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08/17/2023
Reinforced Self-Training (ReST) for Language Modeling
Reinforcement learning from human feedback (RLHF) can improve the qualit...
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08/07/2023
AlphaStar Unplugged: Large-Scale Offline Reinforcement Learning
StarCraft II is one of the most challenging simulated reinforcement lear...
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07/21/2023
On the Universality of Linear Recurrences Followed by Nonlinear Projections
In this note (work in progress towards a full-length paper) we show that...
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03/11/2023
Resurrecting Recurrent Neural Networks for Long Sequences
Recurrent Neural Networks (RNNs) offer fast inference on long sequences ...
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07/05/2022
An Empirical Study of Implicit Regularization in Deep Offline RL
Deep neural networks are the most commonly used function approximators i...
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06/18/2021
Active Offline Policy Selection
This paper addresses the problem of policy selection in domains with abu...
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05/21/2021
On Instrumental Variable Regression for Deep Offline Policy Evaluation
We show that the popular reinforcement learning (RL) strategy of estimat...
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03/17/2021
Regularized Behavior Value Estimation
Offline reinforcement learning restricts the learning process to rely on...
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11/27/2020
Offline Learning from Demonstrations and Unlabeled Experience
Behavior cloning (BC) is often practical for robot learning because it a...
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07/17/2020
Hyperparameter Selection for Offline Reinforcement Learning
Offline reinforcement learning (RL purely from logged data) is an import...
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06/26/2020
Critic Regularized Regression
Offline reinforcement learning (RL), also known as batch RL, offers the ...
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06/24/2020
RL Unplugged: Benchmarks for Offline Reinforcement Learning
Offline methods for reinforcement learning have the potential to help br...
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06/01/2020
Acme: A Research Framework for Distributed Reinforcement Learning
Deep reinforcement learning has led to many recent-and groundbreaking-ad...
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10/22/2019
Improving the Gating Mechanism of Recurrent Neural Networks
Gating mechanisms are widely used in neural network models, where they a...
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10/13/2019
Stabilizing Transformers for Reinforcement Learning
Owing to their ability to both effectively integrate information over lo...
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09/03/2019
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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10/19/2018
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
We propose a unified mechanism for achieving coordination and communicat...
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10/19/2018
Intrinsic Social Motivation via Causal Influence in Multi-Agent RL
We derive a new intrinsic social motivation for multi-agent reinforcemen...
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09/27/2018
Sample Efficient Adaptive Text-to-Speech
We present a meta-learning approach for adaptive text-to-speech (TTS) wi...
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06/04/2018
Relational inductive biases, deep learning, and graph networks
Artificial intelligence (AI) has undergone a renaissance recently, makin...
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05/24/2018
Hyperbolic Attention Networks
We introduce hyperbolic attention networks to endow neural networks with...
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11/28/2017
Plan, Attend, Generate: Planning for Sequence-to-Sequence Models
We investigate the integration of a planning mechanism into sequence-to-...
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06/13/2017
Plan, Attend, Generate: Character-level Neural Machine Translation with Planning in the Decoder
We investigate the integration of a planning mechanism into an encoder-d...
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06/08/2017
Gated Orthogonal Recurrent Units: On Learning to Forget
We present a novel recurrent neural network (RNN) based model that combi...
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05/04/2017
Machine Comprehension by Text-to-Text Neural Question Generation
We propose a recurrent neural model that generates natural-language ques...
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01/30/2017
Memory Augmented Neural Networks with Wormhole Connections
Recent empirical results on long-term dependency tasks have shown that n...
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08/17/2016
Mollifying Networks
The optimization of deep neural networks can be more challenging than tr...
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06/30/2016
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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05/09/2016
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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03/26/2016
Pointing the Unknown Words
The problem of rare and unknown words is an important issue that can pot...
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03/22/2016
Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus
Over the past decade, large-scale supervised learning corpora have enabl...
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03/01/2016
Noisy Activation Functions
Common nonlinear activation functions used in neural networks can cause ...
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02/19/2016
Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond
In this work, we model abstractive text summarization using Attentional ...
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03/11/2015
On Using Monolingual Corpora in Neural Machine Translation
Recent work on end-to-end neural network-based architectures for machine...
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03/05/2015
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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02/09/2015
Gated Feedback Recurrent Neural Networks
In this work, we propose a novel recurrent neural network (RNN) architec...
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12/23/2014
ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient
Stochastic gradient algorithms have been the main focus of large-scale l...
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12/11/2014
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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06/03/2014
Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
In this paper, we propose a novel neural network model called RNN Encode...
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12/20/2013
How to Construct Deep Recurrent Neural Networks
In this paper, we explore different ways to extend a recurrent neural ne...
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11/07/2013
Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks
In this paper we propose and investigate a novel nonlinear unit, called ...
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01/17/2013
