning sophisticated behaviours . On the other hand , deploying neura... | This paper studies the safe RL in the multi-agent setting. Specifically, the author leverage the theories from constrained policy optimization and multi-agent trust region learning to propose two algorithms: MACPO and MAPPO-Lagrangian. From the theoretical side, the author shows that in the idea setting, the proposed a... | SP:9418d0fd67bfb4ff1a64c745cceeb0028ee7ecd0 |
Multi-Agent Constrained Policy Optimisation | 1 INTRODUCTION . In recent years , reinforcement learning ( RL ) techniques have achieved remarkable successes on a variety of complex tasks ( Silver et al. , 2016 ; 2017 ; Vinyals et al. , 2019 ) . Powered by deep neural networks , deep RL enables learning sophisticated behaviours . On the other hand , deploying neura... | This paper considers the multi-agent reinforcement learning (MARL) problem with safety constraints. The authors proposed two methods, Multi-Agent Constrained Policy Optimisation (MACPO) and MAPPO-Lagrangian, by leveraging the theories from both constrained policy optimization and multi-agent trust-region learning. Thei... | SP:9418d0fd67bfb4ff1a64c745cceeb0028ee7ecd0 |
Go with the Flow: the distribution of information processing in multi-path networks | 1 INTRODUCTION . The architectures of Convolutional Neural Network ( CNN ) classifiers are an important influencing factor regarding their predictive performance and computational efficiency , with many designs being proposed over the years ( Simonyan & Zisserman , 2015 ; He et al. , 2016 ; Szegedy et al. , 2015 ; Tan ... | This is an experimental paper that seeks out to investigate information processing in multi-path networks i.e. networks such as ResNet, EfficientNet, Inception-style. The goal was to investigate how different pathways process information in such networks in order to better understand learned representations to inform n... | SP:60133dcf473580ef37878b0d79fc044c30adefda |
Go with the Flow: the distribution of information processing in multi-path networks | 1 INTRODUCTION . The architectures of Convolutional Neural Network ( CNN ) classifiers are an important influencing factor regarding their predictive performance and computational efficiency , with many designs being proposed over the years ( Simonyan & Zisserman , 2015 ; He et al. , 2016 ; Szegedy et al. , 2015 ; Tan ... | This paper analyzes the distribution of information processing in multi-path networks (including skip-connection models such as ResNet and DenseNet). They apply logistic regression on the hidden layers, namely logistic regression probes, to track the progress of the intermediate solution quality, and they analyze in wh... | SP:60133dcf473580ef37878b0d79fc044c30adefda |
Generalized rectifier wavelet covariance models for texture synthesis | 1 INTRODUCTION . Textures ares spatially homogeneous images , consisting of similar patterns forming a coherent ensemble . In texture modeling , one of the standard approaches to synthesize textures relies on defining a maximum entropy model ( Jaynes , 1957 ) using a single observed image ( Raad et al. , 2018 ) . It co... | This work proposed a texture synthesis framework using the rectified wavelet coefficients. The paper claims that the proposed method cand achieve similar quality with the VGG feature based method (Gatys et al. 22015) and random filter based method (RF, Ustyuzhaninov et al 2017) and gets better quality than PS (Portilla... | SP:7b58625be5e935efe59d293b377ddf7abdd2c845 |
Generalized rectifier wavelet covariance models for texture synthesis | 1 INTRODUCTION . Textures ares spatially homogeneous images , consisting of similar patterns forming a coherent ensemble . In texture modeling , one of the standard approaches to synthesize textures relies on defining a maximum entropy model ( Jaynes , 1957 ) using a single observed image ( Raad et al. , 2018 ) . It co... | This paper presents a new image representation model based on wavelets and non-linear rectifiers that allows to synthesize complex geometric textures with a better visual quality than previous wavelet-based models. The main interest of the paper is the usage of the mathematical results from Mallat et al 2020 and Zhang... | SP:7b58625be5e935efe59d293b377ddf7abdd2c845 |
Crystal Diffusion Variational Autoencoder for Periodic Material Generation | Generating the periodic structure of stable materials is a long-standing challenge for the material design community . This task is difficult because stable materials only exist in a low-dimensional subspace of all possible periodic arrangements of atoms : 1 ) the coordinates must lie in the local energy minimum define... | This paper aims to address challenging stable crystal materials generation problems via diffusion variational autoencoder with graph representation learning. Several recent advances in generative models and GNN are combined together to develop the entire workflow from data distribution learning, prediction to sample ge... | SP:5633fe1fee1abdd2a61bdd7679a771ce611f0f4e |
Crystal Diffusion Variational Autoencoder for Periodic Material Generation | Generating the periodic structure of stable materials is a long-standing challenge for the material design community . This task is difficult because stable materials only exist in a low-dimensional subspace of all possible periodic arrangements of atoms : 1 ) the coordinates must lie in the local energy minimum define... | The paper proposed a new generative model for 3D periodic material molecular structure. They designed a special variational autoencoder, where the decoder is parameterized by the denoising score-matching framework. Experiments demonstrate the model can successfully generate valid and realistic material, and optimize de... | SP:5633fe1fee1abdd2a61bdd7679a771ce611f0f4e |
LPRules: Rule Induction in Knowledge Graphs Using Linear Programming | 1 INTRODUCTION . Knowledge graphs ( KG ) are used to represent a collection of known facts via labeled directed edges . Each node of the graph represents an entity , and a labeled directed edge from one node to another indicates that the pair of nodes satisfies a binary relation given by the edge label . A fact in the ... | The paper presents a method to obtain weights for a knowledge graph scoring model for link prediction. There are numerous good algorithms for mining rules from knowledge graphs (e.g. AnyBURL and AMIE). Less research has focused on the problem of creating scoring functions based on an (implicit) list of rules. This is w... | SP:5cf0b8f16bda52b90c78f312effd934f828e3bab |
LPRules: Rule Induction in Knowledge Graphs Using Linear Programming | 1 INTRODUCTION . Knowledge graphs ( KG ) are used to represent a collection of known facts via labeled directed edges . Each node of the graph represents an entity , and a labeled directed edge from one node to another indicates that the pair of nodes satisfies a binary relation given by the edge label . A fact in the ... | This paper proposed a simple linear programming model for learning logical rules for KG completion. The model selects candidate rules from KG with explicit constraints and then solves a linear programming problem. The authors conduct experiments on several public datasets and the model has better efficiency than baseli... | SP:5cf0b8f16bda52b90c78f312effd934f828e3bab |
Distributional Decision Transformer for Hindsight Information Matching | 1 INTRODUCTION . Reinforcement learning ( RL ) suffers from the problem of sample inefficiency , and a central question is how to extract as much learning signals , or constraint equations ( Pong et al. , 2018 ; Tu & Recht , 2019 ; Dean et al. , 2020 ) , from each trajectory data as possible . As dynamics transitions a... | The paper proposes an extension of Decision Transformer (DT) that can work with distributions of features. A number of prior hindsight-based or context-dependent methods are shown to be special cases of a generic scheme called Hindsight Information Matching (HIM), where arbitrary statistics of future trajectories can b... | SP:9318b2155b674b593441ffbb97b56c7ce57cb39a |
Distributional Decision Transformer for Hindsight Information Matching | 1 INTRODUCTION . Reinforcement learning ( RL ) suffers from the problem of sample inefficiency , and a central question is how to extract as much learning signals , or constraint equations ( Pong et al. , 2018 ; Tu & Recht , 2019 ; Dean et al. , 2020 ) , from each trajectory data as possible . As dynamics transitions a... | This work discusses many prior methods under a Hindsight Information Matching (HIM) framework, where the methods can be interpreted as trying to minimizing the KL divergence between the achieved statistic and some target statistic for a particular choice of statistic. The authors propose to replace the return-to-go co... | SP:9318b2155b674b593441ffbb97b56c7ce57cb39a |
When, Why, and Which Pretrained GANs Are Useful? | 1 INTRODUCTION . These days , generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) can successfully approximate the high-dimensional distributions of real images . The exceptional quality of the state-ofthe-art GANs ( Karras et al. , 2020b ; Brock et al. , 2019 ) makes them a key ingredient in applicat... | This paper aims to achieve a better understanding of GAN fine-tuning. A synthetic experiment demonstrates that pretrained discriminators improve the quality of the initial gradients, and pretrained generators help with improving mode coverage. Transfer learning experiments on real image datasets reveal that pretraining... | SP:23c0b97268ae96bfb6f18bbefe9d3f208ce8170f |
When, Why, and Which Pretrained GANs Are Useful? | 1 INTRODUCTION . These days , generative adversarial networks ( GANs ) ( Goodfellow et al. , 2014 ) can successfully approximate the high-dimensional distributions of real images . The exceptional quality of the state-ofthe-art GANs ( Karras et al. , 2020b ; Brock et al. , 2019 ) makes them a key ingredient in applicat... | This paper performs a large-scale study of transfer learning in GANs. It proposes a way to understand the relevance of a pre-trained generator and discriminator, as well as heuristics to select good source/initialization dataset and even a training snapshot. All of this is very valuable for the practitioners. | SP:23c0b97268ae96bfb6f18bbefe9d3f208ce8170f |
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING | 1 INTRODUCTION . In recent years the neural network-based methods have shown great success in various NLP-related tasks ( Zhao et al. , 2021 ) . Tow popular complementary natural language processing methods are word2vec ( Mikolov et al. , 2013a ; b ) and Latent Dirichlet Allocation ( LDA ) ( Blei et al. , 2003 ) ; but ... | This paper focuses on learning document embeddings, presenting a topic-document embedding (TDE) method employing syntactic and semantic properties by jointly learning topic and word embedding in a single framework. The proposed TDE approach follows corruption mechanism to create the global context and randomly select t... | SP:3e6d8b50675d42c92cc1c707868b029984319a4a |
JOINTLY LEARNING TOPIC SPECIFIC WORD AND DOCUMENT EMBEDDING | 1 INTRODUCTION . In recent years the neural network-based methods have shown great success in various NLP-related tasks ( Zhao et al. , 2021 ) . Tow popular complementary natural language processing methods are word2vec ( Mikolov et al. , 2013a ; b ) and Latent Dirichlet Allocation ( LDA ) ( Blei et al. , 2003 ) ; but ... | Jointly learning word embeddings and document embeddings while taking into account topical information is somewhat interesting. The paper presents an improved skip-gram model where each individual word is associated with two matrices (e.g., the input projection matrix and the output projection matrix) which is designed... | SP:3e6d8b50675d42c92cc1c707868b029984319a4a |
Learning an Object-Based Memory System | 1 INTRODUCTION . Consider a robot operating in a household , making observations of multiple objects as it moves around over the course of days or weeks . The objects may be moved by the inhabitants , even when the robot is not observing them , and we expect the robot to be able to find any of the objects when requeste... | The paper defines an entity-monitoring problem where the goal is to identify the distinct objects see in an episode where the agent/model moves through the scene/observes partial state. The paper proposes the OBM-Net model architecture to address this problem and identify all the distinct objects observed over each epi... | SP:3e46e1dd9730179c71a009b20355101423c348b1 |
Learning an Object-Based Memory System | 1 INTRODUCTION . Consider a robot operating in a household , making observations of multiple objects as it moves around over the course of days or weeks . The objects may be moved by the inhabitants , even when the robot is not observing them , and we expect the robot to be able to find any of the objects when requeste... | The paper proposes an end-to-end system for the data association and filtering (DAF) problem. The architecture is built to mimic components typically found in DAF systems to provide a sort of algorithmic prior to the network. The resulting system is evaluated on several different tasks using synthetic data. | SP:3e46e1dd9730179c71a009b20355101423c348b1 |
Real-Time Neural Voice Camouflage | 1 INTRODUCTION . Automatic speech recognition models are embedded in nearly all smart devices . Although these models have many exciting applications , the concern for the potential of these devices to eavesdrop is significant . It is becoming increasingly important to develop methods that give users the autonomy to sa... | This paper proposes a Neural Voice Camouflage (NVC) method that has three important characteristics, which are essential for an NVC method to be used in practical scenarios: general, real-time, and robust. Since the proposed method trains a model to learn predictive attacks without any constraints about input and outpu... | SP:7dace4ef94b6bd673112dda394ef5225f62df0b4 |
Real-Time Neural Voice Camouflage | 1 INTRODUCTION . Automatic speech recognition models are embedded in nearly all smart devices . Although these models have many exciting applications , the concern for the potential of these devices to eavesdrop is significant . It is becoming increasingly important to develop methods that give users the autonomy to sa... | This paper proposes a novel attack approach with a purpose of disrupting the automatic speech recognition system. The proposed method, called Neural Voice Camouflage, works in real time by forecasting attacks ahead of time when they are added to speech streams. The authors conducted experiments with the LibriSpeech dat... | SP:7dace4ef94b6bd673112dda394ef5225f62df0b4 |
Hierarchical Multimodal Variational Autoencoders | 1 INTRODUCTION . Data modalities represent different perspectives of the same concept . Generative models can learn from such data by reproducing it , which can be useful for tasks such as image caption generation ( Vinyals et al. , 2015 ) . This model family can also reproduce feature vector representations of the dat... | The authors propose a Hierarchical framework for multimodal learning HMVAE. They define modality specific latent factor as well as the shared latent factor across multiple modalities. They represent modality-specific variations using latent variables dependent on the shared top-level variable. They parameterize the pos... | SP:7e0bdc833324174b53617123d8268988c0263a34 |
Hierarchical Multimodal Variational Autoencoders | 1 INTRODUCTION . Data modalities represent different perspectives of the same concept . Generative models can learn from such data by reproducing it , which can be useful for tasks such as image caption generation ( Vinyals et al. , 2015 ) . This model family can also reproduce feature vector representations of the dat... | This paper proposes a new type of model called a hierarchical multimodal VAE (HMVAE) that captures modality-specific variations using latent variables dependent on a shared top-level variable, in a manner similar to a multi-layer hierarchy. Their assumption is that modality-specific variations can sometimes depend on t... | SP:7e0bdc833324174b53617123d8268988c0263a34 |
The Infinite Contextual Graph Markov Model | 1 INTRODUCTION . It can be argued that one of the most daunting processes in machine learning is the selection of appropriate hyper-parameters for the task at hand . Indeed , due to the data-dependent nature of the learning problem , there usually exists no single model configuration that works well in all contexts . T... | This paper is an extension of the Contextual Graph Markov Model, a deep unsupervised probabilistic approach for modeling graph data. The key idea is to leverage Hierarchical Dirichlet Processes, which enables the proposed approach to automatically choose the size of each layer’s latent representation. The authors condu... | SP:0d10eeb943cf56da483878662ceeb5c6ec09df2c |
The Infinite Contextual Graph Markov Model | 1 INTRODUCTION . It can be argued that one of the most daunting processes in machine learning is the selection of appropriate hyper-parameters for the task at hand . Indeed , due to the data-dependent nature of the learning problem , there usually exists no single model configuration that works well in all contexts . T... | In this paper, the authors propose a mechanism to automate the size selection of each latent representation layer of the Contextual Graph Markov model. The model automatically adjusts the size of the model parameters mitigating the expensive model selection. Moreover, the authors introduce some techniques to scale the ... | SP:0d10eeb943cf56da483878662ceeb5c6ec09df2c |
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers | Pre-training deep neural network models using large unlabelled datasets followed by finetuning them on small task-specific datasets has emerged as a dominant paradigm in natural language processing ( NLP ) and computer vision ( CV ) . Despite the widespread success , such a paradigm has remained atypical in reinforceme... | ### What is the Problem / Question? Pre-training has not been thoroughly explored within RL, so it is unknown how effectively task-free unlabeled datasets can be used for PT in an RL context. The authors present a simple strategy for such PT for RL that shows benefits with limited reward annotations. ### Why is it imp... | SP:1e8a3732ecec3487984655db61d6d56e3f4e0426 |
Semi-supervised Offline Reinforcement Learning with Pre-trained Decision Transformers | Pre-training deep neural network models using large unlabelled datasets followed by finetuning them on small task-specific datasets has emerged as a dominant paradigm in natural language processing ( NLP ) and computer vision ( CV ) . Despite the widespread success , such a paradigm has remained atypical in reinforceme... | This paper presents Pre-trained Decision Transformer (PDT) for semi-supervised offline reinforcement learning. PDT first pre-train a decision transformer model on the trajectory dataset without rewards, and then fine-tune on a smaller dataset with reward annotations. Empirically, PDT achieves compatible performance wit... | SP:1e8a3732ecec3487984655db61d6d56e3f4e0426 |
Connecting Data to Mechanisms with Meta Structual Causal Model | Recent years have seen impressive progress in theoretical and algorithmic developments of causal inference across various disciplines in science and engineering . However , there are still some unresolved theoretical problems , especially for cyclic causal relationships . In this article , we propose a meta structure c... | This paper proposes to use **active set** of causal edges, instead of auxiliary context variable or domain index to characterize nonstationary causal relations in data. In addition, the authors propose Sufficient Activated Mechanisms (SAM) to replace Sparse Mechanism Shift (SMS) as the inductive bias (assumption) for c... | SP:15ebeef5c445120900b9c94bbcd6a997bfb92899 |
Connecting Data to Mechanisms with Meta Structual Causal Model | Recent years have seen impressive progress in theoretical and algorithmic developments of causal inference across various disciplines in science and engineering . However , there are still some unresolved theoretical problems , especially for cyclic causal relationships . In this article , we propose a meta structure c... | This paper presents a new lens on causal graphical models from a lens of fuller generality. Rather than considering models under assumptions such as acyclicity or other constraints that enable tractable modeling, the authors consider the notion of a meta causal model which, when indexed, includes SCM as one possible sn... | SP:15ebeef5c445120900b9c94bbcd6a997bfb92899 |
SPP-RL: State Planning Policy Reinforcement Learning | We introduce an algorithm for reinforcement learning , in which the actor plans for the next state provided the current state . To communicate the actor output to the environment we incorporate an inverse dynamics control model and train it using supervised learning . We train the RL agent using off-policy state-of-the... | This paper proposes a new RL algorithm whereby the policy selects a new state rather than action, with constraints to ensure the next state selected is a valid one. The contribution is the new algorithm "SPP-RL" which can be applied to off policy algorithms such as TD3/SAC. There is experimental evidence this may be an... | SP:e65255cf3bf0f0931b173745cc587476f9c2e867 |
SPP-RL: State Planning Policy Reinforcement Learning | We introduce an algorithm for reinforcement learning , in which the actor plans for the next state provided the current state . To communicate the actor output to the environment we incorporate an inverse dynamics control model and train it using supervised learning . We train the RL agent using off-policy state-of-the... | The paper describes an approach where a state-state mapping is learned (called state-planning policy or SPP) coupled with an off-policy RL system (the authors included experiments with DDPG, TD3, and SAC). Learning a state-state mapping, Q(s,s'), needs also to learn an inverse dynamic model to estimate which action (... | SP:e65255cf3bf0f0931b173745cc587476f9c2e867 |
Learning by Directional Gradient Descent | 1 INTRODUCTION . A key requirement of intelligence is the ability to integrate information over time , by constructing an internal representation of state from sequences of observations . This is critical for an agent to predict or control its future experience , whenever its environment is not fully observable ( i.e .... | The paper proposes a new gradient-based learning algorithm for recurrent neural networks, making use of the directional derivative along a candidate direction. The directional derivative serves the purpose of improving the usefulness of a given candidate direction for gradient-based parameter updates by computing the p... | SP:ddd7d3c20a2aed0e4408e88e37806824c5d52b4d |
Learning by Directional Gradient Descent | 1 INTRODUCTION . A key requirement of intelligence is the ability to integrate information over time , by constructing an internal representation of state from sequences of observations . This is critical for an agent to predict or control its future experience , whenever its environment is not fully observable ( i.e .... | This paper proposes to adapt the RTRL technique when training RNNs to make it usable in practice. That is, instead of computing the full gradient of the loss $L$ according to all parameters, the proposed method "DODGE" consists in computing the gradient of $L$ in *one* or a small number of directions in the parameter s... | SP:ddd7d3c20a2aed0e4408e88e37806824c5d52b4d |
Controlling Directions Orthogonal to a Classifier | 1 INTRODUCTION . Many machine learning applications require explicit control of directions that are orthogonal to a predefined one . For example , to ensure fairness , we can learn a classifier that is orthogonal to sensitive attributes such as gender or race ( Zemel et al. , 2013 ; Madras et al. , 2018 ) . Similar , i... | - The paper introduces the notion of "orthogonal classifiers": classifiers that rely on orthogonal variables. It starts with the simple linear case, and adds a definition that also applies to the non-linear case. - The paper proposes two methods to identify a classifier orthogonal to a given one. - It then describes 3 ... | SP:082a02221a8515ae6c08356eeae7ca4412bd2e1f |
Controlling Directions Orthogonal to a Classifier | 1 INTRODUCTION . Many machine learning applications require explicit control of directions that are orthogonal to a predefined one . For example , to ensure fairness , we can learn a classifier that is orthogonal to sensitive attributes such as gender or race ( Zemel et al. , 2013 ; Madras et al. , 2018 ) . Similar , i... | The paper presents classifier orthogonalization technique that works for non-linear classifiers. The idea is to find a method that orthogonalizes the full classifier w.r.t. the principal classifier so that the resulting classifier is statistically orthogonal to principal classifier. The algorithm turns out to be very s... | SP:082a02221a8515ae6c08356eeae7ca4412bd2e1f |
Efficient Certification for Probabilistic Robustness | 1 Introduction . Neural networks have found great success in a wide variety of applications . For many of these applications , understanding when and how a neural network can fail is crucial . Szegedy et al . ( 2014 ) found that almost visually imperceptible perturbations of input images could drastically change the ou... | In this paper, the authors consider a notion of statistical/probabilistic robustness which does not require a model to be robust to all inputs in a specified set, only a certain, high-probability subset of these inputs. The authors rely on a bound propagation methodology to compute the the probability that a given inpu... | SP:09784594743442e7d357697bd4fd0c370df170c3 |
Efficient Certification for Probabilistic Robustness | 1 Introduction . Neural networks have found great success in a wide variety of applications . For many of these applications , understanding when and how a neural network can fail is crucial . Szegedy et al . ( 2014 ) found that almost visually imperceptible perturbations of input images could drastically change the ou... | This work considers problem of local robustness where each input is perturbed according to some probability distribution (e.g. uniform distribution over the L-infinity ball). Proposed approach is based on extending an approach proposed by prior work which uses linear bounds to compute bounds on the output of the networ... | SP:09784594743442e7d357697bd4fd0c370df170c3 |
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning | 1 INTRODUCTION . In machine learning , incremental learning is the process of updating a model as new data becomes available or extended to support further tasks . An incrementally trained model should ideally retain previously attained knowledge while incorporating any new knowledge made available as it trains Syed et... | The authors propose some changes to an existing approach called FullVAE for incremental metric learning for the problem of catastrophic forgetting. Both ConVAER and FullVAE use VAEs for generating feature representation prototypes that could be passed through the intermediate layers of a network. The proposed changes a... | SP:7a0e649fb9f937acd9cbc350d34313a131d2afc0 |
ConVAEr: Convolutional Variational AutoEncodeRs for incremental similarity learning | 1 INTRODUCTION . In machine learning , incremental learning is the process of updating a model as new data becomes available or extended to support further tasks . An incrementally trained model should ideally retain previously attained knowledge while incorporating any new knowledge made available as it trains Syed et... | This paper presents the method for incremental similarity learning using feature replay with VAEs. The experimental section investigates the impact of different loss functions on the final performance of the model. Yet, the discussion of the results lacks any insights which could be beneficial for the community. The id... | SP:7a0e649fb9f937acd9cbc350d34313a131d2afc0 |
Network robustness as a mathematical property: training, evaluation and attack | 1 INTRODUCTION . Safety and security are critical for some complex AI systems involving neural networks , yet they are difficult to ensure . The most famous instance of this problem is guaranteeing robustness against adversarial attacks ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . Intuitively , an image is -b... | The authors are proposing an approach to systematization of the types of robustness of neural network. In particular, they are discussing four types of robustness: through augmentation, through adversarial training, through Lipschitz constraint training and through logical constraints training. The authors discuss the ... | SP:de7e056a3ebbbb4fce17331392af4840953ea064 |
Network robustness as a mathematical property: training, evaluation and attack | 1 INTRODUCTION . Safety and security are critical for some complex AI systems involving neural networks , yet they are difficult to ensure . The most famous instance of this problem is guaranteeing robustness against adversarial attacks ( Szegedy et al. , 2014 ; Goodfellow et al. , 2015 ) . Intuitively , an image is -b... | This paper formally summarizes common robustness notions in the literature as: standard robustness (SR), classification robustness (CR), Lipschitz robustness (LR), and strong classification robustness (SCR). The paper proves LR implies SR, SCR implies CR. Then some empirical studies are conducted. | SP:de7e056a3ebbbb4fce17331392af4840953ea064 |
Deep Attentive Variational Inference | 1 INTRODUCTION . A core line of research in both supervised and unsupervised learning relies on deep probabilistic models . This class of models uses deep neural networks to model distributions that express hypotheses about the way in which the data have been generated . Such architectures are preferred due to their ca... | This paper identifies a common problem in previous VAE related models: adding more stochastic layers to an already very deep model yields small predictive improvement while substantially increasing the inference and training time. Therefore, a new model that proposes to use attention mechanisms to build more expressive... | SP:b5f7c5fcade8783b63c1067e0a14f5bafa88dfce |
Deep Attentive Variational Inference | 1 INTRODUCTION . A core line of research in both supervised and unsupervised learning relies on deep probabilistic models . This class of models uses deep neural networks to model distributions that express hypotheses about the way in which the data have been generated . Such architectures are preferred due to their ca... | This paper improves the architecture of deep VAEs using the attention mechanism. The attention mechanism is used in two ways: 1. layer-wise attention (attending stochastic feature maps which are conditioned on other variables within the hierarchy, interpreted as a mixture of skip connections) 2. non-local attention (... | SP:b5f7c5fcade8783b63c1067e0a14f5bafa88dfce |
Understanding and Preventing Capacity Loss in Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning has achieved remarkable successes in a variety of environments ( Mnih et al. , 2015 ; Moravčı́k et al. , 2017 ; Silver et al. , 2017 ; Abreu et al. , 2019 ) , but the precise reasons for its successes largely remain mysterious . Existing algorithms are highly sensitive to h... | Summary of paper: - The authors identify a problem of "capacity loss" during RL training with neural networks, this is the problem of agents gradually losing the ability to fit new functions while trainig. - The authors argue this is a key factor that hinders learning and is most prominent in sparse reward environment... | SP:ad8e2fe49ecd290848c910422f8ba49924d31065 |
Understanding and Preventing Capacity Loss in Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning has achieved remarkable successes in a variety of environments ( Mnih et al. , 2015 ; Moravčı́k et al. , 2017 ; Silver et al. , 2017 ; Abreu et al. , 2019 ) , but the precise reasons for its successes largely remain mysterious . Existing algorithms are highly sensitive to h... | Summary: - Over course of training, deep RL agents experience "capacity loss", where networks are unable to quickly fit new functions. This problem is further exacerbated by non-stationary predictions (such as bootstrapping) over the course of training. - To prove this problem, the author runs two experiments (Atari an... | SP:ad8e2fe49ecd290848c910422f8ba49924d31065 |
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory | 1 INTRODUCTION . Real-world multi-agent systems are considered partially observable since agents do not have a complete perception of the environment states . The predictive state representation ( PSR ) ( Littman et al. , 2001 ) is a representation of the dynamic system state by a vector of predictions of tests conditi... | The authors present a framework and method in which predictive state representations for multiple agents simultatneously acting and interacting within an environment. This is presented in a general way, where predictive states are Hilbert space operators which when applied to sequences of observations and actions appro... | SP:791463405deae8b2ebe7e98d38022bfa866d02cd |
Reinforcement Learning under a Multi-agent Predictive State Representation Model: Method and Theory | 1 INTRODUCTION . Real-world multi-agent systems are considered partially observable since agents do not have a complete perception of the environment states . The predictive state representation ( PSR ) ( Littman et al. , 2001 ) is a representation of the dynamic system state by a vector of predictions of tests conditi... | The paper introduces a predictive state representation (PSR)-based MARL framework. The framework uses a graph representation to model the interactions between agents. Performance bounds are given for the learned predictive state representation. With the MAPSR, individual policies are trained by replacing the partial o... | SP:791463405deae8b2ebe7e98d38022bfa866d02cd |
Variational Neural Cellular Automata | 1 INTRODUCTION . The process of cellular growth and differentiation is capable of creating an astonishing breadth of form and function , filling every conceivable niche in our ecosystem . Organisms range from complex multi-cellular organisms like trees , birds , and humans to tiny microorganisms living near hydrotherma... | This paper introduces a variational generative model based on Neural Cellular Automata (NCA). The model is called the Variational NCA (VNCA). The VNCA is designed for images: - The encoder is a typical convolutional neural network. - The decoder is an NCA that iteratively refines the image, alternating convolutions... | SP:92b53426c0008d9890df2c2213da536c132b1046 |
Variational Neural Cellular Automata | 1 INTRODUCTION . The process of cellular growth and differentiation is capable of creating an astonishing breadth of form and function , filling every conceivable niche in our ecosystem . Organisms range from complex multi-cellular organisms like trees , birds , and humans to tiny microorganisms living near hydrotherma... | This paper proposes variational extension of Neural Cellular Automata for image generation. It performs experiments on MNIST, Noto Emoji and CelebA. The likelihood results are shown to be significantly behind SOTA on CelebA and also behind on other datasets. The paper provides qualitative analysis of the self-organized... | SP:92b53426c0008d9890df2c2213da536c132b1046 |
EigenGame Unloaded: When playing games is better than optimizing | 1 INTRODUCTION . Large , high-dimensional datasets containing billions of samples are commonplace . Dimensionality reduction to extract the most informative features is an important step in the data processing pipeline which enables faster learning of classifiers and regressors ( Dhillon et al. , 2013 ) , clustering ( ... | The authors study the problem of finding the top $k$ right singular vectors of a data matrix $X$. They propose a modification of an established game theoretic gradient based algorithm $\alpha$-EigenGame. They observe that the gradients of $\alpha$-EigenGame are biased when implemented stochastically by subsampling the ... | SP:64afac7388471f9ecc2ea9a47befeda6e7fd0703 |
EigenGame Unloaded: When playing games is better than optimizing | 1 INTRODUCTION . Large , high-dimensional datasets containing billions of samples are commonplace . Dimensionality reduction to extract the most informative features is an important step in the data processing pipeline which enables faster learning of classifiers and regressors ( Dhillon et al. , 2013 ) , clustering ( ... | The paper considers PCA problem from a game-theoretic view and propose a novel algorithm ($\mu$-EigenGame) with stochastic convergence guarantees. The proposed method introduces an unbiased update which allows greater parallelism over data. The empirical results show that $\mu$-EigenGame outperforms its predecessor $\a... | SP:64afac7388471f9ecc2ea9a47befeda6e7fd0703 |
Reversible Instance Normalization for Accurate Time-Series Forecasting against Distribution Shift | 1 INTRODUCTION . Time-series forecasting plays a significant role in various daily problems such as health care , economics , and traffic engineering ( Che et al. , 2018 ; Bauer et al. , 2016 ; Zhang et al. , 2017 ) . Recently , time-series forecasting models have achieved comparative performance on these problems by o... | This work proposes to use a normalization method to address temporal distribution shift in time-series forecasting. The proposed approach, *RevIN*, consists of two steps: instance normalization on input sequences and "de-normalization" of output sequences by re-using statistics (mean and variance) computed during the n... | SP:549b4b2a88e8b13656ee6bd9425fe1d2be77b334 |
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