
Behavior Priors for Efficient Reinforcement Learning
As we deploy reinforcement learning agents to solve increasingly challen...
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BYOL works even without batch statistics
Bootstrap Your Own Latent (BYOL) is a selfsupervised learning approach ...
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Linear Mode Connectivity in Multitask and Continual Learning
Continual (sequential) training and multitask (simultaneous) training ar...
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Understanding the Role of Training Regimes in Continual Learning
Catastrophic forgetting affects the training of neural networks, limitin...
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Pointer Graph Networks
Graph neural networks (GNNs) are typically applied to static graphs that...
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A Deep Neural Network's Loss Surface Contains Every Lowdimensional Pattern
The work "Loss Landscape Sightseeing with MultiPoint Optimization" (Sko...
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Continual Unsupervised Representation Learning
Continual learning aims to improve the ability of modern learning system...
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Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they a...
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Stabilizing Transformers for Reinforcement Learning
Owing to their ability to both effectively integrate information over lo...
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MetaLearning with Warped Gradient Descent
A versatile and effective approach to metalearning is to infer a gradie...
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Task Agnostic Continual Learning via Meta Learning
While neural networks are powerful function approximators, they suffer f...
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Metalearning of Sequential Strategies
In this report we review memorybased metalearning as a tool for buildi...
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Information asymmetry in KLregularized RL
Many real world tasks exhibit rich structure that is repeated across dif...
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Ray Interference: a Source of Plateaus in Deep Reinforcement Learning
Rather than proposing a new method, this paper investigates an issue pre...
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A RAD approach to deep mixture models
Flow based models such as Real NVP are an extremely powerful approach to...
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Exploiting Hierarchy for Learning and Transfer in KLregularized RL
As reinforcement learning agents are tasked with solving more challengin...
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Distilling Policy Distillation
The transfer of knowledge from one policy to another is an important too...
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Functional Regularisation for Continual Learning using Gaussian Processes
We introduce a novel approach for supervised continual learning based on...
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Adapting Auxiliary Losses Using Gradient Similarity
One approach to deal with the statistical inefficiency of neural network...
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MetaLearning with Latent Embedding Optimization
Gradientbased metalearning techniques are both widely applicable and p...
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Relational Deep Reinforcement Learning
We introduce an approach for deep reinforcement learning (RL) that impro...
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Relational recurrent neural networks
Memorybased neural networks model temporal data by leveraging an abilit...
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Mix&Match  Agent Curricula for Reinforcement Learning
We introduce Mix&Match (M&M)  a training framework designed to facilita...
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Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with...
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Been There, Done That: MetaLearning with Episodic Recall
Metalearning agents excel at rapidly learning new tasks from openended...
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Progress & Compress: A scalable framework for continual learning
We introduce a conceptually simple and scalable framework for continual ...
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Lowpass Recurrent Neural Networks  A memory architecture for longerterm correlation discovery
Reinforcement learning (RL) agents performing complex tasks must be able...
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Block Mean Approximation for Efficient Second Order Optimization
Advanced optimization algorithms such as Newton method and AdaGrad benef...
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Learning Deep Generative Models of Graphs
Graphs are fundamental data structures which concisely capture the relat...
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Memorybased Parameter Adaptation
Deep neural networks have excelled on a wide range of problems, from vis...
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Model compression via distillation and quantization
Deep neural networks (DNNs) continue to make significant advances, solvi...
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ImaginationAugmented Agents for Deep Reinforcement Learning
We introduce ImaginationAugmented Agents (I2As), a novel architecture f...
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Learning modelbased planning from scratch
Conventional wisdom holds that modelbased planning is a powerful approa...
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Distral: Robust Multitask Reinforcement Learning
Most deep reinforcement learning algorithms are data inefficient in comp...
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Visual Interaction Networks
From just a glance, humans can make rich predictions about the future st...
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A simple neural network module for relational reasoning
Relational reasoning is a central component of generally intelligent beh...
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Metacontrol for Adaptive ImaginationBased Optimization
Many machine learning systems are built to solve the hardest examples of...
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Sharp Minima Can Generalize For Deep Nets
Despite their overwhelming capacity to overfit, deep learning architectu...
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Overcoming catastrophic forgetting in neural networks
The ability to learn tasks in a sequential fashion is crucial to the dev...
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Interaction Networks for Learning about Objects, Relations and Physics
Reasoning about objects, relations, and physics is central to human inte...
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Local minima in training of neural networks
There has been a lot of recent interest in trying to characterize the er...
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Learning to Navigate in Complex Environments
Learning to navigate in complex environments with dynamic elements is an...
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Theano: A Python framework for fast computation of mathematical expressions
Theano is a Python library that allows to define, optimize, and evaluate...
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Natural Neural Networks
We introduce Natural Neural Networks, a novel family of algorithms that ...
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On the saddle point problem for nonconvex optimization
A central challenge to many fields of science and engineering involves m...
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On the Number of Linear Regions of Deep Neural Networks
We study the complexity of functions computable by deep feedforward neur...
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On the number of response regions of deep feed forward networks with piecewise linear activations
This paper explores the complexity of deep feedforward networks with lin...
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How to Construct Deep Recurrent Neural Networks
In this paper, we explore different ways to extend a recurrent neural ne...
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LearnedNorm Pooling for Deep Feedforward and Recurrent Neural Networks
In this paper we propose and investigate a novel nonlinear unit, called ...
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Razvan Pascanu
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Research Scientist at Google DeepMind, Phd Student in Machine Learning at Université de Montréal, Developer at Université de Montréal