Caglar Gulcehre

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Google ∙ Université de Montréal

Research Scientist at DeepMind

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Regularized Behavior Value Estimation

Offline reinforcement learning restricts the learning process to rely on...
03/17/2021 ∙ by Caglar Gulcehre, et al. ∙ 8 ∙ share

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Offline Learning from Demonstrations and Unlabeled Experience

Behavior cloning (BC) is often practical for robot learning because it a...
11/27/2020 ∙ by Konrad Zolna, et al. ∙ 6 ∙ share

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Hyperparameter Selection for Offline Reinforcement Learning

Offline reinforcement learning (RL purely from logged data) is an import...
07/17/2020 ∙ by Tom Le Paine, et al. ∙ 30 ∙ share

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Critic Regularized Regression

Offline reinforcement learning (RL), also known as batch RL, offers the ...
06/26/2020 ∙ by Ziyu Wang, et al. ∙ 32 ∙ share

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RL Unplugged: Benchmarks for Offline Reinforcement Learning

Offline methods for reinforcement learning have the potential to help br...
06/24/2020 ∙ by Caglar Gulcehre, et al. ∙ 10 ∙ share

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Acme: A Research Framework for Distributed Reinforcement Learning

Deep reinforcement learning has led to many recent-and groundbreaking-ad...
06/01/2020 ∙ by Matt Hoffman, et al. ∙ 22 ∙ share

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Improving the Gating Mechanism of Recurrent Neural Networks

Gating mechanisms are widely used in neural network models, where they a...
10/22/2019 ∙ by Albert Gu, et al. ∙ 30 ∙ share

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Stabilizing Transformers for Reinforcement Learning

Owing to their ability to both effectively integrate information over lo...
10/13/2019 ∙ by Emilio Parisotto, et al. ∙ 7 ∙ share

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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...
09/03/2019 ∙ by Tom Le Paine, et al. ∙ 10 ∙ share

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

We propose a unified mechanism for achieving coordination and communicat...
10/19/2018 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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Intrinsic Social Motivation via Causal Influence in Multi-Agent RL

We derive a new intrinsic social motivation for multi-agent reinforcemen...
10/19/2018 ∙ by Natasha Jaques, et al. ∙ 0 ∙ share

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Sample Efficient Adaptive Text-to-Speech

We present a meta-learning approach for adaptive text-to-speech (TTS) wi...
09/27/2018 ∙ by Yutian Chen, et al. ∙ 2 ∙ share

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Relational inductive biases, deep learning, and graph networks

Artificial intelligence (AI) has undergone a renaissance recently, makin...
06/04/2018 ∙ by Peter W. Battaglia, et al. ∙ 0 ∙ share

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Hyperbolic Attention Networks

We introduce hyperbolic attention networks to endow neural networks with...
05/24/2018 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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

We investigate the integration of a planning mechanism into sequence-to-...
11/28/2017 ∙ by Francis Dutil, et al. ∙ 0 ∙ share

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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...
06/13/2017 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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Gated Orthogonal Recurrent Units: On Learning to Forget

We present a novel recurrent neural network (RNN) based model that combi...
06/08/2017 ∙ by Li Jing, et al. ∙ 0 ∙ share

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Machine Comprehension by Text-to-Text Neural Question Generation

We propose a recurrent neural model that generates natural-language ques...
05/04/2017 ∙ by Xingdi Yuan, et al. ∙ 0 ∙ share

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Memory Augmented Neural Networks with Wormhole Connections

Recent empirical results on long-term dependency tasks have shown that n...
01/30/2017 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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Mollifying Networks

The optimization of deep neural networks can be more challenging than tr...
08/17/2016 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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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...
06/30/2016 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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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...
05/09/2016 ∙ by The Theano Development Team, et al. ∙ 0 ∙ share

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Pointing the Unknown Words

The problem of rare and unknown words is an important issue that can pot...
03/26/2016 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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Generating Factoid Questions With Recurrent Neural Networks: The 30M Factoid Question-Answer Corpus

Over the past decade, large-scale supervised learning corpora have enabl...
03/22/2016 ∙ by Iulian Vlad Serban, et al. ∙ 0 ∙ share

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Noisy Activation Functions

Common nonlinear activation functions used in neural networks can cause ...
03/01/2016 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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

In this work, we model abstractive text summarization using Attentional ...
02/19/2016 ∙ by Ramesh Nallapati, et al. ∙ 0 ∙ share

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On Using Monolingual Corpora in Neural Machine Translation

Recent work on end-to-end neural network-based architectures for machine...
03/11/2015 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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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...
03/05/2015 ∙ by Samira Ebrahimi Kahou, et al. ∙ 0 ∙ share

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Gated Feedback Recurrent Neural Networks

In this work, we propose a novel recurrent neural network (RNN) architec...
02/09/2015 ∙ by Junyoung Chung, et al. ∙ 0 ∙ share

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ADASECANT: Robust Adaptive Secant Method for Stochastic Gradient

Stochastic gradient algorithms have been the main focus of large-scale l...
12/23/2014 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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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...
12/11/2014 ∙ by Junyoung Chung, et al. ∙ 0 ∙ share

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Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

In this paper, we propose a novel neural network model called RNN Encode...
06/03/2014 ∙ by Kyunghyun Cho, et al. ∙ 0 ∙ share

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How to Construct Deep Recurrent Neural Networks

In this paper, we explore different ways to extend a recurrent neural ne...
12/20/2013 ∙ by Razvan Pascanu, et al. ∙ 0 ∙ share

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Learned-Norm Pooling for Deep Feedforward and Recurrent Neural Networks

In this paper we propose and investigate a novel nonlinear unit, called ...
11/07/2013 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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Knowledge Matters: Importance of Prior Information for Optimization

We explore the effect of introducing prior information into the intermed...
01/17/2013 ∙ by Caglar Gulcehre, et al. ∙ 0 ∙ share

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