
Hyperparameter Selection for Offline Reinforcement Learning
Offline reinforcement learning (RL purely from logged data) is an import...
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Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the ...
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RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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Acme: A Research Framework for Distributed Reinforcement Learning
Deep reinforcement learning has led to many recentand groundbreakingad...
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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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Making Efficient Use of Demonstrations to Solve Hard Exploration Problems
This paper introduces R2D3, an agent that makes efficient use of demonst...
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Social Influence as Intrinsic Motivation for MultiAgent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communicat...
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Intrinsic Social Motivation via Causal Influence in MultiAgent RL
We derive a new intrinsic social motivation for multiagent reinforcemen...
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Sample Efficient Adaptive TexttoSpeech
We present a metalearning approach for adaptive texttospeech (TTS) wi...
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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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Plan, Attend, Generate: Planning for SequencetoSequence Models
We investigate the integration of a planning mechanism into sequenceto...
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Plan, Attend, Generate: Characterlevel Neural Machine Translation with Planning in the Decoder
We investigate the integration of a planning mechanism into an encoderd...
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Gated Orthogonal Recurrent Units: On Learning to Forget
We present a novel recurrent neural network (RNN) based model that combi...
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Machine Comprehension by TexttoText Neural Question Generation
We propose a recurrent neural model that generates naturallanguage ques...
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Memory Augmented Neural Networks with Wormhole Connections
Recent empirical results on longterm dependency tasks have shown that n...
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Mollifying Networks
The optimization of deep neural networks can be more challenging than tr...
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Dynamic Neural Turing Machine with Soft and Hard Addressing Schemes
We extend neural Turing machine (NTM) model into a dynamic neural Turing...
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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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Pointing the Unknown Words
The problem of rare and unknown words is an important issue that can pot...
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Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid QuestionAnswer Corpus
Over the past decade, largescale supervised learning corpora have enabl...
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Noisy Activation Functions
Common nonlinear activation functions used in neural networks can cause ...
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Abstractive Text Summarization Using SequencetoSequence RNNs and Beyond
In this work, we model abstractive text summarization using Attentional ...
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On Using Monolingual Corpora in Neural Machine Translation
Recent work on endtoend neural networkbased architectures for machine...
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EmoNets: Multimodal deep learning approaches for emotion recognition in video
The task of the emotion recognition in the wild (EmotiW) Challenge is to...
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Gated Feedback Recurrent Neural Networks
In this work, we propose a novel recurrent neural network (RNN) architec...
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ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient
Stochastic gradient algorithms have been the main focus of largescale l...
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Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
In this paper we compare different types of recurrent units in recurrent...
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Learning Phrase Representations using RNN EncoderDecoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN Encode...
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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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Knowledge Matters: Importance of Prior Information for Optimization
We explore the effect of introducing prior information into the intermed...
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