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LMSA: Low-relation Mutil-head Self-Attention Mechanism in Visual Transformer | 1 INTRODUCTION . The self-attention mechanism proposed by Vaswani et al . ( 2017 ) is originated from the natural language processing ( NLP ) Transformer network . Due to its superior performance , the self-attention mechanism has been widely used in the NLP field . The standard self-attention formula is defined as : A... | This paper presents a mechanism to reduce the computation costs of a standard self-attention module, named LMSA. The basic idea of LMSA is to reduce the dimension of key&query of self-attention(SA) while keeping the dimension of value unchanged. Therefore, the computational complexity of SA will be reduced from O(N^2D... | SP:bfd698531bd15daa78d1084ad669c886027e687c |
A Collaborative Attention Adaptive Network for Financial Market Forecasting | 1 INTRODUCTION . In recent years , the use of social media information to predict the financial market has attracted the attention of more and more researchers , and some satisfactory experimental results have been achieved . This is due to the fact that social media information contains investor-related attitudes and ... | This paper proposed a novel approach to jointly model text and stock price information and fuse them for stock market forecasting. It encodes text and stock price information in parallel and then fuses them using a co-attention transformer. Empirical results over a real-world dataset and trading simulations demonstrate... | SP:814c416499bba8dfbf99ef716c350bb9256c2dbd |
A Collaborative Attention Adaptive Network for Financial Market Forecasting | 1 INTRODUCTION . In recent years , the use of social media information to predict the financial market has attracted the attention of more and more researchers , and some satisfactory experimental results have been achieved . This is due to the fact that social media information contains investor-related attitudes and ... | This paper proposes a method to fuse tweets and stock prices for stock trend prediction flexibly. The authors claim that the proposed method outperforms other existing fusing methods. Furthermore, according to the results of the market trading simulation, this method achieves higher profits than other methods. | SP:814c416499bba8dfbf99ef716c350bb9256c2dbd |
Rewardless Open-Ended Learning (ROEL) | 1 INTRODUCTION . Development of machine learning over the past several decades has principally relied upon researchers solving a series of challenges proposed by experts within the community ( e.g . MNIST Lecun et al . ( 1998 ) , ATARI Bellemare et al . ( 2013 ) and robotics Yu et al . ( 2021 ) ) . Learning algorithms ... | In this work, the authors a method that lies in the intersection of two subfields of RL, namely open-endedness and unsupervised skill discovery. Specifically, they introduce a method called Rewardless Open-Ended Learning (ROEL) which extends a recent method called POET to perform skill discovery in a reward-free settin... | SP:33f86309e63b0605e3d2b83e839971523e236f5f |
Rewardless Open-Ended Learning (ROEL) | 1 INTRODUCTION . Development of machine learning over the past several decades has principally relied upon researchers solving a series of challenges proposed by experts within the community ( e.g . MNIST Lecun et al . ( 1998 ) , ATARI Bellemare et al . ( 2013 ) and robotics Yu et al . ( 2021 ) ) . Learning algorithms ... | Rewardless Open-Ended Learning (ROEL) is fundamentally a combination between two Paired Open-Ended Trailblazer (POET) and Dynamics-Aware Unsupervised Discovery of Skills (DADS). POET presents a framework for "open-ended learning" which automatically generating progressively more difficult environments to elicit agents ... | SP:33f86309e63b0605e3d2b83e839971523e236f5f |
Provably Robust Adversarial Examples | 1 INTRODUCTION . Deep neural networks ( DNNs ) are vulnerable to adversarial attacks : small input perturbations that cause misclassification ( Szegedy et al. , 2013 ) . This has caused an increased interest in investigating powerful attacks ( Goodfellow et al. , 2015 ; Carlini & Wagner , 2017 ; Madry et al. , 2018 ; A... | This paper presents a novel algorithm for identifying "provably robust adversarial examples": large regions in the input space that provably contain only adversarial examples. Each region corresponds to a single adversarial example $\tilde{x}$ found in the center of the region, along with all the points that can be ge... | SP:264e8676eea93e3f351c395c7165d754db4bd07e |
Provably Robust Adversarial Examples | 1 INTRODUCTION . Deep neural networks ( DNNs ) are vulnerable to adversarial attacks : small input perturbations that cause misclassification ( Szegedy et al. , 2013 ) . This has caused an increased interest in investigating powerful attacks ( Goodfellow et al. , 2015 ; Carlini & Wagner , 2017 ; Madry et al. , 2018 ; A... | The manuscript introduces a definition of provablely-robust adversarial examples, a set of examples that are verified to be classified as different labels compared with the input of interest. The main idea of the technique is to shrink a box-like region from an over approximation to a verifiable smaller sub-region such... | SP:264e8676eea93e3f351c395c7165d754db4bd07e |
Gotta Go Fast When Generating Data with Score-Based Models | Score-based ( denoising diffusion ) generative models have recently gained a lot of success in generating realistic and diverse data . These approaches define a forward diffusion process for transforming data to noise and generate data by reversing it ( thereby going from noise to data ) . Unfortunately , current score... | The paper presents a new SDE solver for the reverse process in score-based models. The algorithm is fast and offers high quality, and avoids some hyperparameter tuning. There is theoretical analysis on the stability and bias of the algorithm. The paper also has experiments comparing the proposed algorithm to several ba... | SP:906e0447c7ae0b27e107132f3795a83d4b7e48e6 |
Gotta Go Fast When Generating Data with Score-Based Models | Score-based ( denoising diffusion ) generative models have recently gained a lot of success in generating realistic and diverse data . These approaches define a forward diffusion process for transforming data to noise and generate data by reversing it ( thereby going from noise to data ) . Unfortunately , current score... | Score-based/diffusion-based generative models can achieve high sample quality. However, their sampling speed is slow due to the large number of evaluations required by numerical SDE solvers. This works aims to accelerate the sampling process by using a more efficient SDE solver. The proposed approach generates data 2 t... | SP:906e0447c7ae0b27e107132f3795a83d4b7e48e6 |
Multivariate Time Series Forecasting with Latent Graph Inference | This paper introduces a new architecture for multivariate time series forecasting that simultaneously infers and leverages relations among time series . We cast our method as a modular extension to univariate architectures where relations among individual time series are dynamically inferred in the latent space obtaine... | The paper proposes using graph neural net (GNN) operations to combine per-series embeddings, to enable multivariate forecasting. Specifically, N individual series are separately encoded for a given time window to get representations per series. These representations are then updated with a GNN - either assuming a ful... | SP:fb6ceb7cc788fb39fbe67529b2c4401f51fd74cb |
Multivariate Time Series Forecasting with Latent Graph Inference | This paper introduces a new architecture for multivariate time series forecasting that simultaneously infers and leverages relations among time series . We cast our method as a modular extension to univariate architectures where relations among individual time series are dynamically inferred in the latent space obtaine... | This paper presents a method to combine information of multivariate time series by extending univariate architectures. The technique uses a graph representation to represent interactions by assuming a bipartite structure, which allows the technique to scale the representation to reduce the complexity from $O(N^2)$ to $... | SP:fb6ceb7cc788fb39fbe67529b2c4401f51fd74cb |
FCause: Flow-based Causal Discovery | 1 INTRODUCTION . Understanding causal relationships between variables is crucial in many applications , including biology ( Koller & Friedman , 2009 ; Sachs et al. , 2005 ) , economics ( Varian , 2016 ; Cunningham , 2020 ) , and healthcare ( Tu et al. , 2019 ) . In addition , such information can also be used to advanc... | This paper proposes a general flow-based approach to learn DAGs from data which provides a unified view of existing continuous optimization methods for structure learning. As a side benefit, the authors demonstrate that the proposed method could naturally be modified to handle missing data. The authors provide empirica... | SP:0fd58ad8e49120d88bdf5fe2b7c0b6cf8d11b789 |
FCause: Flow-based Causal Discovery | 1 INTRODUCTION . Understanding causal relationships between variables is crucial in many applications , including biology ( Koller & Friedman , 2009 ; Sachs et al. , 2005 ) , economics ( Varian , 2016 ; Cunningham , 2020 ) , and healthcare ( Tu et al. , 2019 ) . In addition , such information can also be used to advanc... | This paper proposes to combine the continuous optimization-based causal discovery approach from notears with flow-based function learning. An extension is given to data missing (completely) at random. Also, a unifying framework is presented facilitating the comparison and exchange of ideas between different continuous ... | SP:0fd58ad8e49120d88bdf5fe2b7c0b6cf8d11b789 |
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance | 1 INTRODUCTION . Transfer learning ( TL ) is a set of techniques of using abundant somewhat related source data p ( X ( s ) , Y ( s ) ) to ensure that a model can generalize well to the target domain , defined as either little amount of labelled data p ( X ( t ) , Y ( t ) ) ( supervised ) , and/or a lot of unlabelled d... | This paper focuses on transferability measures both in a supervised and unsupervised context. In particular, the authors propose a shrinkage-based estimation of H-score in order to correct its instability and discuss the limitations of the other approaches on two different scenarios: source model selection or target ta... | SP:480a43692dce83fedcd2464b293c1481d84e8952 |
Newer is not always better: Rethinking transferability metrics, their peculiarities, stability and performance | 1 INTRODUCTION . Transfer learning ( TL ) is a set of techniques of using abundant somewhat related source data p ( X ( s ) , Y ( s ) ) to ensure that a model can generalize well to the target domain , defined as either little amount of labelled data p ( X ( t ) , Y ( t ) ) ( supervised ) , and/or a lot of unlabelled d... | This paper is interested in task transferability measures (both in the supervised and the unsupervised case). Task transferability measures are useful in quantifying how much knowledge of the source domain or model is transferable to the target model (or target domain in the unsupervised setup). Ultimately, one aims at... | SP:480a43692dce83fedcd2464b293c1481d84e8952 |
Global Convergence and Stability of Stochastic Gradient Descent | In machine learning , stochastic gradient descent ( SGD ) is widely deployed to train models using highly non-convex objectives with equally complex noise models . Unfortunately , SGD theory often makes restrictive assumptions that fail to capture the non-convexity of real problems , and almost entirely ignore the comp... | In this paper, the authors consider the global convergence and stability of stochastic gradient descent in a fairly general non-convex setting. They are able to remove the often-assumed unrealistic uniform bounded assumption on the noise, and also relax the global Holder assumption in the literature. Their discussions ... | SP:a84af29bb856abf8eebf6eb8ba8ffd18066f1550 |
Global Convergence and Stability of Stochastic Gradient Descent | In machine learning , stochastic gradient descent ( SGD ) is widely deployed to train models using highly non-convex objectives with equally complex noise models . Unfortunately , SGD theory often makes restrictive assumptions that fail to capture the non-convexity of real problems , and almost entirely ignore the comp... | In this paper, the authors study the behavior of SGD under very general assumptions. The existing global convergence of SGD requires two restrictive assumptions: a global Holder continuity for gradients and unrealistic noise models for stochastic gradients. This paper relaxes the global Holder continuity assumption to ... | SP:a84af29bb856abf8eebf6eb8ba8ffd18066f1550 |
Recursive Construction of Stable Assemblies of Recurrent Neural Networks | 1 INTRODUCTION . Neuro-inspired machine learning has profoundly altered many fields such as computer vision , natural language processing , and computational neuroscience ( Bengio et al. , 2017 ; Hassabis et al. , 2017 ) . While models trained with e.g . deep learning are remarkably powerful , they are for the most par... | In the paper, the authors study stable architectures for RNNs. On the theoretical side, the authors present a series of conditions such that a weight matrix of an RNN is contractive. On the modeling side, the authors propose RNN architectures that have contractive weight matrices. The proposed methods are evaluated on ... | SP:3e145b336f187ed10a193fc6102d245fdd99e004 |
Recursive Construction of Stable Assemblies of Recurrent Neural Networks | 1 INTRODUCTION . Neuro-inspired machine learning has profoundly altered many fields such as computer vision , natural language processing , and computational neuroscience ( Bengio et al. , 2017 ; Hassabis et al. , 2017 ) . While models trained with e.g . deep learning are remarkably powerful , they are for the most par... | This paper is primarily a theoretical contribution to the construction of assemblies of recurrent neural networks. We know that combinations of learned modular components can be powerful and far more tractable than learning bespoke models from scratch, particularly in applied domains (e.g. AlphaGo). Yet so far, we have... | SP:3e145b336f187ed10a193fc6102d245fdd99e004 |
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models | 1 INTRODUCTION . Computed Tomography ( CT ) and Magnetic Resonance Imaging ( MRI ) are commonly used imaging tools for medical diagnosis . Reconstructing CT and MRI images from raw measurements ( sinograms for CT and k-spaces for MRI ) are well-known inverse problems . Specifically , measurements in CT are given by X-r... | The manuscript applies denoising score matching to linear inverse problems to solve compressed sensing problems in medical imaging, such as angular-undersampled CT and accelerated MRI reconstruction. Throughout the paper, the observed measurements $y$ are considered noise-free, which is reflected by a Dirac measurement... | SP:2b838e0fc3a29d7d098f2163a92aa74b1aef2d2f |
Solving Inverse Problems in Medical Imaging with Score-Based Generative Models | 1 INTRODUCTION . Computed Tomography ( CT ) and Magnetic Resonance Imaging ( MRI ) are commonly used imaging tools for medical diagnosis . Reconstructing CT and MRI images from raw measurements ( sinograms for CT and k-spaces for MRI ) are well-known inverse problems . Specifically , measurements in CT are given by X-r... | This paper provides an unsupervised approach to solve the inverse problem for reconstructing medical CT and MRI scans using score-based generative models. The proposed method was evaluated on the LIDC and BraTS datasets. Compared to existing supervised and unsupervised approaches, the proposed method demonstrates compa... | SP:2b838e0fc3a29d7d098f2163a92aa74b1aef2d2f |
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis | 1 INTRODUCTION . 1.1 THE TRUNCATED SVD AND PCA . Let A be an m×n real matrix and A = UΣV T its singular value decomposition ( SVD ) . The rank-k truncated SVD ( rank-k TSVD ) of A is the matrix Ak : = UkΣkV TK , where Uk and Vk are the first k columns of U and V , respectively , and Σk is the leading k × k block of Σ .... | This paper presents a PCA algorithm that terminates after approximate singular values fall below a user provided threshold. The proposed method is based on the FFQR algorithm of Feng et al, but includes a tolerance based stopping criteria. It is stated that the algorithm runs $O(mnl)$ time. Experimental results are pro... | SP:b91c2d68ececc4827e5619ade1afdd2249b0d962 |
An Efficient and Reliable Tolerance-Based Algorithm for Principal Component Analysis | 1 INTRODUCTION . 1.1 THE TRUNCATED SVD AND PCA . Let A be an m×n real matrix and A = UΣV T its singular value decomposition ( SVD ) . The rank-k truncated SVD ( rank-k TSVD ) of A is the matrix Ak : = UkΣkV TK , where Uk and Vk are the first k columns of U and V , respectively , and Σk is the leading k × k block of Σ .... | This paper develops an algorithm that approximates the principal components corresponding to the top singular values. The algorithm requires the user specified tolerance instead of the number of top singular values, which is often the case in practice. It presents the detailed algorithm and evaluate its performance usi... | SP:b91c2d68ececc4827e5619ade1afdd2249b0d962 |
Invariant Causal Representation Learning for Out-of-Distribution Generalization | 1 INTRODUCTION . Modern machine learning algorithms still lack robustness , and may fail to generalize outside of a specific training distribution because they learn easy-to-fit spurious correlations which are prone to change between training and testing environments . We recall the widely used example of classifying i... | This paper proposes invariant Causal Representation Learning (iCaRL) for OOD generalization in the nonlinear setting. The work extends iVAE to a somewhat more general setting and shows the direct cause of the target can be discovered. iCARL is then developed based on the direct cause. . Extensive experiments verify the... | SP:14efc52bb1949c529498fea95f1f5e94710c85a5 |
Invariant Causal Representation Learning for Out-of-Distribution Generalization | 1 INTRODUCTION . Modern machine learning algorithms still lack robustness , and may fail to generalize outside of a specific training distribution because they learn easy-to-fit spurious correlations which are prone to change between training and testing environments . We recall the widely used example of classifying i... | This paper proposes a general framework for so-called "out-of-distribution generalization" that can handle non-linear associations and causal links between variables. It provides proofs for identifiability and generalization. It proposes a general causal model that is sensible for many prediction problems. It proposes ... | SP:14efc52bb1949c529498fea95f1f5e94710c85a5 |
Domino: Discovering Systematic Errors with Cross-Modal Embeddings | 1 INTRODUCTION . Machine learning models often make systematic errors on important subsets ( or slices ) of data1 . For instance , models trained to detect collapsed lungs in chest X-rays have been shown to make predictions based on the presence of chest drains , a device typically used during treatment ( OakdenRayner ... | The paper propose a framework for identifying on which subsets of data machine learning models make systematic errors. The problem is cast in two parts: (1) identify a model that can be identify a subset of data and predict degraded performance of the machine learning model for this subset and (2) ensure that the ident... | SP:d7bfb6a33941b5691275b77917b1d100764b91af |
Domino: Discovering Systematic Errors with Cross-Modal Embeddings | 1 INTRODUCTION . Machine learning models often make systematic errors on important subsets ( or slices ) of data1 . For instance , models trained to detect collapsed lungs in chest X-rays have been shown to make predictions based on the presence of chest drains , a device typically used during treatment ( OakdenRayner ... | Recent studies have proposed automated slice discovery methods (SDMs), which leverage learned model representations to mine input data for slices, or important subgroups of data, on which a model performs poorly. An ideal SDM should automatically identify: 1. Slices that contain examples on which the model underperfo... | SP:d7bfb6a33941b5691275b77917b1d100764b91af |
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data | 1 INTRODUCTION . The study of the human brain is probably one of the greatest challenges in the field of neuroscience . Recent developments in experimental neuroscience have considerably increased the availability of novel recordings and reconstructions shedding further light into the structure and function of the brai... | Authors show how the nervous system of C. elegans can be modelled and simulated with data-driven models using different neural network architectures. Specifically, they target the use of state of the art recurrent neural networks architectures such as LSTMs and GRUs and compare these architectures in terms of their pro... | SP:e0748a938f66f678cbe7827a9f336bdf4c1ec891 |
Modelling neuronal behaviour with time series regression: Recurrent Neural Networks on synthetic C. elegans data | 1 INTRODUCTION . The study of the human brain is probably one of the greatest challenges in the field of neuroscience . Recent developments in experimental neuroscience have considerably increased the availability of novel recordings and reconstructions shedding further light into the structure and function of the brai... | This paper investigates the use of recurrent neural networks as a model reduction tool in computational neuroscience. More specifically, the authors consider the problem of predicting the activity of a set of four neurons in the C Elegans nematode worm, resulting from the (simulated) electrical stimulation of other neu... | SP:e0748a938f66f678cbe7827a9f336bdf4c1ec891 |
Occupy & Specify: Investigations into a Maximum Credit Assignment Occupancy Objective for Data-efficient Reinforcement Learning | 1 PROBLEM STATEMENT . Learning in the real world implies dealing with very large , potentially unlimited environments , over which the data to collect is seemingly infinite . Efficient exploration is thus one of the key aspects of open-ended learning Santucci et al . ( 2020 ) , when no final model of the environment ca... | # Summary & Contributions * This paper examines a variational approach to reinforcement learning, leveraging occupancy measures over previously visited and future state-action pairs in order to address the exploration challenge. * The author propose a variational approximation to the so-called "conditional occupancy" o... | SP:916a926ade24fe69c246bd54d314087bafb1b5b8 |
Occupy & Specify: Investigations into a Maximum Credit Assignment Occupancy Objective for Data-efficient Reinforcement Learning | 1 PROBLEM STATEMENT . Learning in the real world implies dealing with very large , potentially unlimited environments , over which the data to collect is seemingly infinite . Efficient exploration is thus one of the key aspects of open-ended learning Santucci et al . ( 2020 ) , when no final model of the environment ca... | The authors address the problem of exploration in reinforcement learning. They suggest applying the maximum entropy principle over the state space and try to maximize the make the occupancy of the policy as entropic as possible while still optimizing the original goal of maximizing cumulative reward. They claims includ... | SP:916a926ade24fe69c246bd54d314087bafb1b5b8 |
Dynamic Graph Representation Learning via Graph Transformer Networks | 1 INTRODUCTION . In recent years , graph representation learning has been recognized as a fundamental learning problem and has received much attention due to its widespread use in various domains , including social network analysis ( Kipf & Welling , 2017 ; Hamilton et al. , 2017 ) , traffic prediction ( Cui et al. , 2... | In this study, the authors propose a new graph transformer network for dynamic graph representation. To solve the challenges of static graphs learning and the temporal information aggregating, this paper introduces a Dynamic Graph Transformer (DGT) which contains three components: (1) a two-tower Transformer-based meth... | SP:f24b5b36d4329213d20b80cf6da2ed289f52890d |
Dynamic Graph Representation Learning via Graph Transformer Networks | 1 INTRODUCTION . In recent years , graph representation learning has been recognized as a fundamental learning problem and has received much attention due to its widespread use in various domains , including social network analysis ( Kipf & Welling , 2017 ; Hamilton et al. , 2017 ) , traffic prediction ( Cui et al. , 2... | This paper makes an attempt to study the dynamic graph representation learning. The paper proposes a couple tricks for the combination with graph transformer networks. The tricks include sampling, union graph, pretraining etc. Some experiments have been conducted. | SP:f24b5b36d4329213d20b80cf6da2ed289f52890d |
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications | 1 INTRODUCTION . Anderson mixing ( AM ) ( Anderson , 1965 ) is a sequence acceleration method ( Brezinski et al. , 2018 ) in scientific computing . It is widely used to accelerate the slow convergence of fixed-point iterations ( Lin et al. , 2019 ; Fu et al. , 2020 ; An et al. , 2017 ) , e.g. , the self-consistent fiel... | The paper introduces 3 new variants of Anderson Mixing methods relying on very limited short term memory. This makes these methods more attractive for usual machine learning workloads. The paper also provides detailed analysis of the performance of each of these AM algorithms, as well as thorough experiments, showcasin... | SP:03a357be8e34c07221c6f2829928bf733428f0e3 |
A Class of Short-term Recurrence Anderson Mixing Methods and Their Applications | 1 INTRODUCTION . Anderson mixing ( AM ) ( Anderson , 1965 ) is a sequence acceleration method ( Brezinski et al. , 2018 ) in scientific computing . It is widely used to accelerate the slow convergence of fixed-point iterations ( Lin et al. , 2019 ; Fu et al. , 2020 ; An et al. , 2017 ) , e.g. , the self-consistent fiel... | The paper proposes a new class of memory-efficient Anderson mixing (AM) methods. Compared with classical Anderson mixing which requires saving m historical iterates, the new variants require only storing two historical iterates while keeping good performance. Convergence analyses are given to the proposed variants show... | SP:03a357be8e34c07221c6f2829928bf733428f0e3 |
Relational Multi-Task Learning: Modeling Relations between Data and Tasks | 1 INTRODUCTION . The general idea of learning from multiple tasks has been explored under different settings , including multi-task learning ( Caruana , 1997 ) , meta learning ( Finn et al. , 2017 ) , and few-shot learning ( Vinyals et al. , 2016 ) . While these learning settings have inspired models that can utilize r... | The paper exploits the multi-task modeling using a heterogenous Graph Neural Network. The key contribution of the work is having both data (sample) and task nodes in the same graph, focusing on data-task relation, accommodating for sparse task labels by design. Experimental results show relevant improvements with the ... | SP:40aeb37eea243288569e41cdd3100403021fc866 |
Relational Multi-Task Learning: Modeling Relations between Data and Tasks | 1 INTRODUCTION . The general idea of learning from multiple tasks has been explored under different settings , including multi-task learning ( Caruana , 1997 ) , meta learning ( Finn et al. , 2017 ) , and few-shot learning ( Vinyals et al. , 2016 ) . While these learning settings have inspired models that can utilize r... | In this paper, they introduce relational multi-task learning in which they construct a graph with the data points and task and labels as edges. They represent the data points as the embedding of NN model and task as the last layer of NN model for that task. Then they solve the link label prediction problem between each... | SP:40aeb37eea243288569e41cdd3100403021fc866 |
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) has been proved effective in various complex domains , including the game of Go ( Silver et al. , 2017 ) , Dota ( OpenAI , 2018 ) , and StarCraft II ( Vinyals et al. , 2017 ) . However , as problems increase in scale , the training of DRL models is increasingly timec... | This paper introduces a new distributed value-based multi-agent reinforcement learning framework to solve problems faced by multi-agent tasks. It divides the system into two parts, multiple containers, and one centralizer. Containers are trained with trajectories generated by their own actors interacting with the envir... | SP:1585c85c36d252c0d9f3d321a7ae59f12ea60dbd |
Containerized Distributed Value-Based Multi-Agent Reinforcement Learning | 1 INTRODUCTION . Deep reinforcement learning ( DRL ) has been proved effective in various complex domains , including the game of Go ( Silver et al. , 2017 ) , Dota ( OpenAI , 2018 ) , and StarCraft II ( Vinyals et al. , 2017 ) . However , as problems increase in scale , the training of DRL models is increasingly timec... | This paper focuses on an interesting and important question: how to perform distributed multi-agent deep reinforcement learning? The author first proposed three challenges to be considered: 1) Demanding data transfer. 2) Inter-process communication. 3) Effective Exploration. Further, the author proposed a container-bas... | SP:1585c85c36d252c0d9f3d321a7ae59f12ea60dbd |
A Generalised Inverse Reinforcement Learning Framework | 1 INTRODUCTION . Modelling the behaviours of rational agents is a long active research topic . From early attempts to decompose human and animal locomotion Muybridge ( 1979 ) to more recent approaches to simulate human movements Li & Todorov ( 2006 ) ; Mombaur ( 2009 ) ; Schultz & Mombaur ( 2009 ) , the common thread i... | This paper points out that the classical IRL approach has a tendency to match those occupancy measures that favor short-term behavior. To address this issue, a reformulation is proposed based on GAIL in order to put more emphasis on matching longer-term behavior. Specifically, the main difference is to replace the stan... | SP:735cdc64faed0eb28babb286e250fd3fbb8d9047 |
A Generalised Inverse Reinforcement Learning Framework | 1 INTRODUCTION . Modelling the behaviours of rational agents is a long active research topic . From early attempts to decompose human and animal locomotion Muybridge ( 1979 ) to more recent approaches to simulate human movements Li & Todorov ( 2006 ) ; Mombaur ( 2009 ) ; Schultz & Mombaur ( 2009 ) , the common thread i... | The paper proposes a new formulation for inverse reinforcement learning that aims to address the _bias against policies with longer mixing times_. The key contribution of the paper is the proposal of an alternative optimality criterion that arguably addresses the aforementioned bias by considering, for a given policy, ... | SP:735cdc64faed0eb28babb286e250fd3fbb8d9047 |
Sharper Utility Bounds for Differentially Private Models | ) high probability excess population risk bound for differentially private al- gorithms under the assumptions G-Lipschitz , L-smooth , and Polyak-Łojasiewicz condition , based on gradient perturbation method . If we replace the properties G-Lipschitz and L-smooth by α-Hölder smoothness ( which can be used in nonsmooth... | This paper analyzes the utility bounds of the gradient-perturbation based DP algorithm. They first provide DP by previous result (it is not the key point in this paper). Then, by applying the Generalized Bernstein condition, they give $O(p^{0.5}/(n\epsilon))$ high probability excess population risk bound under the prop... | SP:1bc5cf890e7e8ff9921c1754e3013df7223caa1d |
Sharper Utility Bounds for Differentially Private Models | ) high probability excess population risk bound for differentially private al- gorithms under the assumptions G-Lipschitz , L-smooth , and Polyak-Łojasiewicz condition , based on gradient perturbation method . If we replace the properties G-Lipschitz and L-smooth by α-Hölder smoothness ( which can be used in nonsmooth... | The paper achieves high probability excess risk bound with rate O(1/n) w.r.t n for DP models via uniform stability by using Generalized Bernstein condition under G-Lipschitz, L-smooth, and PL condition. Then the authors expand the result to a more general case, only requiring α-Ho ̈lder smoothness, Polyak-Łojasiewicz c... | SP:1bc5cf890e7e8ff9921c1754e3013df7223caa1d |
Back to Basics: Efficient Network Compression via IMP | 1 INTRODUCTION . Modern Neural Network architectures are commonly highly over-parameterized ( Zhang et al. , 2016 ) , containing millions or even billions of parameters , resulting in both high memory requirements as well as computationally intensive and long training and inference times . It has been shown however ( L... | The paper studies a fundamental and important research approach in network pruning: iterative magnitude pruning (IMP). Previously IMP is criticized to be time-consuming, layer-independent and sub-optimal in performance. In this paper, extensive empirical studies are conducted to show that under proper learning rates, I... | SP:6afe8cd7741417913dd2ccef7cb4feccd5a592ef |
Back to Basics: Efficient Network Compression via IMP | 1 INTRODUCTION . Modern Neural Network architectures are commonly highly over-parameterized ( Zhang et al. , 2016 ) , containing millions or even billions of parameters , resulting in both high memory requirements as well as computationally intensive and long training and inference times . It has been shown however ( L... | This work focuses on highlighting the strengths of Iterative Magnitude Pruning (IMP). Specifically, that it is capable of achieving strong performance when compared to more complex pruning approaches. The work explores the common arguments against IMP like, a) it reaches sub-optimal states since training doesn't compen... | SP:6afe8cd7741417913dd2ccef7cb4feccd5a592ef |
Data-Efficient Augmentation for Training Neural Networks | 1 INTRODUCTION . Data augmentation expands the training data by applying transformations , such as rotations or crops for images , to the original training examples . Due to its effectiveness , data augmentation is a key component in achieving nearly all state-of-the-art results in deep learning applications ( Shorten ... | Deep learning uses augmentations to improve generalization performance. Using all augmentations for a dataset may slow down training. A subset selection technique is proposed (e.g., "coreset") using insights from the Neural Tangent Kernel (NMT) framework such that an alignment between the NMT Jacobian and the residuals... | SP:2d12ba88cb0ad6b2d1f6174b3bb10d138d32c4e9 |
Data-Efficient Augmentation for Training Neural Networks | 1 INTRODUCTION . Data augmentation expands the training data by applying transformations , such as rotations or crops for images , to the original training examples . Due to its effectiveness , data augmentation is a key component in achieving nearly all state-of-the-art results in deep learning applications ( Shorten ... | The authors model data augmentation as an additive perturbation and analyze its effect on training dynamics and how it enlarges the smaller singular values of the network jacobian. Then they propose a new method to iteratively extract a subset of the training data that when augmented closely capture the full augmented ... | SP:2d12ba88cb0ad6b2d1f6174b3bb10d138d32c4e9 |
Bayesian Active Learning with Fully Bayesian Gaussian Processes | 1 INTRODUCTION . Gaussian Processes ( GPs ) are the canonical models to use for Bayesian optimization and metamodeling ( Snoek et al. , 2012 ; Gramacy , 2020 ) . GPs are well-known for their ability to deal with small to medium size data sets as well as balancing complexity and regularization - together with their inhe... | This paper takes a fully Bayesian approach to Gaussian Process (GP) active learning by using MCMC sampling to consider multiple model hypotheses from a full posterior, from which it selects examples for GP regression using two new active learning strategies --- Bayesian Query-by-Committee (B-QBC) and Query by Mixture o... | SP:993d684939ae8074669ba646e49b48ba6b2bf0b9 |
Bayesian Active Learning with Fully Bayesian Gaussian Processes | 1 INTRODUCTION . Gaussian Processes ( GPs ) are the canonical models to use for Bayesian optimization and metamodeling ( Snoek et al. , 2012 ; Gramacy , 2020 ) . GPs are well-known for their ability to deal with small to medium size data sets as well as balancing complexity and regularization - together with their inhe... | This paper introduces two new active-learning strategies for Gaussian process regression, based on a fully-Bayesian treatment of GPs. Instead of using fixed hyperparamers obtained by maximizing the marginal log-likelihood of the GP model, the authors suggest learning a posterior belief over GP hyperparameters, and make... | SP:993d684939ae8074669ba646e49b48ba6b2bf0b9 |
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions | 1 INTRODUCTION . Over the past few years ML models have been increasingly deployed to make critical decisions related to loan approval ( Siddiqi , 2012 ) , insurance ( Scism , 2019 ) , allocation of public resources ( Chouldechova et al. , 2018 ; Shroff , 2017 ) and hiring decisions ( Ajunwa et al. , 2016 ) . Decisions... | In this paper, the problem of algorithmic recourse is studied where the goal is to find best recourse (counterfactual set) that is optimized for user cost. The author proposed new user-incurred cost evaluation method, Expected Minimum Cost (EMC), which approximate user satisfaction without assuming a fixed global user ... | SP:af5ebe07a96714c0bb85aef1f4b3e23120db142e |
Low-Cost Algorithmic Recourse for Users With Uncertain Cost Functions | 1 INTRODUCTION . Over the past few years ML models have been increasingly deployed to make critical decisions related to loan approval ( Siddiqi , 2012 ) , insurance ( Scism , 2019 ) , allocation of public resources ( Chouldechova et al. , 2018 ; Shroff , 2017 ) and hiring decisions ( Ajunwa et al. , 2016 ) . Decisions... | This work introduces a new method for identifying actionable recourses for users with user-specific cost functions. Users’ cost functions are hidden from the recourse method. The paper proposed a discrete optimization algorithm COLS to solve the objective EMC. It further used a popular real-world dataset to illustrate ... | SP:af5ebe07a96714c0bb85aef1f4b3e23120db142e |
Amortized Implicit Differentiation for Stochastic Bilevel Optimization | 1 INTRODUCTION . Bilevel optimization refers to a class of algorithms for solving problems with a hierarchical structure involving two levels : an inner and an outer level . The inner-level problem seeks a solution y ? ( x ) minimizing a cost g ( x , y ) over a set Y given a fixed outer variable x in a set X . The oute... | The paper studies the problem of bi-level optimization. In particular, it considers algorithms based on inexact implicit differentiation, where the inner problem and the implicit differentiation are not solved exactly. Warm-up is used when solving the inner problem and implicit differentiation. The convergence of the p... | SP:857b454e408e1ad21ccb9372ad5feb89d95cb055 |
Amortized Implicit Differentiation for Stochastic Bilevel Optimization | 1 INTRODUCTION . Bilevel optimization refers to a class of algorithms for solving problems with a hierarchical structure involving two levels : an inner and an outer level . The inner-level problem seeks a solution y ? ( x ) minimizing a cost g ( x , y ) over a set Y given a fixed outer variable x in a set X . The oute... | This work focuses on bilevel optimization. The key innovation here is the warm start, which enables improved complexity bounds (under some settings). The analysis nicely builds on singularly perturbed systems (SPS). Numerical tests are also provided on both synthetic and real problems, where the merit of warm start is ... | SP:857b454e408e1ad21ccb9372ad5feb89d95cb055 |
Optimized Separable Convolution: Yet Another Efficient Convolution Operator | The convolution operation is the most critical component in recent surge of deep learning research . Conventional 2D convolution needs O ( C2K2 ) parameters to represent , where C is the channel size and K is the kernel size . The amount of parameters has become really costly considering that these parameters increased... | The convolution operator is the fundamental unit of most modern DNNs. This paper summarizes the existing (efficient) convolution operators and formulates an optimization problem to choose a few important parameters for the convolution. The authors show that their convolution under certain constraints requires fewer par... | SP:0e1607f0f226624429ad959c1f1ee2960fc7de53 |
Optimized Separable Convolution: Yet Another Efficient Convolution Operator | The convolution operation is the most critical component in recent surge of deep learning research . Conventional 2D convolution needs O ( C2K2 ) parameters to represent , where C is the channel size and K is the kernel size . The amount of parameters has become really costly considering that these parameters increased... | This paper proposed an optimized version of depth separable convolution. Optimal separable convolution reduces the model size by replacing both depth-wise and point-wise convolution with group convolution. Furthermore, they allow overlapped channel between group convolution and this was swept to show ablation compariso... | SP:0e1607f0f226624429ad959c1f1ee2960fc7de53 |
Improving Out-of-Distribution Robustness via Selective Augmentation | 1 INTRODUCTION . To deploy machine learning algorithms in real-world applications , we must pay attention to distribution shifts , i.e . when the test distribution is different from the training distribution , which substantially degrades model performance . In this paper , we refer this problem as out-of-distribution ... | This paper propose a mixup-style data augmentation method under the data distribution shift context. In particular, data distributions are formulated as mixture of distributions (i.e., domains), and two distribution shift scenarios are considered: (1) domain shift, where the test domain and train domain are disjoint. (... | SP:ca6acbc11e7d3f693e4710fe6adb8d71cde3fe6b |
Improving Out-of-Distribution Robustness via Selective Augmentation | 1 INTRODUCTION . To deploy machine learning algorithms in real-world applications , we must pay attention to distribution shifts , i.e . when the test distribution is different from the training distribution , which substantially degrades model performance . In this paper , we refer this problem as out-of-distribution ... | Authors introduced approaches aimed at learning invariant predictors across data sources. Rather than using distribution/risk matching schemes as often done by previous work, they propose to train models against mixtures of data points as a means to avoid that models rely on spurious correlations between domain and cla... | SP:ca6acbc11e7d3f693e4710fe6adb8d71cde3fe6b |
Best Practices in Pool-based Active Learning for Image Classification | 1 INTRODUCTION . Deep learning methods require large amounts of labeled data samples to train . Unfortunately , annotating new datasets consisting of thousands or millions of images is very costly . A research topic that focuses on maximizing the performance of deep learning models with a given annotation budget is act... | The paper provides benchmarking of some of the popular active learning methods on CIFAR10, SVHN and FashionMNIST datasets. Effects of factors such as choice of backbone, data augmentation, optimizers, learning rate, cold vs warm starting are studied and the conclusions are provided as best practices. Analysis is also ... | SP:ce6ab36fc3d97a419858fb6f5dd6a0425a389b8b |
Best Practices in Pool-based Active Learning for Image Classification | 1 INTRODUCTION . Deep learning methods require large amounts of labeled data samples to train . Unfortunately , annotating new datasets consisting of thousands or millions of images is very costly . A research topic that focuses on maximizing the performance of deep learning models with a given annotation budget is act... | This paper conducts an extensive analysis of state-of-the-art Active Learning (AL) methods (Coreset, BADGE, LL4AL, JLS, and WAAL) and studies the effect of different training settings (Backbone architecture, Initializing backbone weights, Optimizer, and learning rates, with and without data augmentation) and their effe... | SP:ce6ab36fc3d97a419858fb6f5dd6a0425a389b8b |
HYPOCRITE: Homoglyph Adversarial Examples for Natural Language Web Services in the Physical World | 1 INTRODUCTION . Artificial Intelligence ( AI ) has shown the potential of convenience in many domains . With the advance of AI , people are living affluent lives by AI . AI can judge what is difficult for humans to make , classify what humans struggle with , predict what humans can never measure , and even recommend t... | This paper proposes using homoglyphs to attack commercial NLP models for sentiment classification. Homoglyphs look like the characters in English language but they are encoded differently and, therefore, treated differently by the model. They experiment on various MLaaS models and show that their attack can effectively... | SP:dccdd07d4daa907b04b330248f9a796cf2e2f361 |
HYPOCRITE: Homoglyph Adversarial Examples for Natural Language Web Services in the Physical World | 1 INTRODUCTION . Artificial Intelligence ( AI ) has shown the potential of convenience in many domains . With the advance of AI , people are living affluent lives by AI . AI can judge what is difficult for humans to make , classify what humans struggle with , predict what humans can never measure , and even recommend t... | The paper proposes an attack method that generates homoglyph adversarial examples for NLP APIs. The method replaces English characters with other international characters that look similar. The paper shows empirically that the method achieves good performance for several real-world APIs. | SP:dccdd07d4daa907b04b330248f9a796cf2e2f361 |
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations | Top-k predictions are used in many real-world applications such as machine learning as a service , recommender systems , and web searches . ℓ0-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed ... | This paper provides an almost tight l0-norm certified robustness guarantee for top-k predictions against adversarial perturbations, which extends certified radius of the top-1 prediction from Levine & Feizi (2019) to that of the top-k predictions, and the l2-norm certified radius from Jia et al. (2020) to the l0-norm c... | SP:ee6c7dc9a106d5bebb879ca226723a2fc294b9fb |
Almost Tight L0-norm Certified Robustness of Top-k Predictions against Adversarial Perturbations | Top-k predictions are used in many real-world applications such as machine learning as a service , recommender systems , and web searches . ℓ0-norm adversarial perturbation characterizes an attack that arbitrarily modifies some features of an input such that a classifier makes an incorrect prediction for the perturbed ... | The paper provides both theoretical progress and experimental results on certified robustness of top-$k$ predictions. Specifically, on the theory side, the paper shows that the randomized ablation (Levin & Feizi) has $\ell_0$-norm certified radius of top-$k$ predictions. Also, the paper proves that the certified radius... | SP:ee6c7dc9a106d5bebb879ca226723a2fc294b9fb |
Lottery Tickets can have Structural Sparsity | The lottery ticket hypothesis ( LTH ) has shown that dense models contain highly sparse subnetworks ( i.e. , winning tickets ) that can be trained in isolation to match full accuracy . Despite many exciting efforts being made , there is one “ commonsense ” seldomly challenged : a winning ticket is found by iterative ma... | LTH (lottery ticket hypothesis) was mainly proposed for unstructured pruning. This paper shows it can also be validated on structured pruning, for the first time. The key for them to achieve so is the newly proposed post-processing techniques, refilling(+) and regrouping. They show by these techniques, structural winni... | SP:3030aeb9862c75808ed5e9111432287520ff5062 |
Lottery Tickets can have Structural Sparsity | The lottery ticket hypothesis ( LTH ) has shown that dense models contain highly sparse subnetworks ( i.e. , winning tickets ) that can be trained in isolation to match full accuracy . Despite many exciting efforts being made , there is one “ commonsense ” seldomly challenged : a winning ticket is found by iterative ma... | This work proposes a method to effectively find structurally sparse winning tickets. It consists of some post-processing techniques that can be added to each round of standard iterative magnitude pruning (IMP) methods. Starting from unstructured sparse sub-networks, the method uses a "re-filling and re-grouping" manner... | SP:3030aeb9862c75808ed5e9111432287520ff5062 |
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows | 1 INTRODUCTION . In the past several years , invertible flow networks emerged as powerful deep learning models to learn maps between distributions ( Durkan et al. , 2019a ; Grathwohl et al. , 2018 ; Huang et al. , 2018 ; Jaini et al. , 2019 ; Kingma et al. , 2016 ; Kingma & Dhariwal , 2018 ; Kobyzev et al. , 2020 ; Kru... | This paper studied the expressive power of models composed of invertible flows and injective embeddings. First, this paper defined the concept of the Embedding Gap as the measure by which a model can approximate an embedding with low-dimensional support. Then, this paper defined the concept of MEP as the ability to app... | SP:5bd15d735380afa17fd1dde6df22de951699a755 |
Universal Joint Approximation of Manifolds and Densities by Simple Injective Flows | 1 INTRODUCTION . In the past several years , invertible flow networks emerged as powerful deep learning models to learn maps between distributions ( Durkan et al. , 2019a ; Grathwohl et al. , 2018 ; Huang et al. , 2018 ; Jaini et al. , 2019 ; Kingma et al. , 2016 ; Kingma & Dhariwal , 2018 ; Kobyzev et al. , 2020 ; Kru... | In this paper, the authors studies flow models by a newly developed approximation measure when the data is on a low-dimensional manifold. Specifically, they consider an architecture that alternates a bijective function with the same input and output dimensions, and a function with a larger dimension at the output. Ther... | SP:5bd15d735380afa17fd1dde6df22de951699a755 |
How to Improve Sample Complexity of SGD over Highly Dependent Data? | 1 INTRODUCTION . Stochastic optimization algorithms have attracted great attention in the past decade due to its successful applications to a broad research areas , including deep learning ( Goodfellow et al. , 2016 ) , reinforcement learning ( Sutton & Barto , 2018 ) , online learning ( Bottou , 2010 ; Hazan , 2017 ) ... | In stochastic convex optimization (SCO) characterized by the following objective for convex $f$, $$\min_w f(w) := \mathbb{E}_{\xi\sim \mu}[F(w; \xi)],$$ we typically assume repeated access to the noise distribution $\mu$, for sampling i.i.d. random variables $(\xi_t)_t$. This access can be used to obtain unbiased estim... | SP:ac7517dabc5ac45ae486f427a2efeeed0191e5e7 |
How to Improve Sample Complexity of SGD over Highly Dependent Data? | 1 INTRODUCTION . Stochastic optimization algorithms have attracted great attention in the past decade due to its successful applications to a broad research areas , including deep learning ( Goodfellow et al. , 2016 ) , reinforcement learning ( Sutton & Barto , 2018 ) , online learning ( Bottou , 2010 ; Hazan , 2017 ) ... | This paper studies the sample complexity of a few variants of SGD in solving optimization problems over dependent data. The dependent data introduces non-negligible bias in SGD that slows down convergence of the algorithm, and this paper adopts \phi-mixing data dependence models, to quantify the level of dependence in ... | SP:ac7517dabc5ac45ae486f427a2efeeed0191e5e7 |
A Boosting Approach to Reinforcement Learning | 1 INTRODUCTION . The field of reinforcement learning , formally modelled as learning in Markov decision processes ( MDP ) , models the mechanism of learning from rewards , as opposed to examples . Although the case of tabular MDPs is well understood , the main difficulty in applying RL to practice is the size of the st... | In this paper, the authors study boosting in RL, i.e., how to convert weak learners into effective policies. The authors provide an algorithm that improves the accuracy of the weak learners iteratively, and the sample complexity and running time do not explicitly depend on the number of states. In order to overcome t... | SP:954686264f83d5d8a4ccaff7cc5130be733f7bb0 |
A Boosting Approach to Reinforcement Learning | 1 INTRODUCTION . The field of reinforcement learning , formally modelled as learning in Markov decision processes ( MDP ) , models the mechanism of learning from rewards , as opposed to examples . Although the case of tabular MDPs is well understood , the main difficulty in applying RL to practice is the size of the st... | This paper proposes a new approach for solving RL problems with sample complexity independent of the number of states. Rather than imposing structural assumptions, the authors consider access to weak learners and propose a way to combine these weak learners effectively to generate a near optimal policy. The sample comp... | SP:954686264f83d5d8a4ccaff7cc5130be733f7bb0 |
A Simple Reward-free Approach to Constrained Reinforcement Learning | In constrained reinforcement learning ( RL ) , a learning agent seeks to not only optimize the overall reward but also satisfy the additional safety , diversity , or budget constraints . Consequently , existing constrained RL solutions require several new algorithmic ingredients that are notably different from standard... | The paper applies the techniques from reward-free RL literature to the constrained RL setting. They propose a meta-algorithm that takes a reward-free RL solver and uses it to solve the approachability and constrained-RL problems with convex constraints. The proposed approach comes with an overhead factor that is logari... | SP:d1ed10fc70ad59ec4b69fa48a20155835e655b2d |
A Simple Reward-free Approach to Constrained Reinforcement Learning | In constrained reinforcement learning ( RL ) , a learning agent seeks to not only optimize the overall reward but also satisfy the additional safety , diversity , or budget constraints . Consequently , existing constrained RL solutions require several new algorithmic ingredients that are notably different from standard... | The paper introduces a connection between reward-free and constrained MDPs. The interesting result in the paper is the fact that it is possible to use any reward-free method to solve constrained and approachability problems. In particular, they show that starting from an RFE method with sample guarantees, it is possibl... | SP:d1ed10fc70ad59ec4b69fa48a20155835e655b2d |
Universal Controllers with Differentiable Physics for Online System Identification | 1 INTRODUCTION . In order for robots to shine in real-world applications , they need to handle ever-changing and unpredictable situations in real environments . For instance , a robot waiter should be able to serve a new type of dish without spilling food , and an autonomous vehicle should take a person safely to an un... | The paper tackles the problem of learning robot controllers that can handle changing or unknown environments. It proposes to use differentiable physics for online system identification and reinforcement learning for offline policy training. The differentiable physics module estimates simulation parameters from robot hi... | SP:a8e7d4ce947d6525daace6f13bac6cfb3a68be6e |
Universal Controllers with Differentiable Physics for Online System Identification | 1 INTRODUCTION . In order for robots to shine in real-world applications , they need to handle ever-changing and unpredictable situations in real environments . For instance , a robot waiter should be able to serve a new type of dish without spilling food , and an autonomous vehicle should take a person safely to an un... | This paper proposes an algorithm to control robots with a universal controller conditioned on the robot parameters, that are identified online using differential simulation. The idea is simple yet interesting: giving the controller explicit information about the system could definitely help performance. Estimating thos... | SP:a8e7d4ce947d6525daace6f13bac6cfb3a68be6e |
Mirror Descent Policy Optimization | 1 INTRODUCTION . An important class of RL algorithms consider an additional objective in their policy optimization that aims at constraining the consecutive policies to remain close to each other . These algorithms are referred to as trust region or proximity-based , resonating the fact that they make the new policy to... | The paper connects the optimization method, mirror descent, to the study of the policy optimization method. Based on the mirror descent principle, the paper proposes the MDPO algorithm, which updates the policy via approximately solving a trust-region problem. The paper proposes the on-policy and off-policy variants of... | SP:d4bbe950c85fca32385520bf998941552e7543c1 |
Mirror Descent Policy Optimization | 1 INTRODUCTION . An important class of RL algorithms consider an additional objective in their policy optimization that aims at constraining the consecutive policies to remain close to each other . These algorithms are referred to as trust region or proximity-based , resonating the fact that they make the new policy to... | Summary: Inspired by recent theoretical analysis of TRPO and PPO that use mirror descent, this paper proposes two new algorithms that directly minimize a mirror descent objective by taking multiple gradient steps. While similar to TRPO and PPO, MDPO is different in important ways, and happens to perform better in pract... | SP:d4bbe950c85fca32385520bf998941552e7543c1 |
Can an Image Classifier Suffice For Action Recognition? | 1 INTRODUCTION The recent advances in convolutional neural networks ( CNNs ) ( He et al. , 2016 ; Tan & Le , 2019 ) , along with the availability of large-scale video benchmark datasets ( Kay et al. , 2017 ; Monfort et al. , 2019 ; Damen et al. , 2020 ) , have significantly improved action recognition , one of the fund... | The paper deals with action recognition in videos, i.e. detecting to which class a given sequence of frames belongs to. However, the paper proposes to explore whether an image classifier (instead of a video or spatiotemporal-based classifier) would already be enough to accomplish this task. In order to do so, the autho... | SP:5d9783cb0a70b17938f30263aac12ab010d63844 |
Can an Image Classifier Suffice For Action Recognition? | 1 INTRODUCTION The recent advances in convolutional neural networks ( CNNs ) ( He et al. , 2016 ; Tan & Le , 2019 ) , along with the availability of large-scale video benchmark datasets ( Kay et al. , 2017 ; Monfort et al. , 2019 ; Damen et al. , 2020 ) , have significantly improved action recognition , one of the fund... | The paper proposes to perform action recognition by first rearranging the frames from a video into a 3x3 or 4x4 grid to form a "super image", and then giving the super image to a standard image classifier to perform action recognition. Given that this super image will be a larger image, the paper leverages the more mem... | SP:5d9783cb0a70b17938f30263aac12ab010d63844 |
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations | In real scenarios , state observations that an agent observes may contain measurement errors or adversarial noises , misleading the agent to take suboptimal actions or even collapse while training . In this paper , we study the training robustness of distributional Reinforcement Learning ( RL ) , a class of state-of-th... | This work presented State-Noisy Markov Decision Process (SN-MDP), where there is a noise generating mechanism (either from the environment noise or from the adversary), and the theoretical properties (such as convergence and contraction) for corresponding (expected) Bellman operator and distributional Bellman operator ... | SP:7535989fca66c0c4e072af700c3dbf5c9f2e42f2 |
Exploring the Robustness of Distributional Reinforcement Learning against Noisy State Observations | In real scenarios , state observations that an agent observes may contain measurement errors or adversarial noises , misleading the agent to take suboptimal actions or even collapse while training . In this paper , we study the training robustness of distributional Reinforcement Learning ( RL ) , a class of state-of-th... | This paper studies the robustness of distributional reinforcement learning, in particular the robustness on state observations, which have been demonstrated in a few papers on adversarial attacks to deep reinforcement learning. Compared to existing works on robust reinforcement learning on state observations, the main ... | SP:7535989fca66c0c4e072af700c3dbf5c9f2e42f2 |
On-Target Adaptation | 1 INTRODUCTION . Deep networks achieve tremendous success on various visual tasks at the expense of massive data collection and annotation efforts . Even more data is needed when training ( source ) and testing ( target ) data differ , as the model must be adapted on the new data to maintain accuracy . To reduce the an... | This work addresses the problem of source-free domain adaptation. Instead of fine-tuning *the source model* on the target data, this work proposes to fine-tune *a representation learned on the target data alone* using e.g. self-supervised contrastive learning. This permits distinct target architectures that can improve... | SP:b2cb7590f48bb18dbe0f70b894ef981acea1874b |
On-Target Adaptation | 1 INTRODUCTION . Deep networks achieve tremendous success on various visual tasks at the expense of massive data collection and annotation efforts . Even more data is needed when training ( source ) and testing ( target ) data differ , as the model must be adapted on the new data to maintain accuracy . To reduce the an... | The paper proposes a multi-stage approach for source-free domain adaptation, namely when source data is not available and one can only use the model pre-trained on the source and adapt it based on unlabelled target domain data. The paper advocates for learning on target not via fine-tuning the source model, but rather ... | SP:b2cb7590f48bb18dbe0f70b894ef981acea1874b |
The Effect of diversity in Meta-Learning | 1 INTRODUCTION . It is widely recognized that humans can learn new concepts based on very little supervision , i.e. , with few examples ( or ” shots ” ) , and generalize these concepts to unseen data as mentioned by Lake et al . ( 2011 ) . Recent advances in deep learning , on the other hand , have primarily relied on ... | In this paper, the authors investigate the effect of task diversity in the training process of meta-learning. The findings indicate that increasing task diversity during the meta-training process does not boost performance. They evaluate the performance on four few-shot image classification datasets. | SP:2c485765c1d5d0ada7730ca804d4a298682bf928 |
The Effect of diversity in Meta-Learning | 1 INTRODUCTION . It is widely recognized that humans can learn new concepts based on very little supervision , i.e. , with few examples ( or ” shots ” ) , and generalize these concepts to unseen data as mentioned by Lake et al . ( 2011 ) . Recent advances in deep learning , on the other hand , have primarily relied on ... | The paper studies how the diversity of tasks in the training phase affects the performance of meta-learning algorithms. The paper finds negative evidence, which is consistent with Setlur et al. (2021). Compared with the existing work, the paper performs more extensive experiments with different meta-learning algorithms... | SP:2c485765c1d5d0ada7730ca804d4a298682bf928 |
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) have shown great promise for the generation of photo-realistic synthetic images ( Goodfellow et al. , 2014 ; Radford et al. , 2015 ; Denton et al. , 2015 ; Salimans et al. , 2016 ) , and the highly-compelling nature of images generated by GANs has driven researc... | The paper proposed a new method to tackle text-to-image generation challenge. In the paper, authors introduced a potential problem that current methods only use sentence embedding at beginning of the network to generate initial images, where different attributes may be entangled and are hard to be refined during follow... | SP:0c0df2e874c35381bda95487f3278aea4ae0922f |
Multi-Tailed, Multi-Headed, Spatial Dynamic Memory refined Text-to-Image Synthesis | 1 INTRODUCTION . Generative Adversarial Networks ( GANs ) have shown great promise for the generation of photo-realistic synthetic images ( Goodfellow et al. , 2014 ; Radford et al. , 2015 ; Denton et al. , 2015 ; Salimans et al. , 2016 ) , and the highly-compelling nature of images generated by GANs has driven researc... | This paper is motivated by that most existing text-to-image methods suffer from three limitations and solutions are proposed to address the different limitations. Firstly, it introduces multi-tailed word-level initial generation to enhance global sentence representation with distinct n-gram word presentation. Second... | SP:0c0df2e874c35381bda95487f3278aea4ae0922f |
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness | 1 INTRODUCTION . Combinatorial Optimization covers some of the oldest and most studied computational problems . Well-known examples are the study of the NP-complete SATisfiability problem for boolean statements or the Traveling Salesperson Problem ( TSP ) . Today , these problems can be solved efficiently with approxim... | In this paper, the authors propose to evaluate and improve the robustness and generalization of neural combinatorial solvers with adversarial examples, that is, perturbed inputs that fool the neural network to generate outputs with high loss. The authors claim that their proposal reconciles the tension between the hard... | SP:c0a87b8792e2ec4144737c3b48439aebe4f9f915 |
Generalization of Neural Combinatorial Solvers Through the Lens of Adversarial Robustness | 1 INTRODUCTION . Combinatorial Optimization covers some of the oldest and most studied computational problems . Well-known examples are the study of the NP-complete SATisfiability problem for boolean statements or the Traveling Salesperson Problem ( TSP ) . Today , these problems can be solved efficiently with approxim... | Motivated by the emergence and fast development of the area of neural optimisation solvers having been proposed for a vast number of combinatorial problems, this paper is devoted to studying the adversarial robustness of such solvers. In particular, given the intractability of the target combinatorial problems, the pap... | SP:c0a87b8792e2ec4144737c3b48439aebe4f9f915 |
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods | 1 INTRODUCTION . Climate change is a serious danger to our planet , with warming temperatures causing extreme weather events that affect the livelihood of an increasing number of people globally ( HoeghGuldberg et al. , 2018 ) . In particular , rising sea levels , increasing precipitation and faster snow melt exacerbat... | In this paper, the authors introduce ClimateGAN, a framework for the generation of images of flooded scenarios in order to raise climate change awareness and prompt action. In particular, the authors consider the realistic case of scarcity of training data and propose and unsupervised approach. An evaluation of each Cl... | SP:10884b56b4745dacc61afa80933661fd3786a4cc |
ClimateGAN: Raising Climate Change Awareness by Generating Images of Floods | 1 INTRODUCTION . Climate change is a serious danger to our planet , with warming temperatures causing extreme weather events that affect the livelihood of an increasing number of people globally ( HoeghGuldberg et al. , 2018 ) . In particular , rising sea levels , increasing precipitation and faster snow melt exacerbat... | This paper proposes to generate images of floods, that is, simulate photo-realistic floods, with a model named ClimateGAN, which consists of a Masker to generate masks for floods and a Painter to draw floods on images. The proposed ClimateGAN is a two-stage solution for generating flood masks on input images and painti... | SP:10884b56b4745dacc61afa80933661fd3786a4cc |
Sparse Attention with Learning to Hash | 1 INTRODUCTION . The Transformer architecture ( Vaswani et al. , 2017 ) has been successfully applied to various tasks , including natural language processing ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Dai et al. , 2019 ; Liu et al. , 2019 ; Yang et al. , 2019 ) , computer vision ( Carion et al. , 2020 ; Dosovits... | This paper proposes a learning to hash attention (LHA) to learn sparse attention for Transformer. The proposed LHA addresses the limitation of ANN-based sparse attention method by separate learnable hash functions for queries and keys and utilizes kernelized techniques for efficient approximation of attention utilities... | SP:20b37598155521a49e41e344d8ddf8c8567cde1b |
Sparse Attention with Learning to Hash | 1 INTRODUCTION . The Transformer architecture ( Vaswani et al. , 2017 ) has been successfully applied to various tasks , including natural language processing ( Vaswani et al. , 2017 ; Devlin et al. , 2018 ; Dai et al. , 2019 ; Liu et al. , 2019 ; Yang et al. , 2019 ) , computer vision ( Carion et al. , 2020 ; Dosovits... | This paper introduces a new method based on learnable hash functions to reduce the O(N^2) cost of self-attention in transformers to O(N^1.5). As previously known for bucket-based approaches, this cost is only achieved when buckets are balanced. The paper investigates the effectiveness of related approaches regarding bu... | SP:20b37598155521a49e41e344d8ddf8c8567cde1b |
Evaluating Distributional Distortion in Neural Language Modeling | A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions . Because of this novelty , a heavy-tail of rare events accounts for a significant amount of the total probability mass of distributions in language ( Baayen , 2001 ) . Standard language modeling metrics such a... | The paper conducts experiments to evaluate whether neural sequence models such as LSTMs and Transformers are able to correctly assess the probability of rare sentences, which collectively constitute a large probability mass in natural language productions (heavy-tail phenomenon). In order to do so, it performs experime... | SP:2a936f08edc99fef3191aebe7fce8c3d69dadb63 |
Evaluating Distributional Distortion in Neural Language Modeling | A fundamental characteristic of natural language is the high rate at which speakers produce novel expressions . Because of this novelty , a heavy-tail of rare events accounts for a significant amount of the total probability mass of distributions in language ( Baayen , 2001 ) . Standard language modeling metrics such a... | This paper investigates how language models allocate their probability mass, with an emphasis on rare sequences that are part of the 'heavy tail' of the distribution of natural language sequences. The authors use a language model to define a target distribution with access to samples and ground-truth probabilities. A l... | SP:2a936f08edc99fef3191aebe7fce8c3d69dadb63 |
Structured Pruning Meets Orthogonality | Several recent works empirically found finetuning learning rate is crucial to the final performance in structured neural network pruning . It is shown that the dynamical isometry broken by pruning answers for this phenomenon . How to develop a filter pruning method that maintains or recovers dynamical isometry and is s... | Dynamic isometry is shown to be a useful property that enable effective gradient propagation through the forward/backward. However, pruning will largely damage such a structure. This paper studies how to maintain the “dynamic isometry” property during pruning. Specifically, after getting an initial assessment of filter... | SP:7cee15b5f46a918a6448434840696f3a0d3739ee |
Structured Pruning Meets Orthogonality | Several recent works empirically found finetuning learning rate is crucial to the final performance in structured neural network pruning . It is shown that the dynamical isometry broken by pruning answers for this phenomenon . How to develop a filter pruning method that maintains or recovers dynamical isometry and is s... | This paper proposes regularizing learnable parameters of NNs to maintain the dynamical isometry during pruning and improve their accuracy. In the experimental analyses, the proposed OPP outperforms baseline methods on benchmark datasets. The paper is well written in general, and the proposed methods are intuitive. In ... | SP:7cee15b5f46a918a6448434840696f3a0d3739ee |
Dynamic Parameterized Network for CTR Prediction | 1 INTRODUCTION . Click-through rate ( CTR ) prediction , which aims to estimate the probability of a user clicking an item , is of great importance in recommendation systems and online advertising systems ( Cheng et al. , 2016 ; Guo et al. , 2017 ; Rendle , 2010 ; Zhou et al. , 2018b ) . Effective feature modeling and ... | This submission is on modeling feature interactions for CTR prediction. It proposes a framework that follows meta-learning. Specifically, to model the interaction between feature F1 and feature F2, it uses a meta neural-network g(F1) that takes F1 and produces the parameter for another neural network f(F2) that takes F... | SP:d9337a6584f85065622b88caf23629b10c563ebd |
Dynamic Parameterized Network for CTR Prediction | 1 INTRODUCTION . Click-through rate ( CTR ) prediction , which aims to estimate the probability of a user clicking an item , is of great importance in recommendation systems and online advertising systems ( Cheng et al. , 2016 ; Guo et al. , 2017 ; Rendle , 2010 ; Zhou et al. , 2018b ) . Effective feature modeling and ... | The paper proposed a new method to handle feature/context interactions within the eCTR prediction neural networks. Details are provided in different scenarios. Experiments are conducted for offline and real-world experiments with promising results. | SP:d9337a6584f85065622b88caf23629b10c563ebd |
PROMISSING: Pruning Missing Values in Neural Networks | 1 INTRODUCTION . Missing and incomplete data are abundant in real-world problems ; however , the learning and inference procedures in machine learning ( ML ) models highly rely on high-quality and complete data . Therefore , it is necessary to develop new methods to deal with data imperfections in rugged environments .... | The present paper proposes an alternative to imputation or list-wise deletion in the context of neural networks and incomplete features. Missing values are replaced by a data-specific numerical representation that is learned at the same time as the rest of the network. The handling of the missing values is located in t... | SP:11a1972c3e8ea1c2dda4776b0d751fd47300ae29 |
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