Edward Grefenstette

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Research Scientist at Facebook AI Research.

Honorary Assoc. Professor at University College London.

Formerly at DeepMind and Oxford Computer Science.

A Survey of Reinforcement Learning Informed by Natural Language

To be successful in real-world tasks, Reinforcement Learning (RL) needs ...
06/10/2019 ∙ by Jelena Luketina, et al. ∙ 52 ∙ share

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TorchBeast: A PyTorch Platform for Distributed RL

TorchBeast is a platform for reinforcement learning (RL) research in PyT...
10/08/2019 ∙ by Heinrich Küttler, et al. ∙ 26 ∙ share

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RTFM: Generalising to Novel Environment Dynamics via Reading

Obtaining policies that can generalise to new environments in reinforcem...
10/18/2019 ∙ by Victor Zhong, et al. ∙ 19 ∙ share

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Learning to Follow Language Instructions with Adversarial Reward Induction

Recent work has shown that deep reinforcement-learning agents can learn ...
06/05/2018 ∙ by Dzmitry Bahdanau, et al. ∙ 14 ∙ share

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Analysing Mathematical Reasoning Abilities of Neural Models

Mathematical reasoning---a core ability within human intelligence---pres...
04/02/2019 ∙ by David Saxton, et al. ∙ 14 ∙ share

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Generalized Inner Loop Meta-Learning

Many (but not all) approaches self-qualifying as "meta-learning" in deep...
10/03/2019 ∙ by Edward Grefenstette, et al. ∙ 12 ∙ share

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Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications

Models such as Sequence-to-Sequence and Image-to-Sequence are widely use...
04/26/2019 ∙ by Chenglong Wang, et al. ∙ 6 ∙ share

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Compositional Imitation Learning: Explaining and executing one task at a time

We introduce a framework for Compositional Imitation Learning and Execut...
12/04/2018 ∙ by Thomas Kipf, et al. ∙ 4 ∙ share

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Learning to Transduce with Unbounded Memory

Recently, strong results have been demonstrated by Deep Recurrent Neural...
06/08/2015 ∙ by Edward Grefenstette, et al. ∙ 0 ∙ share

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Semantic Parsing with Semi-Supervised Sequential Autoencoders

We present a novel semi-supervised approach for sequence transduction an...
09/29/2016 ∙ by Tomáš Kočiský, et al. ∙ 0 ∙ share

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Teaching Machines to Read and Comprehend

Teaching machines to read natural language documents remains an elusive ...
06/10/2015 ∙ by Karl Moritz Hermann, et al. ∙ 0 ∙ share

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Concrete Sentence Spaces for Compositional Distributional Models of Meaning

Coecke, Sadrzadeh, and Clark (arXiv:1003.4394v1 [cs.CL]) developed a com...
12/31/2010 ∙ by Edward Grefenstette, et al. ∙ 0 ∙ share

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Discovering Discrete Latent Topics with Neural Variational Inference

Topic models have been widely explored as probabilistic generative model...
06/01/2017 ∙ by Yishu Miao, et al. ∙ 0 ∙ share

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Learning to Compute Word Embeddings On the Fly

Words in natural language follow a Zipfian distribution whereby some wor...
06/01/2017 ∙ by Dzmitry Bahdanau, et al. ∙ 0 ∙ share

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Learning to Compose Words into Sentences with Reinforcement Learning

We use reinforcement learning to learn tree-structured neural networks f...
11/28/2016 ∙ by Dani Yogatama, et al. ∙ 0 ∙ share

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The Neural Noisy Channel

We formulate sequence to sequence transduction as a noisy channel decodi...
11/08/2016 ∙ by Lei Yu, et al. ∙ 0 ∙ share

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Latent Predictor Networks for Code Generation

Many language generation tasks require the production of text conditione...
03/22/2016 ∙ by Wang Ling, et al. ∙ 0 ∙ share

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Reasoning about Entailment with Neural Attention

While most approaches to automatically recognizing entailment relations ...
09/22/2015 ∙ by Tim Rocktäschel, et al. ∙ 0 ∙ share

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Learning Explanatory Rules from Noisy Data

Artificial Neural Networks are powerful function approximators capable o...
11/13/2017 ∙ by Richard Evans, et al. ∙ 0 ∙ share

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The NarrativeQA Reading Comprehension Challenge

Reading comprehension (RC)---in contrast to information retrieval---requ...
12/19/2017 ∙ by Tomáš Kočiský, et al. ∙ 0 ∙ share

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Can Neural Networks Understand Logical Entailment?

We introduce a new dataset of logical entailments for the purpose of mea...
02/23/2018 ∙ by Richard Evans, et al. ∙ 0 ∙ share

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Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles

While deep learning has led to remarkable results on a number of challen...
11/22/2018 ∙ by Edward Grefenstette, et al. ∙ 0 ∙ share

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Differentiable Reasoning on Large Knowledge Bases and Natural Language

Reasoning with knowledge expressed in natural language and Knowledge Bas...
12/17/2019 ∙ by Pasquale Minervini, et al. ∙ 0 ∙ share

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Edward Grefenstette

Featured Co-authors

Yoshua Bengio

Kyunghyun Cho

Pushmeet Kohli

Chris Dyer

Sebastian Riedel

Shimon Whiteson

Phil Blunsom

Lei Yu

Pascal Vincent

Douwe Kiela

Tim Rocktäschel

A Survey of Reinforcement Learning Informed by Natural Language

TorchBeast: A PyTorch Platform for Distributed RL

RTFM: Generalising to Novel Environment Dynamics via Reading

Learning to Follow Language Instructions with Adversarial Reward Induction

Analysing Mathematical Reasoning Abilities of Neural Models

Generalized Inner Loop Meta-Learning

Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications

Compositional Imitation Learning: Explaining and executing one task at a time

Learning to Transduce with Unbounded Memory

Semantic Parsing with Semi-Supervised Sequential Autoencoders

Teaching Machines to Read and Comprehend

Concrete Sentence Spaces for Compositional Distributional Models of Meaning

Discovering Discrete Latent Topics with Neural Variational Inference

Learning to Compute Word Embeddings On the Fly

Learning to Compose Words into Sentences with Reinforcement Learning

The Neural Noisy Channel

Latent Predictor Networks for Code Generation

Reasoning about Entailment with Neural Attention

Learning Explanatory Rules from Noisy Data

The NarrativeQA Reading Comprehension Challenge

Can Neural Networks Understand Logical Entailment?

Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles

Differentiable Reasoning on Large Knowledge Bases and Natural Language