Stop the war!Остановите войну!
for scientists:
default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDEdward Grefenstette
Edward Thomas Grefenstette
Person information
- affiliation: Google DeepMind
- affiliation: University of Oxford, UK
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
Books and Theses
- 2013
[b1]Edward Thomas Grefenstette:
Category-theoretic quantitative compositional distributional models of natural language semantics. University of Oxford, UK, 2013
Journal Articles
- 2023
- [j6]Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel:
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning. J. Artif. Intell. Res. 76: 201-264 (2023) - 2018
- [j5]Richard Evans, Edward Grefenstette:
Learning Explanatory Rules from Noisy Data. J. Artif. Intell. Res. 61: 1-64 (2018) - [j4]Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette:
The NarrativeQA Reading Comprehension Challenge. Trans. Assoc. Comput. Linguistics 6: 317-328 (2018) - 2016
- [j3]Alex Graves, Greg Wayne, Malcolm Reynolds, Tim Harley, Ivo Danihelka, Agnieszka Grabska-Barwinska, Sergio Gomez Colmenarejo, Edward Grefenstette, Tiago Ramalho, John P. Agapiou
, Adrià Puigdomènech Badia, Karl Moritz Hermann, Yori Zwols, Georg Ostrovski, Adam Cain, Helen King, Christopher Summerfield, Phil Blunsom, Koray Kavukcuoglu, Demis Hassabis:
Hybrid computing using a neural network with dynamic external memory. Nat. 538(7626): 471-476 (2016) - 2015
- [j2]Edward Grefenstette, Mehrnoosh Sadrzadeh:
Concrete Models and Empirical Evaluations for the Categorical Compositional Distributional Model of Meaning. Comput. Linguistics 41(1): 71-118 (2015) - 2013
- [j1]Bob Coecke, Edward Grefenstette, Mehrnoosh Sadrzadeh:
Lambek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus. Ann. Pure Appl. Log. 164(11): 1079-1100 (2013)
Conference and Workshop Papers
- 2024
- [c48]Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster:
Scaling Opponent Shaping to High Dimensional Games. AAMAS 2024: 1001-1010 - 2023
- [c47]Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian:
Efficient Planning in a Compact Latent Action Space. ICLR 2023 - [c46]Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth:
Optimal Transport for Offline Imitation Learning. ICLR 2023 - [c45]Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette:
The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs. NeurIPS 2023 - 2022
- [c44]Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel:
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoLLAs 2022: 856-874 - [c43]Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Evolving Curricula with Regret-Based Environment Design. ICML 2022: 17473-17498 - [c42]Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster:
Grounding Aleatoric Uncertainty for Unsupervised Environment Design. NeurIPS 2022 - [c41]Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. NeurIPS 2022 - [c40]Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen Roberts, Tim Rocktäschel, Edward Grefenstette:
Learning General World Models in a Handful of Reward-Free Deployments. NeurIPS 2022 - [c39]Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel:
Improving Policy Learning via Language Dynamics Distillation. NeurIPS 2022 - 2021
- [c38]Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette:
Learning with AMIGo: Adversarially Motivated Intrinsic Goals. ICLR 2021 - [c37]Minqi Jiang, Edward Grefenstette, Tim Rocktäschel:
Prioritized Level Replay. ICML 2021: 4940-4950 - [c36]Eric Hambro, Sharada P. Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michal Sypetkowski:
Insights From the NeurIPS 2021 NetHack Challenge. NeurIPS (Competition and Demos) 2021: 41-52 - [c35]Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Replay-Guided Adversarial Environment Design. NeurIPS 2021: 1884-1897 - [c34]Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel:
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. NeurIPS Datasets and Benchmarks 2021 - 2020
- [c33]Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette:
Differentiable Reasoning on Large Knowledge Bases and Natural Language. AAAI 2020: 5182-5190 - [c32]Victor Zhong, Tim Rocktäschel, Edward Grefenstette:
RTFM: Generalising to New Environment Dynamics via Reading. ICLR 2020 - [c31]Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel:
Learning Reasoning Strategies in End-to-End Differentiable Proving. ICML 2020: 6938-6949 - [c30]Sarah Bechtle, Artem Molchanov, Yevgen Chebotar, Edward Grefenstette, Ludovic Righetti, Gaurav S. Sukhatme, Franziska Meier:
Meta Learning via Learned Loss. ICPR 2020: 4161-4168 - [c29]Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel:
The NetHack Learning Environment. NeurIPS 2020 - 2019
- [c28]Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli:
Knowing When to Stop: Evaluation and Verification of Conformity to Output-Size Specifications. CVPR 2019: 12260-12269 - [c27]Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Seyed Arian Hosseini, Pushmeet Kohli, Edward Grefenstette:
Learning to Understand Goal Specifications by Modelling Reward. ICLR (Poster) 2019 - [c26]David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli:
Analysing Mathematical Reasoning Abilities of Neural Models. ICLR (Poster) 2019 - [c25]Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Alvaro Sanchez-Gonzalez, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
CompILE: Compositional Imitation Learning and Execution. ICML 2019: 3418-3428 - [c24]Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel:
A Survey of Reinforcement Learning Informed by Natural Language. IJCAI 2019: 6309-6317 - 2018
- [c23]Edward Grefenstette:
Teaching Artificial Agents to Understand Language by Modelling Reward. CIKM 2018: 5-6 - [c22]Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette:
Jointly Learning "What" and "How" from Instructions and Goal-States. ICLR (Workshop) 2018 - [c21]Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette:
Can Neural Networks Understand Logical Entailment? ICLR (Poster) 2018 - [c20]Richard Evans, Edward Grefenstette:
Learning Explanatory Rules from Noisy Data (Extended Abstract). IJCAI 2018: 5598-5602 - 2017
- [c19]Xiaodan Zhu, Edward Grefenstette:
Deep Learning for Semantic Composition. ACL (Tutorial Abstracts) 2017: 6-7 - [c18]Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling:
Learning to Compose Words into Sentences with Reinforcement Learning. ICLR (Poster) 2017 - [c17]Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský:
The Neural Noisy Channel. ICLR (Poster) 2017 - [c16]Yishu Miao, Edward Grefenstette, Phil Blunsom:
Discovering Discrete Latent Topics with Neural Variational Inference. ICML 2017: 2410-2419 - 2016
- [c15]Wang Ling, Phil Blunsom, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Fumin Wang, Andrew W. Senior:
Latent Predictor Networks for Code Generation. ACL (1) 2016 - [c14]Tomás Kociský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann:
Semantic Parsing with Semi-Supervised Sequential Autoencoders. EMNLP 2016: 1078-1087 - [c13]Tim Rocktäschel, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Phil Blunsom:
Reasoning about Entailment with Neural Attention. ICLR (Poster) 2016 - 2015
- [c12]Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih:
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality. CVSC 2015 - [c11]Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom:
Teaching Machines to Read and Comprehend. NIPS 2015: 1693-1701 - [c10]Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom:
Learning to Transduce with Unbounded Memory. NIPS 2015: 1828-1836 - 2014
- [c9]Edward Grefenstette, Karl Moritz Hermann, Georgiana Dinu, Phil Blunsom:
New Directions in Vector Space Models of Meaning. ACL (Tutorial Abstracts) 2014: 8 - [c8]Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom:
A Convolutional Neural Network for Modelling Sentences. ACL (1) 2014: 655-665 - 2013
- [c7]Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom:
"Not not bad" is not "bad": A distributional account of negation. CVSM@ACL 2013: 74-82 - [c6]Edward Grefenstette, Georgiana Dinu, Yao-Zhong Zhang, Mehrnoosh Sadrzadeh, Marco Baroni:
Multi-Step Regression Learning for Compositional Distributional Semantics. IWCS 2013: 131-142 - [c5]Edward Grefenstette:
Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors. *SEM@NAACL-HLT 2013: 1-10 - 2011
- [c4]Edward Grefenstette, Mehrnoosh Sadrzadeh:
Experimenting with transitive verbs in a DisCoCat. GEMS 2011: 62-66 - [c3]Edward Grefenstette, Mehrnoosh Sadrzadeh:
Experimental Support for a Categorical Compositional Distributional Model of Meaning. EMNLP 2011: 1394-1404 - [c2]Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke, Stephen Pulman:
Concrete Sentence Spaces for Compositional Distributional Models of Meaning. IWCS 2011 - [c1]Mehrnoosh Sadrzadeh, Edward Grefenstette:
A Compositional Distributional Semantics, Two Concrete Constructions, and Some Experimental Evaluations. QI 2011: 35-47
Parts in Books or Collections
- 2021
- [p2]Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel:
Learning Reasoning Strategies in End-to-End Differentiable Proving. Neuro-Symbolic Artificial Intelligence 2021: 280-293 - 2020
- [p1]Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette:
Differentiable Reasoning on Large Knowledge Bases and Natural Language. Knowledge Graphs for eXplainable Artificial Intelligence 2020: 125-142
Editorship
- 2023
- [e7]Burcu Can, Maximilian Mozes, Samuel Cahyawijaya, Naomi Saphra, Nora Kassner, Shauli Ravfogel, Abhilasha Ravichander, Chen Zhao, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Lena Voita:
Proceedings of the 8th Workshop on Representation Learning for NLP, RepL4NLP@ACL 2023, Toronto, Canada, July 13, 2023. Association for Computational Linguistics 2023, ISBN 978-1-959429-77-7 [contents] - 2022
- [e6]Spandana Gella, He He, Bodhisattwa Prasad Majumder, Burcu Can, Eleonora Giunchiglia, Samuel Cahyawijaya, Sewon Min, Maximilian Mozes, Xiang Lorraine Li, Isabelle Augenstein, Anna Rogers, Kyunghyun Cho, Edward Grefenstette, Laura Rimell, Chris Dyer:
Proceedings of the 7th Workshop on Representation Learning for NLP, RepL4NLP@ACL 2022, Dublin, Ireland, May 26, 2022. Association for Computational Linguistics 2022, ISBN 978-1-955917-48-3 [contents] - 2017
- [e5]Phil Blunsom, Antoine Bordes, Kyunghyun Cho, Shay B. Cohen, Chris Dyer, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Yih:
Proceedings of the 2nd Workshop on Representation Learning for NLP, Rep4NLP@ACL 2017, Vancouver, Canada, August 3, 2017. Association for Computational Linguistics 2017, ISBN 978-1-945626-62-3 [contents] - 2016
- [e4]Phil Blunsom, Kyunghyun Cho, Shay B. Cohen, Edward Grefenstette, Karl Moritz Hermann, Laura Rimell, Jason Weston, Scott Wen-tau Yih:
Proceedings of the 1st Workshop on Representation Learning for NLP, Rep4NLP@ACL 2016, Berlin, Germany, August 11, 2016. Association for Computational Linguistics 2016, ISBN 978-1-945626-04-3 [contents] - 2015
- [e3]Alexandre Allauzen, Edward Grefenstette, Karl Moritz Hermann, Hugo Larochelle, Scott Wen-tau Yih:
Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality, CVSC 2015, Beijing, China, July 26-31, 2015. Association for Computational Linguistics 2015 [contents] - 2014
- [e2]Alexandre Allauzen, Raffaella Bernardi, Edward Grefenstette, Hugo Larochelle, Christopher D. Manning, Scott Wen-tau Yih:
Proceedings of the 2nd Workshop on Continuous Vector Space Models and their Compositionality, CVSC@EACL 2014, Gothenburg, Sweden, April 26-30, 2014. Association for Computational Linguistics 2014, ISBN 978-1-937284-94-7 [contents] - 2013
- [e1]Chris Heunen, Mehrnoosh Sadrzadeh, Edward Grefenstette:
Quantum Physics and Linguistics - A Compositional, Diagrammatic Discourse. Oxford University Press 2013, ISBN 978-0-19-964629-6 [contents]
Informal and Other Publications
- 2024
- [i61]Akbir Khan, John Hughes, Dan Valentine, Laura Ruis, Kshitij Sachan, Ansh Radhakrishnan, Edward Grefenstette, Samuel R. Bowman, Tim Rocktäschel, Ethan Perez:
Debating with More Persuasive LLMs Leads to More Truthful Answers. CoRR abs/2402.06782 (2024) - 2023
- [i60]Yicheng Luo, Zhengyao Jiang, Samuel Cohen, Edward Grefenstette, Marc Peter Deisenroth:
Optimal Transport for Offline Imitation Learning. CoRR abs/2303.13971 (2023) - [i59]Yicheng Luo, Jackie Kay, Edward Grefenstette, Marc Peter Deisenroth:
Finetuning from Offline Reinforcement Learning: Challenges, Trade-offs and Practical Solutions. CoRR abs/2303.17396 (2023) - [i58]Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, Jelena Luketina, Eric Hambro, Edward Grefenstette, Roberta Raileanu:
Understanding the Effects of RLHF on LLM Generalisation and Diversity. CoRR abs/2310.06452 (2023) - [i57]Minqi Jiang, Michael Dennis, Edward Grefenstette, Tim Rocktäschel:
minimax: Efficient Baselines for Autocurricula in JAX. CoRR abs/2311.12716 (2023) - [i56]Samyak Jain, Robert Kirk, Ekdeep Singh Lubana, Robert P. Dick, Hidenori Tanaka, Edward Grefenstette, Tim Rocktäschel, David Scott Krueger:
Mechanistically analyzing the effects of fine-tuning on procedurally defined tasks. CoRR abs/2311.12786 (2023) - [i55]Zhengyao Jiang, Yingchen Xu, Nolan Wagener, Yicheng Luo, Michael Janner, Edward Grefenstette, Tim Rocktäschel, Yuandong Tian:
H-GAP: Humanoid Control with a Generalist Planner. CoRR abs/2312.02682 (2023) - [i54]Alexandra Souly, Timon Willi, Akbir Khan, Robert Kirk, Chris Lu, Edward Grefenstette, Tim Rocktäschel:
Leading the Pack: N-player Opponent Shaping. CoRR abs/2312.12564 (2023) - [i53]Akbir Khan, Timon Willi, Newton Kwan, Andrea Tacchetti, Chris Lu, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster:
Scaling Opponent Shaping to High Dimensional Games. CoRR abs/2312.12568 (2023) - 2022
- [i52]Jesse Mu, Victor Zhong, Roberta Raileanu, Minqi Jiang, Noah D. Goodman, Tim Rocktäschel, Edward Grefenstette:
Improving Intrinsic Exploration with Language Abstractions. CoRR abs/2202.08938 (2022) - [i51]Jack Parker-Holder, Minqi Jiang, Michael Dennis, Mikayel Samvelyan, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Evolving Curricula with Regret-Based Environment Design. CoRR abs/2203.01302 (2022) - [i50]Eric Hambro, Sharada P. Mohanty, Dmitrii Babaev, Minwoo Byeon, Dipam Chakraborty, Edward Grefenstette, Minqi Jiang, DaeJin Jo, Anssi Kanervisto, Jongmin Kim, Sungwoong Kim, Robert Kirk, Vitaly Kurin, Heinrich Küttler, Taehwon Kwon, Donghoon Lee, Vegard Mella, Nantas Nardelli, Ivan Nazarov, Nikita Ovsov, Jack Parker-Holder, Roberta Raileanu, Karolis Ramanauskas, Tim Rocktäschel, Danielle Rothermel, Mikayel Samvelyan, Dmitry Sorokin, Maciej Sypetkowski, Michal Sypetkowski:
Insights From the NeurIPS 2021 NetHack Challenge. CoRR abs/2203.11889 (2022) - [i49]Zhengyao Jiang, Tianjun Zhang, Robert Kirk, Tim Rocktäschel, Edward Grefenstette:
Graph Backup: Data Efficient Backup Exploiting Markovian Transitions. CoRR abs/2205.15824 (2022) - [i48]Minqi Jiang, Michael Dennis, Jack Parker-Holder, Andrei Lupu, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel, Jakob N. Foerster:
Grounding Aleatoric Uncertainty in Unsupervised Environment Design. CoRR abs/2207.05219 (2022) - [i47]Michael Matthews, Mikayel Samvelyan, Jack Parker-Holder, Edward Grefenstette, Tim Rocktäschel:
Hierarchical Kickstarting for Skill Transfer in Reinforcement Learning. CoRR abs/2207.11584 (2022) - [i46]Zhengyao Jiang, Tianjun Zhang, Michael Janner, Yueying Li, Tim Rocktäschel, Edward Grefenstette, Yuandong Tian:
Efficient Planning in a Compact Latent Action Space. CoRR abs/2208.10291 (2022) - [i45]Victor Zhong, Jesse Mu, Luke Zettlemoyer, Edward Grefenstette, Tim Rocktäschel:
Improving Policy Learning via Language Dynamics Distillation. CoRR abs/2210.00066 (2022) - [i44]Yingchen Xu, Jack Parker-Holder, Aldo Pacchiano, Philip J. Ball, Oleh Rybkin, Stephen J. Roberts, Tim Rocktäschel, Edward Grefenstette:
Learning General World Models in a Handful of Reward-Free Deployments. CoRR abs/2210.12719 (2022) - [i43]Laura Ruis, Akbir Khan, Stella Biderman, Sara Hooker, Tim Rocktäschel, Edward Grefenstette:
Large language models are not zero-shot communicators. CoRR abs/2210.14986 (2022) - [i42]Minqi Jiang, Tim Rocktäschel, Edward Grefenstette:
General Intelligence Requires Rethinking Exploration. CoRR abs/2211.07819 (2022) - 2021
- [i41]Mikayel Samvelyan, Robert Kirk, Vitaly Kurin, Jack Parker-Holder, Minqi Jiang, Eric Hambro, Fabio Petroni, Heinrich Küttler, Edward Grefenstette, Tim Rocktäschel:
MiniHack the Planet: A Sandbox for Open-Ended Reinforcement Learning Research. CoRR abs/2109.13202 (2021) - [i40]Minqi Jiang, Michael Dennis, Jack Parker-Holder, Jakob N. Foerster, Edward Grefenstette, Tim Rocktäschel:
Replay-Guided Adversarial Environment Design. CoRR abs/2110.02439 (2021) - [i39]Robert Kirk, Amy Zhang, Edward Grefenstette, Tim Rocktäschel:
A Survey of Generalisation in Deep Reinforcement Learning. CoRR abs/2111.09794 (2021) - 2020
- [i38]Andres Campero, Roberta Raileanu, Heinrich Küttler, Joshua B. Tenenbaum, Tim Rocktäschel, Edward Grefenstette:
Learning with AMIGo: Adversarially Motivated Intrinsic Goals. CoRR abs/2006.12122 (2020) - [i37]Heinrich Küttler, Nantas Nardelli, Alexander H. Miller, Roberta Raileanu, Marco Selvatici, Edward Grefenstette, Tim Rocktäschel:
The NetHack Learning Environment. CoRR abs/2006.13760 (2020) - [i36]Pasquale Minervini, Sebastian Riedel, Pontus Stenetorp, Edward Grefenstette, Tim Rocktäschel:
Learning Reasoning Strategies in End-to-End Differentiable Proving. CoRR abs/2007.06477 (2020) - [i35]Minqi Jiang, Edward Grefenstette, Tim Rocktäschel:
Prioritized Level Replay. CoRR abs/2010.03934 (2020) - 2019
- [i34]David Saxton, Edward Grefenstette, Felix Hill, Pushmeet Kohli:
Analysing Mathematical Reasoning Abilities of Neural Models. CoRR abs/1904.01557 (2019) - [i33]Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli:
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications. CoRR abs/1904.12004 (2019) - [i32]Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson, Tim Rocktäschel:
A Survey of Reinforcement Learning Informed by Natural Language. CoRR abs/1906.03926 (2019) - [i31]Edward Grefenstette, Brandon Amos, Denis Yarats, Phu Mon Htut, Artem Molchanov, Franziska Meier, Douwe Kiela, Kyunghyun Cho, Soumith Chintala:
Generalized Inner Loop Meta-Learning. CoRR abs/1910.01727 (2019) - [i30]Heinrich Küttler, Nantas Nardelli, Thibaut Lavril, Marco Selvatici, Viswanath Sivakumar, Tim Rocktäschel, Edward Grefenstette:
TorchBeast: A PyTorch Platform for Distributed RL. CoRR abs/1910.03552 (2019) - [i29]Victor Zhong, Tim Rocktäschel, Edward Grefenstette:
RTFM: Generalising to Novel Environment Dynamics via Reading. CoRR abs/1910.08210 (2019) - [i28]Pasquale Minervini, Matko Bosnjak, Tim Rocktäschel, Sebastian Riedel, Edward Grefenstette:
Differentiable Reasoning on Large Knowledge Bases and Natural Language. CoRR abs/1912.10824 (2019) - 2018
- [i27]Richard Evans, David Saxton, David Amos, Pushmeet Kohli, Edward Grefenstette:
Can Neural Networks Understand Logical Entailment? CoRR abs/1802.08535 (2018) - [i26]Dzmitry Bahdanau, Felix Hill, Jan Leike, Edward Hughes, Pushmeet Kohli, Edward Grefenstette:
Learning to Follow Language Instructions with Adversarial Reward Induction. CoRR abs/1806.01946 (2018) - [i25]Edward Grefenstette, Robert Stanforth, Brendan O'Donoghue, Jonathan Uesato, Grzegorz Swirszcz, Pushmeet Kohli:
Strength in Numbers: Trading-off Robustness and Computation via Adversarially-Trained Ensembles. CoRR abs/1811.09300 (2018) - [i24]Thomas Kipf, Yujia Li, Hanjun Dai, Vinícius Flores Zambaldi, Edward Grefenstette, Pushmeet Kohli, Peter W. Battaglia:
Compositional Imitation Learning: Explaining and executing one task at a time. CoRR abs/1812.01483 (2018) - 2017
- [i23]Dzmitry Bahdanau, Tom Bosc, Stanislaw Jastrzebski, Edward Grefenstette, Pascal Vincent, Yoshua Bengio:
Learning to Compute Word Embeddings On the Fly. CoRR abs/1706.00286 (2017) - [i22]Yishu Miao, Edward Grefenstette, Phil Blunsom:
Discovering Discrete Latent Topics with Neural Variational Inference. CoRR abs/1706.00359 (2017) - [i21]Richard Evans, Edward Grefenstette:
Learning Explanatory Rules from Noisy Data. CoRR abs/1711.04574 (2017) - [i20]Tomás Kociský, Jonathan Schwarz, Phil Blunsom, Chris Dyer, Karl Moritz Hermann, Gábor Melis, Edward Grefenstette:
The NarrativeQA Reading Comprehension Challenge. CoRR abs/1712.07040 (2017) - 2016
- [i19]Wang Ling, Edward Grefenstette, Karl Moritz Hermann, Tomás Kociský, Andrew W. Senior, Fumin Wang, Phil Blunsom:
Latent Predictor Networks for Code Generation. CoRR abs/1603.06744 (2016) - [i18]Tomás Kociský, Gábor Melis, Edward Grefenstette, Chris Dyer, Wang Ling, Phil Blunsom, Karl Moritz Hermann:
Semantic Parsing with Semi-Supervised Sequential Autoencoders. CoRR abs/1609.09315 (2016) - [i17]Lei Yu, Phil Blunsom, Chris Dyer, Edward Grefenstette, Tomás Kociský:
The Neural Noisy Channel. CoRR abs/1611.02554 (2016) - [i16]Dani Yogatama, Phil Blunsom, Chris Dyer, Edward Grefenstette, Wang Ling:
Learning to Compose Words into Sentences with Reinforcement Learning. CoRR abs/1611.09100 (2016) - 2015
- [i15]Edward Grefenstette, Karl Moritz Hermann, Mustafa Suleyman, Phil Blunsom:
Learning to Transduce with Unbounded Memory. CoRR abs/1506.02516 (2015) - [i14]Karl Moritz Hermann, Tomás Kociský, Edward Grefenstette, Lasse Espeholt, Will Kay, Mustafa Suleyman, Phil Blunsom:
Teaching Machines to Read and Comprehend. CoRR abs/1506.03340 (2015) - 2014
- [i13]Nal Kalchbrenner, Edward Grefenstette, Phil Blunsom:
A Convolutional Neural Network for Modelling Sentences. CoRR abs/1404.2188 (2014) - [i12]Edward Grefenstette, Phil Blunsom, Nando de Freitas, Karl Moritz Hermann:
A Deep Architecture for Semantic Parsing. CoRR abs/1404.7296 (2014) - [i11]Jianpeng Cheng, Dimitri Kartsaklis, Edward Grefenstette:
Investigating the Role of Prior Disambiguation in Deep-learning Compositional Models of Meaning. CoRR abs/1411.4116 (2014) - 2013
- [i10]Edward Grefenstette, Georgiana Dinu, Yao-Zhong Zhang, Mehrnoosh Sadrzadeh, Marco Baroni:
Multi-Step Regression Learning for Compositional Distributional Semantics. CoRR abs/1301.6939 (2013) - [i9]Bob Coecke, Edward Grefenstette, Mehrnoosh Sadrzadeh:
Lambek vs. Lambek: Functorial Vector Space Semantics and String Diagrams for Lambek Calculus. CoRR abs/1302.0393 (2013) - [i8]Edward Grefenstette:
Towards a Formal Distributional Semantics: Simulating Logical Calculi with Tensors. CoRR abs/1304.5823 (2013) - [i7]Stephen Clark, Bob Coecke, Edward Grefenstette, Stephen Pulman, Mehrnoosh Sadrzadeh:
A quantum teleportation inspired algorithm produces sentence meaning from word meaning and grammatical structure. CoRR abs/1305.0556 (2013) - [i6]Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom:
"Not not bad" is not "bad": A distributional account of negation. CoRR abs/1306.2158 (2013) - [i5]Edward Grefenstette:
Category-Theoretic Quantitative Compositional Distributional Models of Natural Language Semantics. CoRR abs/1311.1539 (2013) - 2011
- [i4]Edward Grefenstette, Mehrnoosh Sadrzadeh, Stephen Clark, Bob Coecke, Stephen Pulman:
Concrete Sentence Spaces for Compositional Distributional Models of Meaning. CoRR abs/1101.0309 (2011) - [i3]Mehrnoosh Sadrzadeh, Edward Grefenstette:
A Compositional Distributional Semantics, Two Concrete Constructions, and some Experimental Evaluations. CoRR abs/1105.1702 (2011) - [i2]Edward Grefenstette, Mehrnoosh Sadrzadeh:
Experimental Support for a Categorical Compositional Distributional Model of Meaning. CoRR abs/1106.4058 (2011) - [i1]Edward Grefenstette, Mehrnoosh Sadrzadeh:
Experimenting with Transitive Verbs in a DisCoCat. CoRR abs/1107.3119 (2011)
Coauthor Index
[j6] [c48] [c47] [c45] [c44] [c43] [c42] [c41] [c40] [c39] [c38] [c37] [c36] [c35] [c34] [c33] [c32] [c31] [c29] [c24] [c13] [p2] [p1] [i61] [i57] [i56] [i55] [i54] [i53] [i52] [i51] [i50] [i49] [i48] [i47] [i46] [i45] [i44] [i43] [i42] [i41] [i40] [i39] [i38] [i37] [i36] [i35] [i32] [i30] [i29] [i28]
a service of 
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-05-08 23:51 CEST by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
