Caglar Gulcehre

research

∙ 07/05/2022

An Empirical Study of Implicit Regularization in Deep Offline RL

Deep neural networks are the most commonly used function approximators i...

Caglar Gulcehre, et al. ∙

research

∙ 06/18/2021

Active Offline Policy Selection

This paper addresses the problem of policy selection in domains with abu...

Ksenia Konyushkova, et al. ∙

research

∙ 05/21/2021

On Instrumental Variable Regression for Deep Offline Policy Evaluation

We show that the popular reinforcement learning (RL) strategy of estimat...

Yutian Chen, et al. ∙

research

∙ 03/17/2021

Regularized Behavior Value Estimation

Offline reinforcement learning restricts the learning process to rely on...

Caglar Gulcehre, et al. ∙

research

∙ 11/27/2020

Offline Learning from Demonstrations and Unlabeled Experience

Behavior cloning (BC) is often practical for robot learning because it a...

Konrad Zolna, et al. ∙

research

∙ 07/17/2020

Hyperparameter Selection for Offline Reinforcement Learning

Offline reinforcement learning (RL purely from logged data) is an import...

Tom Le Paine, et al. ∙

research

∙ 06/26/2020

Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...

Ziyu Wang, et al. ∙

research

∙ 06/24/2020

RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...

Caglar Gulcehre, et al. ∙

research

∙ 06/01/2020

Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-ad...

Matt Hoffman, et al. ∙

research

∙ 10/22/2019

Improving the Gating Mechanism of Recurrent Neural Networks

Gating mechanisms are widely used in neural network models, where they a...

Albert Gu, et al. ∙

research

∙ 10/13/2019

Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over lo...

Emilio Parisotto, et al. ∙

research

∙ 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...

Tom Le Paine, et al. ∙

research

∙ 10/19/2018

Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning

We propose a unified mechanism for achieving coordination and communicat...

Natasha Jaques, et al. ∙

research

∙ 10/19/2018

Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

We derive a new intrinsic social motivation for multi-agent reinforcemen...

Natasha Jaques, et al. ∙

research

∙ 09/27/2018

Sample Efficient Adaptive Text-to-Speech

We present a meta-learning approach for adaptive text-to-speech (TTS) wi...

Yutian Chen, et al. ∙

research

∙ 06/04/2018

Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...

Peter W. Battaglia, et al. ∙

research

∙ 05/24/2018

Hyperbolic Attention Networks

We introduce hyperbolic attention networks to endow neural networks with...

Caglar Gulcehre, et al. ∙

research

∙ 11/28/2017

Plan, Attend, Generate: Planning for Sequence-to-Sequence Models

We investigate the integration of a planning mechanism into sequence-to-...

Francis Dutil, et al. ∙

research

∙ 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...

Caglar Gulcehre, et al. ∙

research

∙ 06/08/2017

Gated Orthogonal Recurrent Units: On Learning to Forget

We present a novel recurrent neural network (RNN) based model that combi...

Li Jing, et al. ∙

research

∙ 05/04/2017

Machine Comprehension by Text-to-Text Neural Question Generation

We propose a recurrent neural model that generates natural-language ques...

Xingdi Yuan, et al. ∙

research

∙ 01/30/2017

Memory Augmented Neural Networks with Wormhole Connections

Recent empirical results on long-term dependency tasks have shown that n...

Caglar Gulcehre, et al. ∙

research

∙ 08/17/2016

Mollifying Networks

The optimization of deep neural networks can be more challenging than tr...

Caglar Gulcehre, et al. ∙

research

∙ 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...

Caglar Gulcehre, et al. ∙

research

∙ 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...

The Theano Development Team, et al. ∙

research

∙ 03/26/2016

Pointing the Unknown Words

The problem of rare and unknown words is an important issue that can pot...

Caglar Gulcehre, et al. ∙

research

∙ 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...

Iulian Vlad Serban, et al. ∙

research

∙ 03/01/2016

Noisy Activation Functions

Common nonlinear activation functions used in neural networks can cause ...

Caglar Gulcehre, et al. ∙

research

∙ 02/19/2016

Abstractive Text Summarization Using Sequence-to-Sequence RNNs and Beyond

In this work, we model abstractive text summarization using Attentional ...

Ramesh Nallapati, et al. ∙

research

∙ 03/11/2015

On Using Monolingual Corpora in Neural Machine Translation

Recent work on end-to-end neural network-based architectures for machine...

Caglar Gulcehre, et al. ∙

research

∙ 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...

Samira Ebrahimi Kahou, et al. ∙

research

∙ 02/09/2015

Gated Feedback Recurrent Neural Networks

In this work, we propose a novel recurrent neural network (RNN) architec...

Junyoung Chung, et al. ∙

research

∙ 12/23/2014

ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient

Stochastic gradient algorithms have been the main focus of large-scale l...

Caglar Gulcehre, et al. ∙

research

∙ 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...

Junyoung Chung, et al. ∙

research

∙ 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...

Kyunghyun Cho, et al. ∙

research

∙ 12/20/2013

How to Construct Deep Recurrent Neural Networks

In this paper, we explore different ways to extend a recurrent neural ne...

Razvan Pascanu, et al. ∙

research

∙ 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 ...

Caglar Gulcehre, et al. ∙

research

∙ 01/17/2013

Knowledge Matters: Importance of Prior Information for Optimization

We explore the effect of introducing prior information into the intermed...

Caglar Gulcehre, et al. ∙

Caglar Gulcehre

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