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Predicting Physics in Mesh-reduced Space with Temporal Attention | 1 INTRODUCTION . There has been an increasing interest in many scientific disciplines , from computational fluid dynamics [ 3 , 39 ] over graphics [ 42 , 40 ] to quantum mechanics [ 20 , 1 ] , to accelerate numerical simulation using learned models . In particular , methods based on Graph Neural Networks ( GNN ) have s... | This paper proposes to use auto-regressive sequence models with attention mechanisms to predict the evolution of physical simulations. Mesh-based discretizations are targeted, and hence the employed networks take the form of GNNs. The paper focuses on encode-process-decode structures, and a transformer is proposed as m... | SP:95db394e10cd433a8283269953ca17e8b8f46879 |
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution | Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards . Complex tasks are often hierarchically composed of sub-tasks . Solving a sub-task increases the return expectation and leads to a step in the Q-function . RUDDER identifies these steps and then r... | The paper considers the challenge of improving sample efficiency of RUDDER-style algorithms in sparse MDPs. Building on prior work by Arjona-Medina et al [1], the authors incorporate demonstrations of optimal trajectories from an expert in the training pipeline. Additionally, to improve the sample efficiency and stabil... | SP:7eb0d8278168465270570233e4af64ebb3f2f154 |
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution | Reinforcement Learning algorithms require a large number of samples to solve complex tasks with sparse and delayed rewards . Complex tasks are often hierarchically composed of sub-tasks . Solving a sub-task increases the return expectation and leads to a step in the Q-function . RUDDER identifies these steps and then r... | The paper uses Sequence Alignment technique to redistribute rewards, to a similar effect as with LSTM in RUDDER. The hierarchical agent is trained with behavioral cloning and fine-tuned with RL (tabular in Rooms environment / PPO in MineRL). Tasks are automatically divided into subtasks and specialized agents used for ... | SP:7eb0d8278168465270570233e4af64ebb3f2f154 |
Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings | 1 INTRODUCTION . Natural language processing ( NLP ) is a rapidly advancing field due to the developments of the Transformer model Vaswani et al . ( 2017 ) using a combination of encoder and decoder to process tokenized text with attention . Some popular models use bidirectional embeddings like BERT Devlin et al . ( 20... | ########################################################################## Summary: This works proposes a sentiment classification approach for transformer-based models that employs additional embeddings to represent emotion inputs. These additional emotion inputs are generated using pre-trained transformer in a zer... | SP:eae08fc9f0786b7522db48420aed2b1d02027ad3 |
Improving Sentiment Classification Using 0-Shot Generated Labels for Custom Transformer Embeddings | 1 INTRODUCTION . Natural language processing ( NLP ) is a rapidly advancing field due to the developments of the Transformer model Vaswani et al . ( 2017 ) using a combination of encoder and decoder to process tokenized text with attention . Some popular models use bidirectional embeddings like BERT Devlin et al . ( 20... | This paper proposes a simple way to supplement emotions related information to a downstream classifier. Authors first utilize a pre-trained BART Model to predict probabilities of a pre-defined set of emotion-related words. Then, they prepend the emotion words as text input to the transformer-based classifier. On two pu... | SP:eae08fc9f0786b7522db48420aed2b1d02027ad3 |
The Role of Pretrained Representations for the OOD Generalization of RL Agents | Building sample-efficient agents that generalize out-of-distribution ( OOD ) in realworld settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition . One particularly promising approach is to begin with low-dimensional , pretrained representations of our world , which should f... | This paper is an analysis paper that uses well-known existing pretraining techniques to study the effect of pretrained representations on out-of-distribution generalization performance. The paper mainly uses the VAEs to train the visual representation network. While I believe this is an important topic, the paper only ... | SP:f3373b0ef10578400b8dd9cf54bf71435bd0f726 |
The Role of Pretrained Representations for the OOD Generalization of RL Agents | Building sample-efficient agents that generalize out-of-distribution ( OOD ) in realworld settings remains a fundamental unsolved problem on the path towards achieving higher-level cognition . One particularly promising approach is to begin with low-dimensional , pretrained representations of our world , which should f... | The paper discusses a study of different learned autoencoder based representations in the context of generalisation (in and out of distribution) in reinforcement learning. It focuses on two types of VAE models and a robotic block reaching/pushing setting. Comparisons are performed to separately test OOD data for the re... | SP:f3373b0ef10578400b8dd9cf54bf71435bd0f726 |
Effect of scale on catastrophic forgetting in neural networks | Continual learning is a current challenge in designing machine learning systems : when trained on multiple tasks in sequence , models tend to suffer performance losses on earlier tasks—this is known as catastrophic forgetting ( McCloskey & Cohen , 1989 ) . Catastrophic forgetting occurs in many settings , from supervis... | Following recent works studying the effect of model scaling on tasks such as transfer learning, this paper asks whether scaling up the size of a pretrained network also helps alleviate or minimize catastrophic forgetting. The authors use split CIFAR-10/-100 datasets (early experiments on NLP tasks are also included) an... | SP:15529a4159e8db5b440a0040fd431cedabb12ff4 |
Effect of scale on catastrophic forgetting in neural networks | Continual learning is a current challenge in designing machine learning systems : when trained on multiple tasks in sequence , models tend to suffer performance losses on earlier tasks—this is known as catastrophic forgetting ( McCloskey & Cohen , 1989 ) . Catastrophic forgetting occurs in many settings , from supervis... | The paper explores the effect of scale on catstrophic. They conduct several different experiments (for both ResNets and Vision Transformers) showing: - the size of the model for pretrained models reduces forgetting - (Longer) pretraining reduces forgetting - More pretraining training data size reduces forgetting - A m... | SP:15529a4159e8db5b440a0040fd431cedabb12ff4 |
KINet: Keypoint Interaction Networks for Unsupervised Forward Modeling | 1 INTRODUCTION . Discovering a structured causal representation of the world allows humans to perform a wide repertoire of motor tasks such as interacting with objects . At the core of this process lies the ability to predict the response of the environment to applying an action ( Miall & Wolpert , 1996 ; Wolpert & Kaw... | The paper proposes a keypoint-based forward dynamics model for generalization to unseen number of objects. The model employs unsupervised keypoint extraction methods from the literature, infers probabilistic graph representations over the keypoints, and makes forward predictions by message passing. The model is evaluat... | SP:9163fcb6581b48b4abdbcb046e4b46cb0bb4d0fc |
KINet: Keypoint Interaction Networks for Unsupervised Forward Modeling | 1 INTRODUCTION . Discovering a structured causal representation of the world allows humans to perform a wide repertoire of motor tasks such as interacting with objects . At the core of this process lies the ability to predict the response of the environment to applying an action ( Miall & Wolpert , 1996 ; Wolpert & Kaw... | **The main idea of the paper** This paper combines two powerful ideas, 1. unsupervised keypoint extraction for objects and 2. object-centric representation for forward modeling. KINET first detects objects' key points in the video in an unsupervised manner and extract object-centric representation for future predictio... | SP:9163fcb6581b48b4abdbcb046e4b46cb0bb4d0fc |
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design | 1 INTRODUCTION . Monoclonal antibodies are increasingly adopted as therapeutics targeting a wide range of pathogens such as SARS-CoV-2 ( Pinto et al. , 2020 ) . Since the binding specificity of these Y-shaped proteins is largely determined by their complementarity-determining regions ( CDRs ) , the main goal of computa... | The paper proposed a deep generative model named iterative refinement graph neural network to generate antibody CDR for Y-shaped antibodies. Specifically, it sequentially generates the CDR residue sequence and refines the global structure iteratively. Empirical results show superior performance compared with baselines.... | SP:0337a1d600cc90bd0591d8792102a4486dc03d91 |
Iterative Refinement Graph Neural Network for Antibody Sequence-Structure Co-design | 1 INTRODUCTION . Monoclonal antibodies are increasingly adopted as therapeutics targeting a wide range of pathogens such as SARS-CoV-2 ( Pinto et al. , 2020 ) . Since the binding specificity of these Y-shaped proteins is largely determined by their complementarity-determining regions ( CDRs ) , the main goal of computa... | This paper proposes a joint generative model (co-designing the sequence and the structure at the same time) for the CDRs of antibodies, with the goal to enhance binding specificity or neutralization capabilities. The proposed method and two existing baselines are evaluated on 1) perplexity on hold-out set; 2) perplex... | SP:0337a1d600cc90bd0591d8792102a4486dc03d91 |
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation | 1 INTRODUCTION . Interest in generative models has increased considerably in recent years as the usage of deep learning in industry and academic has exploded . Data generators are useful in scenarios that involve unavailability of any or sufficient amount of real data , restrictions on data usage due to privacy reasons... | The authors present a VAE for multivariate time series generation. The authors add additional decoder building blocks modelling level, trend and seasonality components of time series. Their method, named TimeVAE, is evaluated on four different datasets. Performance measures of generated samples are the similarity to re... | SP:191deadf446a81f8f373cb3766616501d75c0bb9 |
TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation | 1 INTRODUCTION . Interest in generative models has increased considerably in recent years as the usage of deep learning in industry and academic has exploded . Data generators are useful in scenarios that involve unavailability of any or sufficient amount of real data , restrictions on data usage due to privacy reasons... | This work proposes a generative model architecture for time-series that is trained using the variational auto-encoder framework. The main motivation for this model is (a) to obtain a more sample efficient model compared to comparable models based on generative adversarial networks (GANs), and (b) optionally incorproate... | SP:191deadf446a81f8f373cb3766616501d75c0bb9 |
Offline Pre-trained Multi-Agent Decision Transformer | 1 INRTODUCTION . Multi-Agent reinforcement learning ( MARL ) plays an essential role for solving complex decisionmaking tasks by learning from the interaction data between machine or autonomous agents and simulated physical environments . It typically applies to self-driving ( Shalev-Shwartz et al. , 2016 ) , traffic c... | The paper studies the offline training and online fine-tuning setting for MARL, and show that it is promising to learn policy representatrions to improve the performance on downstream MARL tasks. The paper proposes to integrate the transformer architecture to improve efficiency and generalization ability. Authors condu... | SP:ed590dec0a3cd9b7ac1cb1c5407c51509e59a878 |
Offline Pre-trained Multi-Agent Decision Transformer | 1 INRTODUCTION . Multi-Agent reinforcement learning ( MARL ) plays an essential role for solving complex decisionmaking tasks by learning from the interaction data between machine or autonomous agents and simulated physical environments . It typically applies to self-driving ( Shalev-Shwartz et al. , 2016 ) , traffic c... | This is the first paper to pre-train a multi-agent model with offline datasets and online fine-tune it to a particular task. The authors propose MADT an offline pre-training architecture for multi-agent RL integrating transformers with imitation learning on an a collected data set of offline data. Towards this end, the... | SP:ed590dec0a3cd9b7ac1cb1c5407c51509e59a878 |
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning | Designing reward functions for complex , real-world tasks is challenging . Reward learning lets one instead infer reward functions from data . However , multiple reward functions often fit the data equally well , even in the infinite-data limit . Prior work often considers reward functions to be uniquely recoverable , ... | This paper characterizes the partial identifiability of data sources and the reward function. Then it analyzes the impact of this measure on the optimum and the algorithms. Some implications are given. | SP:834027963c400b77f2d47151b621b666cea971a3 |
Invariance in Policy Optimisation and Partial Identifiability in Reward Learning | Designing reward functions for complex , real-world tasks is challenging . Reward learning lets one instead infer reward functions from data . However , multiple reward functions often fit the data equally well , even in the infinite-data limit . Prior work often considers reward functions to be uniquely recoverable , ... | This is a theoretical work on understanding the intrinsic limits of various data sources that are used for reward learning in RL. In particular, by considering the infinite data limit of the data source, they study the level of reward ambiguity that can be obtained for a given downstream task. For example, for the expe... | SP:834027963c400b77f2d47151b621b666cea971a3 |
Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound | 1 INTRODUCTION . In many cases , a practitioner has access to multiple related but distinct datasets such as agricultural measurements from two farms , experimental data collected in different months , or sales data before and after a major event . Unsupervised dataset alignment ( UDA ) is the ML task aimed at aligning... | The paper presents a unified framework for domain alignment through non-adversarial approaches. Instead of solving a min-max adversarial problem, this paper aims to solve a min-min optimization equivalent to minimizing the upper bound of JSD. It proves the equivalence and offers a computational and feasible solution th... | SP:32b0b62a233b816c8405deb383aed5e3a7d1cf0b |
Why be adversarial? Let's cooperate!: Cooperative Dataset Alignment via JSD Upper Bound | 1 INTRODUCTION . In many cases , a practitioner has access to multiple related but distinct datasets such as agricultural measurements from two farms , experimental data collected in different months , or sales data before and after a major event . Unsupervised dataset alignment ( UDA ) is the ML task aimed at aligning... | This paper proposes a flow-based method for the unsupervised data set alignment problem. It firstly reveals that the minimization problem over density models can be addressed by optimizing the upper bound of generalized JSD. Based on this theoretical result, the authors propose a new regularizer for the multi-distribut... | SP:32b0b62a233b816c8405deb383aed5e3a7d1cf0b |
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop | 1 INTRODUCTION . Geophysical properties ( such as velocity , impedance , and density ) play an important role in various subsurface applications including subsurface energy exploration , carbon capture and sequestration , estimating pathways of subsurface contaminant transport , and earthquake early warning systems to ... | The paper presents a method for full-waveform inversion (an inverse problem in seismic imaging) that combines a convolutional neural network (CNN) with a physics-based forward modeling operator. As opposed to other work which directly learns the inverse mapping from measurements to the sought-after velocity parameter, ... | SP:4d58f330415e44e06446eaccd907b9fa0861f40e |
Unsupervised Learning of Full-Waveform Inversion: Connecting CNN and Partial Differential Equation in a Loop | 1 INTRODUCTION . Geophysical properties ( such as velocity , impedance , and density ) play an important role in various subsurface applications including subsurface energy exploration , carbon capture and sequestration , estimating pathways of subsurface contaminant transport , and earthquake early warning systems to ... | The paper introduces a novel way to solve Full-Waveform Inversion problem which is a common problem in geological surveys. Their method is based on CNN, giving a reconstruction of velocity field for measured seismic data, and the loss function which connects CNN to a discretized version of the governing partial differe... | SP:4d58f330415e44e06446eaccd907b9fa0861f40e |
Decoupled Adaptation for Cross-Domain Object Detection | 1 INTRODUCTION . The object detection task has aroused great interest due to its wide applications . In the past few years , the development of deep neural networks has boosted the performance of object detectors [ 33 ; 15 ; 41 ] . While these detectors have achieved excellent performance on the benchmark datasets [ 11... | Paper addresses the problem of cross-domain object detection. The formed source domain detectors (in the form of Faster R-CNN), learned in a supervised manner, are adopted to perform well on the target domain where no annotations are available. The key to the approach is separate adaptation of the classification head a... | SP:9766ea2942d933a8bcf240338cbadedf9d4aa897 |
Decoupled Adaptation for Cross-Domain Object Detection | 1 INTRODUCTION . The object detection task has aroused great interest due to its wide applications . In the past few years , the development of deep neural networks has boosted the performance of object detectors [ 33 ; 15 ; 41 ] . While these detectors have achieved excellent performance on the benchmark datasets [ 11... | The authors propose a tailored method, named Decoupled Adaptation (D-adapt), for cross-domain object detection. The conventional domain adaptation techniques hurt the discriminability of the detector and ignores the adaptation on bounding box regression. In order to avoid hurting the discriminability of the detector, t... | SP:9766ea2942d933a8bcf240338cbadedf9d4aa897 |
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining | Deep Generative Networks ( DGNs ) are extensively employed in Generative Adversarial Networks ( GANs ) , Variational Autoencoders ( VAEs ) , and their variants to approximate the data manifold and distribution . However , training samples are often distributed in a non-uniform fashion on the manifold , due to costs or ... | This paper concerns with uniform sampling from deep generative networks such as GANs and VAEs. The training samples of DGNs are often biased as they are obatined based on preferences, costs, or convenience that leads to DGNs producing biased examples. This paper gives a gemoetry based sampler MaGNET, that given any tra... | SP:cddf3d13882b412a10ed5981101b1088f184c647 |
MaGNET: Uniform Sampling from Deep Generative Network Manifolds Without Retraining | Deep Generative Networks ( DGNs ) are extensively employed in Generative Adversarial Networks ( GANs ) , Variational Autoencoders ( VAEs ) , and their variants to approximate the data manifold and distribution . However , training samples are often distributed in a non-uniform fashion on the manifold , due to costs or ... | The authors propose a uniform sampling technique for deep generative networks (DGNs) inspired by the probabilistic change of variables formula. The technique works with any already trained DGN and does not involve any further training. (Though it does require back propagation w.r.t. the input $x$.) In essence, the algo... | SP:cddf3d13882b412a10ed5981101b1088f184c647 |
Distributionally Robust Learning for Uncertainty Calibration under Domain Shift | We propose a framework for learning calibrated uncertainties under domain shifts . We consider the case where the source ( training ) distribution differs from the target ( test ) distribution . We detect such domain shifts through the use of binary domain classifier and integrate it with the task network and train the... | This paper focuses on the problem of uncertainty calibration under distribution shift. By using a domain classifier in the distributional robust learning (DRL) framework, the authors estimate the density ratio between the source and target domain to achieve well-calibrated predictions under domain shift. A regularized ... | SP:8fc97dbc3d6e099b49cb402a6057a41dbe72ac9f |
Distributionally Robust Learning for Uncertainty Calibration under Domain Shift | We propose a framework for learning calibrated uncertainties under domain shifts . We consider the case where the source ( training ) distribution differs from the target ( test ) distribution . We detect such domain shifts through the use of binary domain classifier and integrate it with the task network and train the... | This paper proposes scaled uncertainty prediction in the context of unsupervised domain adaptation. The problem in unsupervised domain adaptation is to obtain predictions in a target domain where no (or few) labels are available. A secondary problem then is the calibration of predictive uncertainties in the target doma... | SP:8fc97dbc3d6e099b49cb402a6057a41dbe72ac9f |
Pretrained Language Models are Symbolic Mathematics Solvers too! | 1 Introduction . Deep learning is a ubiquitous choice in solving statistical pattern recognition problems of regression and classification . With a large training data set and compute power , they have proven to be very effective and achieve state-of-the-art performance in a wide range of tasks in natural language proc... | This work investigate the problem of whether pretraining on language task such as machine translation could help with solving symbolic mathematics problems. Specifically they focused on solving symbolic integration and differential equations using the dataset similar to [Lample & Charleston, 2019]. The authors argued t... | SP:852ee74381bfef42fbef5d0e9c474788793d384b |
Pretrained Language Models are Symbolic Mathematics Solvers too! | 1 Introduction . Deep learning is a ubiquitous choice in solving statistical pattern recognition problems of regression and classification . With a large training data set and compute power , they have proven to be very effective and achieve state-of-the-art performance in a wide range of tasks in natural language proc... | The authors use transformers pre-trained on machine translation tasks on the integration and differential equation datasets proposed by Lample and Charton (Deep Learning for Symbolic Mathematics, ICLR 2020). They show that pre-training on "pure language" tasks help models, in terms of learning speed, accuracy, and, to... | SP:852ee74381bfef42fbef5d0e9c474788793d384b |
Tabular Data Imputation: Choose KNN over Deep Learning | 1 INTRODUCTION . Missing values constitute a serious issue in data science . Incomplete datasets result from uncollected , lost or voluntarily deleted observations . Most machine learning algorithms can not handle datasets with missing values , and meticulous data preprocessing is therefore needed . The most common pre... | This paper considers the problem of tabular data imputation to handle missing numeric data. It compares the RMSE of recently proposed GAN-based methods (GAIN and mis-GAN) with the classical KNN method (either through uniform or distance-weighted imputation). It considers 9 datasets, two are simulated by a mixture of Ga... | SP:e547064b6600f601b2ea5bfe9c3f67d119d2c8c9 |
Tabular Data Imputation: Choose KNN over Deep Learning | 1 INTRODUCTION . Missing values constitute a serious issue in data science . Incomplete datasets result from uncollected , lost or voluntarily deleted observations . Most machine learning algorithms can not handle datasets with missing values , and meticulous data preprocessing is therefore needed . The most common pre... | This manuscript presents a nice empirical comparison between the classic KNN imputation with two state-of-the-art GAN-based imputations for tabular data imputation. The experiments are performed on both simulated and publicly available real data. The results, overall, show that the KNN, despite its simplicity, provides... | SP:e547064b6600f601b2ea5bfe9c3f67d119d2c8c9 |
Pixab-CAM: Attend Pixel, not Channel | 1 INTRODUCTION . As the performance of convolution neural networks ( CNNs ) converges to some extent ( Szegedy et al. , 2015 ; Simonyan & Zisserman , 2014 ; Huang et al. , 2017 ; Howard et al. , 2017 ; Chollet , 2017 ) , researchers have become interested in interpreting their predictions ( Springenberg et al. , 2014 ;... | This paper proposes a method for generating an explanation map (Pixab-CAM) showing which part the CNN model for classification refers to in making a decision. Pixab-CAM utilizes how the classification results are changed by focusing or removing a specific region of the convolutional feature map. By using this, the auth... | SP:9371386507f6840c71853847faddb3075eecbca8 |
Pixab-CAM: Attend Pixel, not Channel | 1 INTRODUCTION . As the performance of convolution neural networks ( CNNs ) converges to some extent ( Szegedy et al. , 2015 ; Simonyan & Zisserman , 2014 ; Huang et al. , 2017 ; Howard et al. , 2017 ; Chollet , 2017 ) , researchers have become interested in interpreting their predictions ( Springenberg et al. , 2014 ;... | This paper aims to solve two common limitations in CAM-based methods, and proposes a new type of CAM (Pixab-CAM) that utilize pixel-wise weights instead of channel-wise weight. Meanwhile, the author also proposes to use adversarial attack as a novel evaluation metric. This work demonstrates to be superior to previous C... | SP:9371386507f6840c71853847faddb3075eecbca8 |
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities | 1 INTRODUCTION . Designing effective reinforcement learning algorithms to learn control from high-dimensional visual observations is crucial and has attracted more attentions in the reinforcement learning ( RL ) community ( Yarats et al. , 2021 ; Laskin et al. , 2020a ; Schwarzer et al. , 2021 ; Lesort et al. , 2018 ) ... | This work considers the problem of learning representations of high-dimensional pixel observations for RL. High-dimensional pixel observations often include many task-irrelevant details and ideally, an effective representation of such observations should only encode the task-relevant details. To implement this intuitio... | SP:794bafd503d3cee3060a5c8ba489ca141d0d3e5e |
Learning Generalizable Representations for Reinforcement Learning via Adaptive Meta-learner of Behavioral Similarities | 1 INTRODUCTION . Designing effective reinforcement learning algorithms to learn control from high-dimensional visual observations is crucial and has attracted more attentions in the reinforcement learning ( RL ) community ( Yarats et al. , 2021 ; Laskin et al. , 2020a ; Schwarzer et al. , 2021 ; Lesort et al. , 2018 ) ... | The paper studies the problem of learning invariant visual representations for reinforcement learning. Specifically, it builds on prior work Deep Bisimulation for Control (DBC) (Zhang et al), which learns an image representation predictive of rewards and dynamics and uses it with SAC. This paper makes a few modificatio... | SP:794bafd503d3cee3060a5c8ba489ca141d0d3e5e |
Beyond Prioritized Replay: Sampling States in Model-Based Reinforcement Learning via Simulated Priorities | Prioritized Experience Replay ( ER ) has been empirically shown to improve sample efficiency across many domains and attracted great attention ; however , there is little theoretical understanding of why such prioritized sampling helps and its limitations . In this work , we take a deep look at the prioritized ER . In ... | This paper takes a deep look at prioritized experience replay, a popular technique in deep reinforcement learning. The paper gives insights on why error-based prioritized experience replay can help when the importance ratio is unused. This paper also pointed out two limitations of prioritized experience replay, which a... | SP:76dd5e85169aea4045abea494d5e10909c5fee1f |
Beyond Prioritized Replay: Sampling States in Model-Based Reinforcement Learning via Simulated Priorities | Prioritized Experience Replay ( ER ) has been empirically shown to improve sample efficiency across many domains and attracted great attention ; however , there is little theoretical understanding of why such prioritized sampling helps and its limitations . In this work , we take a deep look at the prioritized ER . In ... | This paper proposes an alternative method for performing Prioritized Experience Replay (PER), which avoids issues of inadequate state coverage and staleness in priority scores. Their method is based on a result from stochastic langevin dynamics, which shows their specific stochastic gradient Langevin dynamics (SGLD) up... | SP:76dd5e85169aea4045abea494d5e10909c5fee1f |
How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem | Reinforcement learning is generally difficult for partially observable Markov decision processes ( POMDPs ) , which occurs when the agent ’ s observation is partial or noisy . To seek good performance in POMDPs , one strategy is to endow the agent with a finite memory , whose update is governed by the policy . However ... | The paper attempts to investigate how memory architecture affects learning performance of POMDP agents. It focuses on a very simple two-arm bandit problem with two hypotheses for their probabilities. Two memory structures are considered: random access memory and memento memory. For each memory structure, one policy is ... | SP:82b3049ba37482bbbd54b7e88daf183a5a06962e |
How memory architecture affects learning in a simple POMDP: the two-hypothesis testing problem | Reinforcement learning is generally difficult for partially observable Markov decision processes ( POMDPs ) , which occurs when the agent ’ s observation is partial or noisy . To seek good performance in POMDPs , one strategy is to endow the agent with a finite memory , whose update is governed by the policy . However ... | This paper tackles a two-armed bandit problem (of means $Ber(1/2+\mu)$ and $Ber(1/2-\mu)$, respectively) when memory is limited. Specifically, they model this problem as a POMDP (or, alternatively, hypothesis testing), where the hidden state determines the mean of each arm. The author claim that this simple model might... | SP:82b3049ba37482bbbd54b7e88daf183a5a06962e |
L0-Sparse Canonical Correlation Analysis | 1 INTRODUCTION . Canonical Correlation Analysis ( CCA ) ( Hotelling , 1936 ; Thompson , 2005 ) , is a classic statistical method for finding the maximally correlated linear transformations of two modalities ( or views ) . Using modalitiesX ∈ RDx×N andY ∈ RDy×N , which are centered and haveN samples withDx and Dy featur... | The paper proposes a sparse canonical correlation analysis method based on l_0 norm. A continuous relaxation scheme is adopted for solving sparse CCA. The proposed model is then extended to nonlinear function estimation and combined with deep neural networks. Experimental results on synthetic and real-world datasets de... | SP:74e6032281e4470acb02303c8c6a319cfb896f28 |
L0-Sparse Canonical Correlation Analysis | 1 INTRODUCTION . Canonical Correlation Analysis ( CCA ) ( Hotelling , 1936 ; Thompson , 2005 ) , is a classic statistical method for finding the maximally correlated linear transformations of two modalities ( or views ) . Using modalitiesX ∈ RDx×N andY ∈ RDy×N , which are centered and haveN samples withDx and Dy featur... | The paper proposes a new method for l0-CCA using stochastic gating which allows for an efficient algorithm and also permits a deep-version of the l0-CCA. Results are shown on synthetic and real-world datasets that highlight the superior performance of the proposed approach. Main Contributions: 1). The paper propose... | SP:74e6032281e4470acb02303c8c6a319cfb896f28 |
An Interpretable Graph Generative Model with Heterophily | Many models for graphs fall under the framework of edge-independent dot product models . These models output the probabilities of edges existing between all pairs of nodes , and the probability of a link between two nodes increases with the dot product of vectors associated with the nodes . Recent work has shown that t... | The paper proposes an edge-independent graph generative model. Besides the model, the paper also proposes the training process of the model and some theoretical contributions. Unfortunately, the evaluation of the model is based on another problem, clustering. This makes the paper complicate to evaluate. While the main ... | SP:e617ea828dc7662fa494799aac7136580ca492f0 |
An Interpretable Graph Generative Model with Heterophily | Many models for graphs fall under the framework of edge-independent dot product models . These models output the probabilities of edges existing between all pairs of nodes , and the probability of a link between two nodes increases with the dot product of vectors associated with the nodes . Recent work has shown that t... | This article deals with the task of community detection in the case of mixed membership networks, where a node can have multiple comunity assignments at once. It has been notice that in real-world networks, community overlaps tend to be much denser than the rest of the graph, which is a challenge to usual models (such ... | SP:e617ea828dc7662fa494799aac7136580ca492f0 |
Robust Deep Neural Networks for Heterogeneous Tabular Data | 1 INTRODUCTION . Tabular data is the most commonly used form of data , and it is ubiquitous in various applications , such as medical diagnosis based on patient history ( Fatima et al. , 2017 ) , predictive analytics for financial applications ( Dastile et al. , 2020 ) , cybersecurity ( Buczak & Guven , 2015 ) , and so... | This paper proposes DeepTLF, a new framework for prediction tasks using tabular data. DeepTLF first trains gradient boosted decision trees (GBDT) using the entire tabular training samples. Then it uses node values as the input the neural network predictor (e.g. classifier or regressor) for the actual prediction phase. ... | SP:0e00204408c0655e6f5a5c5823a0df36fe72b871 |
Robust Deep Neural Networks for Heterogeneous Tabular Data | 1 INTRODUCTION . Tabular data is the most commonly used form of data , and it is ubiquitous in various applications , such as medical diagnosis based on patient history ( Fatima et al. , 2017 ) , predictive analytics for financial applications ( Dastile et al. , 2020 ) , cybersecurity ( Buczak & Guven , 2015 ) , and so... | For the problem of learning (supervised classification and regression) on tabular data, the authors propose to used the decision functions of tree based ensemble methods as input features for a deep neural network (DNN). Tabular data is typically heterogenous, that is the data come from different modalities (continuous... | SP:0e00204408c0655e6f5a5c5823a0df36fe72b871 |
Training Meta-Surrogate Model for Transferable Adversarial Attack | 1 INTRODUCTION . The developments of Convolutional Neural Network ( CNN ) ( LeCun et al. , 1995 ; Krizhevsky et al. , 2012 ) have greatly promoted the advancements in Computer Vision ( Ren et al. , 2016 ) . However , previous works ( Goodfellow et al. , 2014 ; Carlini & Wagner , 2017 ; Croce & Hein , 2020a ; Ganeshan e... | A complete black box adversarial attack thus far has not performed well as research shows. By complete black box, it means there is no information available about the target model, not even query based probing. Most SOTA uses a surrogate model to generate adversarial examples, where the surrogate model may, every likel... | SP:ba46a3b9b5fe435a365a863ccc87cf07bf16cd93 |
Training Meta-Surrogate Model for Transferable Adversarial Attack | 1 INTRODUCTION . The developments of Convolutional Neural Network ( CNN ) ( LeCun et al. , 1995 ; Krizhevsky et al. , 2012 ) have greatly promoted the advancements in Computer Vision ( Ren et al. , 2016 ) . However , previous works ( Goodfellow et al. , 2014 ; Carlini & Wagner , 2017 ; Croce & Hein , 2020a ; Ganeshan e... | In this paper, the authors proposed a novel method of Meta-Surrogate Model (MSM) which can generate more transferable adversarial examples in the black-box adversarial attacks. The problem is formulated as a bi-level-like optimization problem which achieved via a differentiable attacker. Extensive experiments demonstra... | SP:ba46a3b9b5fe435a365a863ccc87cf07bf16cd93 |
Memory-Constrained Policy Optimization | 1 INTRODUCTION . Reinforcement learning ( RL ) combined with neural networks is the current workhorse in machine learning . Using neural networks to approximate value and policy functions enables classical approaches such as Q-learning and policy gradient to achieve promising results on many challenging problems such a... | This paper proposes a new approach to regularize on-policy methods utilizing two soft trust region terms. The first term encourages the new policy to stay close to old policy like PPO and the second term enforces the new policy to stay close to the combination of past policies called virtual policy. The virtual policy ... | SP:8d7ad283640648010986d77969506a28725b2e2a |
Memory-Constrained Policy Optimization | 1 INTRODUCTION . Reinforcement learning ( RL ) combined with neural networks is the current workhorse in machine learning . Using neural networks to approximate value and policy functions enables classical approaches such as Q-learning and policy gradient to achieve promising results on many challenging problems such a... | In this work, the authors propose the use a trust-region method in the vein of Proximal Policy Optimization (PPO). In contrast to previous work, the author propose to use two trust regions instead of a single one. Instead of constraining the policy to stay near to the previous policy, the author's proposed algorithm in... | SP:8d7ad283640648010986d77969506a28725b2e2a |
Triangular Dropout: Variable Network Width without Retraining | One of the most fundamental design choices in neural networks is layer width : it affects the capacity of what a network can learn and determines the complexity of the solution . This latter property is often exploited when introducing information bottlenecks , forcing a network to learn compressed representations . Ho... | This paper proposes Triangular Dropout, a mechanism for training fully-connected layers whose width can be decreased at test time, by using dropout masks with a particular structure at train time. The proposed approach compares favorably to previous methods when training autoencoders of varying bottleneck size on MNIST... | SP:ce7d5818ec4598134e27b2f5bc346686d95f8f70 |
Triangular Dropout: Variable Network Width without Retraining | One of the most fundamental design choices in neural networks is layer width : it affects the capacity of what a network can learn and determines the complexity of the solution . This latter property is often exploited when introducing information bottlenecks , forcing a network to learn compressed representations . Ho... | This paper proposes a method, called Triangular Dropout, to allow a fully connected layer in a network to have variable width at deployment, which is achieved by applying a lower-triangular mask to the output of the fully-connected layer. Experiments were conducted in three different scenarios, autoencoder, image class... | SP:ce7d5818ec4598134e27b2f5bc346686d95f8f70 |
Universalizing Weak Supervision | 1 INTRODUCTION . Weak supervision ( WS ) frameworks help overcome the labeling bottleneck : the challenge of building a large dataset for use in training data-hungry deep models . WS approaches replace hand-labeling with synthesizing multiple noisy but cheap sources , called labeling functions , applied to unlabeled da... | This work studies a weakly supervised learning setup of aggregating multiple weak sources of labels into high-quality pseudo labels for learning. The proposed approach is based on building graphical models with the true labels as latent variables following existing techniques, but generalizes existing works in 1) sup... | SP:a6788a6cbd301cb34e8b8ed0fa34167c777d50ae |
Universalizing Weak Supervision | 1 INTRODUCTION . Weak supervision ( WS ) frameworks help overcome the labeling bottleneck : the challenge of building a large dataset for use in training data-hungry deep models . WS approaches replace hand-labeling with synthesizing multiple noisy but cheap sources , called labeling functions , applied to unlabeled da... | The paper proposes a framework for weak supervision which works for both discrete and continuous labels. The proposed framework assumes the sources are coming from an exponential family distribution and generalizes the accuracy and correlation terms in previous approaches. They use a discriminative learning setting and... | SP:a6788a6cbd301cb34e8b8ed0fa34167c777d50ae |
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders | 1 INTRODUCTION AND MOTIVATION . Many real-world applications of data-driven classifiers , e.g. , neural networks , involve corruptions that pose significant challenges to the pretrained classifiers . Often , the corruption must previously be included , and thus already be known , during the process of training . For in... | This paper proposes a false classification detection method. The proposed method first trains an autoencoder using uncorrupted data to obtain a decoder to define a generative model. The generative model is then exploited for quantifying the uncertainty of the model. Using MGVI, the generative model infers the posterior... | SP:0c654d1ec408359642e987d4e84a445fbd2d41fb |
Classification and Uncertainty Quantification of Corrupted Data using Semi-Supervised Autoencoders | 1 INTRODUCTION AND MOTIVATION . Many real-world applications of data-driven classifiers , e.g. , neural networks , involve corruptions that pose significant challenges to the pretrained classifiers . Often , the corruption must previously be included , and thus already be known , during the process of training . For in... | This paper presents a method that simultaneously classifies corrupted data and quantifies uncertainty, despite the model being fitted only on uncorrupted data. The idea is to fit a semi-supervised autoencoder. Then, the encoded representation $h$ is fed into a decoder $g$, and the output $g(h)$ is augmented with variou... | SP:0c654d1ec408359642e987d4e84a445fbd2d41fb |
VISCOS Flows: Variational Schur Conditional Sampling with Normalizing Flows | 1 INTRODUCTION . Conditional data generation is a ubiquitous and challenging problem , even more so when the data is high dimensional . If the partitioning of the conditioned data and conditioning itself can be established in advance , a wide variety of inference tools can be used ranging from Bayesian inference ( Gelm... | This paper presents a variant of flow, VISCOS flow, utilizing the Schur complement. This flow model is applied to the in-painting task, which estimates the unobserved feature variables given the observed features. Given this in-painting task, the inference requires the modeling on the latent variable, which results in ... | SP:3759750e13689e4641744d077e21647795b03765 |
VISCOS Flows: Variational Schur Conditional Sampling with Normalizing Flows | 1 INTRODUCTION . Conditional data generation is a ubiquitous and challenging problem , even more so when the data is high dimensional . If the partitioning of the conditioned data and conditioning itself can be established in advance , a wide variety of inference tools can be used ranging from Bayesian inference ( Gelm... | This paper proposes a technique for performing data imputation using pre-trained normalizing flows. They do so by fitting a variational distribution over a subset of the 'base' variables of the normalizing flow. Along with the observations, a sample from this variational distribution is sufficient to fully specify a sa... | SP:3759750e13689e4641744d077e21647795b03765 |
Plant 'n' Seek: Can You Find the Winning Ticket? | 1 INTRODUCTION . Deep learning has achieved breakthroughs in multiple challenging areas pertaining to machine learning , in particular in areas for which we lack competitive hand-crafted algorithms . The benefits of overparameterization for training with SGD ( Belkin et al. , 2019 ) seem to call for ever wider and deep... | The authors note a distinction between kinds of sparse networks in the literature. "Weak tickets" require training to perform comparably to the original network, while "strong tickets" do not. The authors prove a lower bound for the probability that a strong ticket exists, and note that instead of training, this "stro... | SP:d8fb4eefbb282d80e357ec143ea0697334b55ab2 |
Plant 'n' Seek: Can You Find the Winning Ticket? | 1 INTRODUCTION . Deep learning has achieved breakthroughs in multiple challenging areas pertaining to machine learning , in particular in areas for which we lack competitive hand-crafted algorithms . The benefits of overparameterization for training with SGD ( Belkin et al. , 2019 ) seem to call for ever wider and deep... | The authors identify that contemporary methods for finding very sparse subnetworks in deep neural networks (DNNs), including both methods for finding either "weak tickets" (after training) or "strong tickets" (before training), do not find very sparse (and good) solutions. They question if this is a fundamental limitat... | SP:d8fb4eefbb282d80e357ec143ea0697334b55ab2 |
Lifting Imbalanced Regression with Self-Supervised Learning | 1 INTRODUCTION . Regression is a fundamental task in machine learning and statistical analysis , which involves modeling the relationship , between one or more independent and dependent variables . As distinct from the discrete output values in classification , regression makes predictions for continuous values . The i... | This paper researched on the recently proposed long-tailed regression problem with self-supervised learning method. Two questions are investigated by this paper: 1) how to measure the similarity and dissimilarity under the regression sense; 2) it is not guaranteed that the sampled with perturbations are similar to the ... | SP:f3cd4edcd106270bf2b809a5adc3dc8fc86f6f5b |
Lifting Imbalanced Regression with Self-Supervised Learning | 1 INTRODUCTION . Regression is a fundamental task in machine learning and statistical analysis , which involves modeling the relationship , between one or more independent and dependent variables . As distinct from the discrete output values in classification , regression makes predictions for continuous values . The i... | This paper proposes a novel algorithm SSIR to address the imbalanced problem in regression tasks, which seamlessly combines self-supervised learning and imbalanced regression by giving the formal definition of similarity in the regression task. Besides, the authors specifically propose to limit the volume of noise on t... | SP:f3cd4edcd106270bf2b809a5adc3dc8fc86f6f5b |
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning | Posterior Sampling for Reinforcement Learning ( PSRL ) is a well-known algorithm that augments model-based reinforcement learning ( MBRL ) algorithms with Thompson sampling . PSRL maintains posterior distributions of the environment transition dynamics and the reward function to procure posterior samples that are used ... | This paper improves a model-based reinforcement learning method by Kaiser et al. (2020) by introducing three different types of neural network layers to the transition and reward model. These modifications are motivated by intuitions about what would be beneficial to model for object-based environments (in Atari games)... | SP:fe963687c10f65d3f824a56f900de29ccabccb43 |
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning | Posterior Sampling for Reinforcement Learning ( PSRL ) is a well-known algorithm that augments model-based reinforcement learning ( MBRL ) algorithms with Thompson sampling . PSRL maintains posterior distributions of the environment transition dynamics and the reward function to procure posterior samples that are used ... | This paper proposes an approach for Model-based reinforcement learning relying on Posterior Sampling for Reinforcement learning. Posterior sampling for reinforcement learning uses Thompson sampling to balance exploitation and exploration. However this requires maintaining posterior of dynamic transitions which is often... | SP:fe963687c10f65d3f824a56f900de29ccabccb43 |
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts | 1 INTRODUCTION . Machine learning ( ML ) prediction APIs have made it dramatically easier to build ML applications . For example , one can use Microsoft text API ( Mic , a ) to determine the polarity of a text review written by a customer , or Google speech API ( Goo , b ) to recognize users ’ spoken commands received ... | This paper considers the problem of estimating the change in the performance of commercial ML APIs (ML as a service) as the models are updated over time (experiments are for 2020 vs 2021). It formalizes the problem as estimating the change in the confusion matrix over time. The main theoretical contribution is an adapt... | SP:be306853f891ecd112e8ec21ec332af48be9b3ec |
How Did the Model Change? Efficiently Assessing Machine Learning API Shifts | 1 INTRODUCTION . Machine learning ( ML ) prediction APIs have made it dramatically easier to build ML applications . For example , one can use Microsoft text API ( Mic , a ) to determine the polarity of a text review written by a customer , or Google speech API ( Goo , b ) to recognize users ’ spoken commands received ... | Authors show that ML models behind publicly available APIs change and these changes cause result changes for input datasets. Authors track the changes through confusion matrix differences. They propose an efficient algorithm they call MASA to evaluate the changes in results with reduced number of queries. Their algorit... | SP:be306853f891ecd112e8ec21ec332af48be9b3ec |
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks | 1 INTRODUCTION . In recent years , GCNs ( Defferrard et al. , 2016 ; Kipf & Welling , 2016 ; Veličković et al. , 2017 ) have been widely used in applications ranging from social science ( Li & Goldwasser , 2019 ) and biology ( Yan et al. , 2019 ) to program understanding ( Allamanis et al. , 2018 ; Shi et al. , 2019 ... | Two issues confronting GCNs, heterophily and over-smoothing, share a common cause. This article asserts that the relative degree and homophily level account for node representation movement and further affect the likelihood of misclassification. The contributions of this paper are as follows: 1) it attempts to estab... | SP:9631ec37258a40acaa6666ca4b66158d6fd9c3dc |
Two Sides of the Same Coin: Heterophily and Oversmoothing in Graph Convolutional Neural Networks | 1 INTRODUCTION . In recent years , GCNs ( Defferrard et al. , 2016 ; Kipf & Welling , 2016 ; Veličković et al. , 2017 ) have been widely used in applications ranging from social science ( Li & Goldwasser , 2019 ) and biology ( Yan et al. , 2019 ) to program understanding ( Allamanis et al. , 2018 ; Shi et al. , 2019 ... | The paper studies two problems plaguing graph neural networks (GCNs/GNNs): 1) oversmoothing of GCNs/GNNs; and 2) GCNs/GNNs often do not yield good performance on heterophilous networks. The authors aim to develop a theory to explain these issues simultaneously, where a few lemmas and theorems were developed based on ce... | SP:9631ec37258a40acaa6666ca4b66158d6fd9c3dc |
Federated Learning via Plurality Vote | 1 INTRODUCTION . Federated learning enables multiple workers to solve a machine learning problem under the coordination of a central server ( Kairouz et al. , 2021 ) . Throughout the training stage , client data will be kept locally and only model weights or model updates will be shared with the server . Federated aver... | The paper presents a scheme for federated learning (FL) called FedVote. In FedVote, clients use binary neural networks where a latent restricted range weight vector $\boldsymbol{h}$ is learned, and stochastic rounding is applied to obtain quantized weights $\in$ {$-1,+1$}. The communication cost to the server (up-link)... | SP:6a92c5ac2c70f85a65c73a703d863dc1564ab549 |
Federated Learning via Plurality Vote | 1 INTRODUCTION . Federated learning enables multiple workers to solve a machine learning problem under the coordination of a central server ( Kairouz et al. , 2021 ) . Throughout the training stage , client data will be kept locally and only model weights or model updates will be shared with the server . Federated aver... | This paper aims to reduce the communication cost for federated learning while ensuring that the aggregation method at the server is tolerant to the presence of Byzantine clients. Towards this instead of communicating the local update/gradients or their quantized version, this paper proposes to communicate the quantized... | SP:6a92c5ac2c70f85a65c73a703d863dc1564ab549 |
Provable Federated Adversarial Learning via Min-max Optimization | 1 INTRODUCTION . Federated learning ( FL ) is playing an important role nowadays , as it allows different parties or clients to collaboratively train deep learning models without sharing private data . One popular FL paradigm called FedAvg ( McMahan et al. , 2017 ) introduces an easy-to-implement distributed learning m... | This paper is a direct follow-up of Zhang et al 2020b. With assumptions including overparametrized two-layer ReLU network, normalized dataset, gamma-separability, and Lipschitz convex loss, it proves the convergence of FedAvg under adversarial perturbation. These assumptions are easy adaptations from Zhang et al 2020b.... | SP:59629689ec4e6ffa51a30e0a931f103caadd81b8 |
Provable Federated Adversarial Learning via Min-max Optimization | 1 INTRODUCTION . Federated learning ( FL ) is playing an important role nowadays , as it allows different parties or clients to collaboratively train deep learning models without sharing private data . One popular FL paradigm called FedAvg ( McMahan et al. , 2017 ) introduces an easy-to-implement distributed learning m... | This paper proposes a federated adversarial learning (FAL) framework with strong theoretical guarantees. Compared to the centralized model, the federated model allows each local client to generate the adversarial samples and updates the gradient themselves for several iterations and then communicate to the centralized ... | SP:59629689ec4e6ffa51a30e0a931f103caadd81b8 |
RAVE: A variational autoencoder for fast and high-quality neural audio synthesis | Deep generative models applied to audio have improved by a large margin the state-of-the-art in many speech and music related tasks . However , as raw waveform modelling remains an inherently difficult task , audio generative models are either computationally intensive , rely on low sampling rates , are complicated to ... | This paper proposes an autoencoder for high-quality waveform synthesis. The approach is based on multi-band decomposition, VAE and adversarial fine-tuning. The synthesis speed is substantially faster than existing approaches on both CPU and GPU. | SP:31e7f8d332aaeed8669c0a98d3e43edf1e6271bc |
RAVE: A variational autoencoder for fast and high-quality neural audio synthesis | Deep generative models applied to audio have improved by a large margin the state-of-the-art in many speech and music related tasks . However , as raw waveform modelling remains an inherently difficult task , audio generative models are either computationally intensive , rely on low sampling rates , are complicated to ... | This paper aims to achieve fast and high-quality audio synthesis. The authors propose a variational autoencoder-based model called real-time audio variational autoencoder (RAVE) to achieve this aim. In particular, the authors introduce a two-stage training scheme to alleviate the difficulty in joint learning of the ade... | SP:31e7f8d332aaeed8669c0a98d3e43edf1e6271bc |
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective? | Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks . The most significant advantage of using synthetic images is that the ground-truth labels are automatically available , enabling unlimited expansion... | This paper proposes a scaling law for transfer learning that takes into account the size of both the pretraining and finetuning datasets. They showed the parameters of the scaling law can be estimated and this can be used to approximate the performance of the model given the amount of pretraining data. The authors ex... | SP:5bdbbd811a7a85540ccf3d99348aa96702191849 |
A Scaling Law for Syn-to-Real Transfer: How Much Is Your Pre-training Effective? | Synthetic-to-real transfer learning is a framework in which a synthetically generated dataset is used to pre-train a model to improve its performance on real vision tasks . The most significant advantage of using synthetic images is that the ground-truth labels are automatically available , enabling unlimited expansion... | This paper postulates a scaling law for pre-training and transfer learning via fine-tuning. They empirically postulate a law and also theoretically show that in a simpler setting with assumptions, a similar law should hold. The paper is written and experiments are done in a synthetic to real transfer setting. Experimen... | SP:5bdbbd811a7a85540ccf3d99348aa96702191849 |
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks | 1 INTRODUCTION . Federated learning ( FL ) ( McMahan et al. , 2017a ; Yang et al. , 2019 ; Konečný et al. , 2015 ; 2017 ) allows multiple clients to collaboratively train a strong model that benefits from their individual data , without having to share that data . In particular , aggregating the parameters of locally ... | This paper aims to train a GHN model that can predict weights of each layer. The architecture is represented in adjacent matrix format, where a node represents one layer and an edge indicates whether two nodes are connected in given architecture. Each type of layer is represented with a one-hot vector as their node att... | SP:34e3fdc7b317dbac05df5db0fc75ce396e324c10 |
Federated Learning with Heterogeneous Architectures using Graph HyperNetworks | 1 INTRODUCTION . Federated learning ( FL ) ( McMahan et al. , 2017a ; Yang et al. , 2019 ; Konečný et al. , 2015 ; 2017 ) allows multiple clients to collaboratively train a strong model that benefits from their individual data , without having to share that data . In particular , aggregating the parameters of locally ... | This paper proposes a method to tackle the problem of architecture-agnostic federated learning. The primary motivation of this paper is that in some cases of federated learning, different participants may need to use different neural architectures and may not want the exact architecture to be known. To this end, the au... | SP:34e3fdc7b317dbac05df5db0fc75ce396e324c10 |
Hypothesis Driven Coordinate Ascent for Reinforcement Learning | 1 INTRODUCTION . In recent years , Reinforcement Learning ( RL ) techniques have contributed to striking advancements in artificial intelligence . From mastering complicated games like Go ( Silver et al. , 2017 ) to autonomous helicopter flight ( Ng et al. , 2006 ) , RL agents continue to make impressive strides . RL i... | The paper proposes a derivative-free optimization algorithm which is applicable to stochastic black-box functions. (The target application area is reinforcement learning, but the algorithm is more broadly applicable.) The core elements of the algorithm are 1. Block coordinate ascent: Only a few parameters are optimized... | SP:a49f7236b067ec4d09f71a2d9d5ed87421ac8351 |
Hypothesis Driven Coordinate Ascent for Reinforcement Learning | 1 INTRODUCTION . In recent years , Reinforcement Learning ( RL ) techniques have contributed to striking advancements in artificial intelligence . From mastering complicated games like Go ( Silver et al. , 2017 ) to autonomous helicopter flight ( Ng et al. , 2006 ) , RL agents continue to make impressive strides . RL i... | This paper introduces a new policy optimization method based on coordinate ascent. As the paper notes early on, the core of the proposed method is based on two central insights. The first is that coordinate ascent is a suitable approach for policy optimization as it decomposes the large search space (the policy space) ... | SP:a49f7236b067ec4d09f71a2d9d5ed87421ac8351 |
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback | 1 INTRODUCTION In this paper , we consider the nonconvex distributed/federated optimization problem of the form min 𝑥∈R𝑑 { ︂ 𝑓 ( 𝑥 ) def = 1𝑛 𝑛∑︀ 𝑖=1 𝑓𝑖 ( 𝑥 ) } ︂ , ( 1 ) where 𝑛 denotes the number of clients/workers/devices/nodes connected with a server/master and client 𝑖 has an access to the local loss f... | Error feedback (EF) is a technique for ensuring convergence of biased contractive compressor. However, it achieves suboptimal convergence rate when full gradient is used. Recently, EF21 was introduced to mitigate the theoretical deficiencies of EF. This paper studies several extensions of EF21 including EF21 with stoch... | SP:85960c4b263657c555864e5203386bba26f4f77d |
EF21 with Bells & Whistles: Practical Algorithmic Extensions of Modern Error Feedback | 1 INTRODUCTION In this paper , we consider the nonconvex distributed/federated optimization problem of the form min 𝑥∈R𝑑 { ︂ 𝑓 ( 𝑥 ) def = 1𝑛 𝑛∑︀ 𝑖=1 𝑓𝑖 ( 𝑥 ) } ︂ , ( 1 ) where 𝑛 denotes the number of clients/workers/devices/nodes connected with a server/master and client 𝑖 has an access to the local loss f... | This paper studies the extended versions of a previously proposed algorithm called EF21. The paper includes several variants of EF21 such as stochastic optimization, partial participation, variance reduction, momentum, etc. The theoretical analysis for each extension is given and experiments on real world datasets are ... | SP:85960c4b263657c555864e5203386bba26f4f77d |
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions | 1 INTRODUCTION . The point cloud registration is defined as a process to determine the spatial geometric transformations ( i.e . rigid and non-rigid transformation ) that can optimally register the source point cloud towards the target one . In comparison to classical registration methods Besl & McKay ( 1992 ) ; Yang e... | This paper addresses point cloud registration from a meta-learning perspective to quickly adapt with limited training data. The main idea is using a meta-learner is to initialise a 3D registration learner. The meta-learner predicts a prior registration that can rapidly adapt to new registration problems. Experimental r... | SP:b796596921ad17276bd65df6b8fc9195db76a574 |
3D Meta-Registration: Meta-learning 3D Point Cloud Registration Functions | 1 INTRODUCTION . The point cloud registration is defined as a process to determine the spatial geometric transformations ( i.e . rigid and non-rigid transformation ) that can optimally register the source point cloud towards the target one . In comparison to classical registration methods Besl & McKay ( 1992 ) ; Yang e... | This paper presents a new architecture for point cloud registration. Different from existing methods, the proposed architecture consists of two stages. The first one is called meta learner, which is used to predict a task distribution and sample the key parameters for the second stage - 3D registration learner. By sepa... | SP:b796596921ad17276bd65df6b8fc9195db76a574 |
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning | 1 INTRODUCTION . In Federated Learning , periodic full model aggregation is the most common approach for aggregating local models across clients . Many Federated Learning algorithms , such as FedAvg ( McMahan et al . ( 2017 ) ) , FedProx ( Li et al . ( 2018 ) ) , FedNova ( Wang et al . ( 2020 ) ) , and SCAFFOLD ( Karim... | This paper developed an adaptive aggregation method for federated learning. The theoretical analysis shows how the interval affects the convergence rate. The experiments show that it can reduce the communication cost. | SP:cf5a5f9660d0aab430057bbe11ed925e0b9419d6 |
Layer-wise Adaptive Model Aggregation for Scalable Federated Learning | 1 INTRODUCTION . In Federated Learning , periodic full model aggregation is the most common approach for aggregating local models across clients . Many Federated Learning algorithms , such as FedAvg ( McMahan et al . ( 2017 ) ) , FedProx ( Li et al . ( 2018 ) ) , FedNova ( Wang et al . ( 2020 ) ) , and SCAFFOLD ( Karim... | In this paper, the authors propose a layer-wise model aggregation scheme in federated learning cases to reduce the communication cost. Specifically, they quantified the model discrepancy between local models and global models and adaptively adjusted the aggregation interval in a layer-wise manner. By increasing the agg... | SP:cf5a5f9660d0aab430057bbe11ed925e0b9419d6 |
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring | 1 INTRODUCTION . Modern machine learning ( ML ) has seen tremendous success in various tasks across multiple domains ( Bojarski et al. , 2016 ; Hinton , 2018 ; Kreinovich & Kosheleva , 2020 ; van den Oord et al. , 2016 ) , surpassing human performance in many benchmarks ( Esteva et al. , 2017 ; Yala et al. , 2019 ; Kri... | This paper was fundamentally about out of distribution detection. The authors tried to use the changing confidence around prediction values to predict changing distributions. They did so specifically for high dimensional, unstructured data, where distributions are often difficult to understand or quantify and compared ... | SP:f76ec36462a44eabd6c76768a6fe8b0b8146b75e |
TIME-LAPSE: Learning to say “I don't know” through spatio-temporal uncertainty scoring | 1 INTRODUCTION . Modern machine learning ( ML ) has seen tremendous success in various tasks across multiple domains ( Bojarski et al. , 2016 ; Hinton , 2018 ; Kreinovich & Kosheleva , 2020 ; van den Oord et al. , 2016 ) , surpassing human performance in many benchmarks ( Esteva et al. , 2017 ; Yala et al. , 2019 ; Kri... | The paper proposes a spatio-temporal approach for unsupervised out-of-distribution detection. In particular, it suggests a 'hierarchical' evaluation method based on computation of existing distance/similarity scores on hidden embeddings of high-dimensional data succeeded by a temporal component, that is also based on e... | SP:f76ec36462a44eabd6c76768a6fe8b0b8146b75e |
A Deep Variational Approach to Clustering Survival Data | 1 INTRODUCTION Survival analysis ( Rodrı́guez , 2007 ; D. G. Altman , 2020 ) has been extensively used in a variety of medical applications to infer a relationship between explanatory variables and a potentially censored survival outcome . The latter indicates the time to a certain event , such as death or cancer recur... | This work tackles the problem of clustering in the context of survival data using a generative model. A variational autoencoder is used for modelling the data, while the latent representation is leveraged to model the survival outcome conditionned on the assigned cluster following a Weibull distribution. This approach ... | SP:5948c59cae322efd1b6fef76d3cbddfe9771c102 |
A Deep Variational Approach to Clustering Survival Data | 1 INTRODUCTION Survival analysis ( Rodrı́guez , 2007 ; D. G. Altman , 2020 ) has been extensively used in a variety of medical applications to infer a relationship between explanatory variables and a potentially censored survival outcome . The latter indicates the time to a certain event , such as death or cancer recur... | In this work, the authors propose a model for clustering survival data which accounts for both survival time as well as covariates, such as patient demographics. The model is similar to previous approaches, though it incorporates a neural network encoder to handle unstructured data types as input. A modest set of exper... | SP:5948c59cae322efd1b6fef76d3cbddfe9771c102 |
Nonparametric Learning of Two-Layer ReLU Residual Units | [ ( A∗x ) + + x ] , where ground-truth network parametersA∗ ∈ Rd×d represent a nonnegative full-rank matrix andB∗ ∈ Rm×d is full-rank with m ≥ d and for c ∈ Rd , [ c+ ] i = max { 0 , ci } . We design layer-wise objectives as functionals whose analytic minimizers express the exact ground-truth network in terms of its pa... | This paper gives an algorithm for learning two-layer neural networks with ReLU activation (realizable case). The network also consists of skip connections to avoid the problems of identifiability. As a warm up, the authors first give a simple algorithms based on linear regression, whenever enough samples could be obser... | SP:6feb07f0db5f1efc2395d906e1c645fc8b512cae |
Nonparametric Learning of Two-Layer ReLU Residual Units | [ ( A∗x ) + + x ] , where ground-truth network parametersA∗ ∈ Rd×d represent a nonnegative full-rank matrix andB∗ ∈ Rm×d is full-rank with m ≥ d and for c ∈ Rd , [ c+ ] i = max { 0 , ci } . We design layer-wise objectives as functionals whose analytic minimizers express the exact ground-truth network in terms of its pa... | This paper considers the problem of learning the parameters of a two-layer ReLU network with a residual unit: given samples of the form $(x_i, y_i)$, with $y_i = B^*[(A^* x_i)^+ + x_i]$, the authors provide an algorithm that provably learns $A^*$ and $B^*$, as the sample size grows. It is crucially assumed that the ent... | SP:6feb07f0db5f1efc2395d906e1c645fc8b512cae |
Counterfactual Plans under Distributional Ambiguity | Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains . A counterfactual plan consists of multiple possibilities to modify a given instance so that the model ’ s prediction will be altered . As the predictive model can be... | This paper studies the effect of uncertainly in the parameters of an ML model used for consequential decision making on the validity, proximity, and diversity of sets of counterfactual explanations (CFE), namely counterfactual plans. In particular, the paper assumes the model parameters are sampled from a distribution ... | SP:650494d831e1b4a0bb8f51b21574f233b1e18986 |
Counterfactual Plans under Distributional Ambiguity | Counterfactual explanations are attracting significant attention due to the flourishing applications of machine learning models in consequential domains . A counterfactual plan consists of multiple possibilities to modify a given instance so that the model ’ s prediction will be altered . As the predictive model can be... | The paper considers the problem of how to provide a collection of counterfactual explanations for a binary linear classifier such that an informed choice can be made on how best to actualise a new input based on individual preferences such that a different classification is likely to be made, even under potentially cha... | SP:650494d831e1b4a0bb8f51b21574f233b1e18986 |
Neural Energy Minimization for Molecular Conformation Optimization | 1 INTRODUCTION . The 3D molecular conformation is one of the most important features in determining many physicochemical and biological properties of a molecule . The molecule ’ s 3D charge distribution and physical shape are crucial when considering the steric constraints or electronic effects for chemical reactions a... | This paper proposes a new method to generate molecular conformations (i.e., the spatial arrangements of atoms belonging to a given molecule). The method works by updating initial atom positions (which can be initialized either randomly or using an alternative conformer generation method) through an iterative process. E... | SP:a5afbc98268d9495590e593abed021add38e5c1c |
Neural Energy Minimization for Molecular Conformation Optimization | 1 INTRODUCTION . The 3D molecular conformation is one of the most important features in determining many physicochemical and biological properties of a molecule . The molecule ’ s 3D charge distribution and physical shape are crucial when considering the steric constraints or electronic effects for chemical reactions a... | This paper proposes a new deep neural network (DNN) architecture to generate molecular 3D conformation. It derives its architecture starting from gradient-based updates for minimization of the molecule's energy function. Then the architecture becomes a repetition of vertex-wise aggregation layer of SE(3)-equivariant Tr... | SP:a5afbc98268d9495590e593abed021add38e5c1c |
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics | 1 INTRODUCTION . The recent impressive advances in reinforcement learning ( RL ) range from robotics , to strategy games and recommendation systems ( Kalashnikov et al. , 2018 ; Li et al. , 2010 ) . Reinforcement learning is canonically regarded as an active learning process - also referred to as online RL - where the ... | This paper develops a new method (Koopman Forward (Conservative) Q-learning, KFC in short) for offline reinforcement learning by extending the static dataset to include new states that are computed by learning an action-invariant Koopman latent representation of the system. The Koopman transformation to obtain the tran... | SP:b0ddaded23d30a4f36ea93c27971167644ac43f1 |
Koopman Q-learning: Offline Reinforcement Learning via Symmetries of Dynamics | 1 INTRODUCTION . The recent impressive advances in reinforcement learning ( RL ) range from robotics , to strategy games and recommendation systems ( Kalashnikov et al. , 2018 ; Li et al. , 2010 ) . Reinforcement learning is canonically regarded as an active learning process - also referred to as online RL - where the ... | The paper uses koopman theory to design principled data augmentation method for offline reinforcement learning. They learn a VAE style encoder-decoder model such that $D(E(s))=s$ and a forward model such that $F(s_t, a_t) = D((K_0 + \sum_{i=1}^m K_i, a_{t,i})E(s_t)) = s_{t+1}$. The koopman operator $K$ is then used to ... | SP:b0ddaded23d30a4f36ea93c27971167644ac43f1 |
Exploring the Limits of Large Scale Pre-training | 1 INTRODUCTION . Recent impressive progress on transfer and few-shot learning ( Brown et al. , 2020 ; Goyal et al. , 2021 ; Kolesnikov et al. , 2019 ; Pham et al. , 2020 ; Dosovitskiy et al. , 2020 ; Dumoulin et al. , 2021 ; Radford et al. , 2021 ) suggests an emerging direction that scaling up models and training them... | ### What is the Problem / Question? There is a ongoing trend in ML right now of exploring larger and larger pre-trained (PT) models. In general, increasing the scale of these PT models has yielded better performance on downstream tasks, though this is not universally the case. This work analyzes the limits of this tren... | SP:95f32813140f9e12b9d0d6f3ecad90c1ad00b0a0 |
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