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.

Learning Reasoning Strategies in End-to-End Differentiable Proving

Attempts to render deep learning models interpretable, data-efficient, a...
07/13/2020 ∙ by Pasquale Minervini, et al. ∙ 42 ∙ share

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The NetHack Learning Environment

Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand wit...
06/24/2020 ∙ by Heinrich Küttler, et al. ∙ 33 ∙ share

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Learning with AMIGo: Adversarially Motivated Intrinsic Goals

A key challenge for reinforcement learning (RL) consists of learning in ...
06/22/2020 ∙ by Andres Campero, et al. ∙ 22 ∙ 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. ∙ 1 ∙ 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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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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Edward Grefenstette

Featured Co-authors

Yoshua Bengio

Kyunghyun Cho

Joshua B. Tenenbaum

Pushmeet Kohli

Sebastian Riedel

Chris Dyer

Shimon Whiteson

Phil Blunsom

Lei Yu

Douwe Kiela

Tim Rocktäschel

Learning Reasoning Strategies in End-to-End Differentiable Proving

The NetHack Learning Environment

Learning with AMIGo: Adversarially Motivated Intrinsic Goals

Differentiable Reasoning on Large Knowledge Bases and Natural Language

RTFM: Generalising to Novel Environment Dynamics via Reading

TorchBeast: A PyTorch Platform for Distributed RL

Generalized Inner Loop Meta-Learning

A Survey of Reinforcement Learning Informed by Natural Language

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

Analysing Mathematical Reasoning Abilities of Neural Models

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

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

Learning to Follow Language Instructions with Adversarial Reward Induction

Can Neural Networks Understand Logical Entailment?

The NarrativeQA Reading Comprehension Challenge

Learning Explanatory Rules from Noisy Data

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

Semantic Parsing with Semi-Supervised Sequential Autoencoders

Latent Predictor Networks for Code Generation

Reasoning about Entailment with Neural Attention

Teaching Machines to Read and Comprehend

Learning to Transduce with Unbounded Memory

Concrete Sentence Spaces for Compositional Distributional Models of Meaning