Edward Grefenstette
Edward Grefenstette
Head of Machine Learning, Cohere; Honorary Professor, UCL
Verified email at - Homepage
Cited by
Cited by
A convolutional neural network for modelling sentences
N Kalchbrenner, E Grefenstette, P Blunsom
arXiv preprint arXiv:1404.2188, 2014
Teaching machines to read and comprehend
KM Hermann, T Kocisky, E Grefenstette, L Espeholt, W Kay, M Suleyman, ...
Advances in neural information processing systems 28, 2015
Hybrid computing using a neural network with dynamic external memory
A Graves, G Wayne, M Reynolds, T Harley, I Danihelka, ...
Nature 538 (7626), 471-476, 2016
Reasoning about entailment with neural attention
T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom
arXiv preprint arXiv:1509.06664, 2015
The narrativeqa reading comprehension challenge
T Kočiský, J Schwarz, P Blunsom, C Dyer, KM Hermann, G Melis, ...
Transactions of the Association for Computational Linguistics 6, 317-328, 2018
Learning explanatory rules from noisy data
R Evans, E Grefenstette
Journal of Artificial Intelligence Research 61, 1-64, 2018
Latent predictor networks for code generation
W Ling, E Grefenstette, KM Hermann, T Kočiský, A Senior, F Wang, ...
arXiv preprint arXiv:1603.06744, 2016
Experimental support for a categorical compositional distributional model of meaning
E Grefenstette, M Sadrzadeh
arXiv preprint arXiv:1106.4058, 2011
Learning to transduce with unbounded memory
E Grefenstette, KM Hermann, M Suleyman, P Blunsom
Advances in neural information processing systems 28, 2015
Discovering discrete latent topics with neural variational inference
Y Miao, E Grefenstette, P Blunsom
International Conference on Machine Learning, 2410-2419, 2017
Analysing mathematical reasoning abilities of neural models
D Saxton, E Grefenstette, F Hill, P Kohli
arXiv preprint arXiv:1904.01557, 2019
A survey of reinforcement learning informed by natural language
J Luketina, N Nardelli, G Farquhar, J Foerster, J Andreas, E Grefenstette, ...
arXiv preprint arXiv:1906.03926, 2019
Learning to compose words into sentences with reinforcement learning
D Yogatama, P Blunsom, C Dyer, E Grefenstette, W Ling
arXiv preprint arXiv:1611.09100, 2016
Multi-step regression learning for compositional distributional semantics
E Grefenstette, G Dinu, YZ Zhang, M Sadrzadeh, M Baroni
arXiv preprint arXiv:1301.6939, 2013
Learning to Understand Goal Specifications by Modelling Reward
D Bahdanau, F Hill, J Leike, E Hughes, P Kohli, E Grefenstette
arXiv preprint arXiv:1806.01946, 2018
A Survey of Zero-shot Generalisation in Deep Reinforcement Learning
R Kirk, A Zhang, E Grefenstette, T Rocktäschel
Journal of Artificial Intelligence Research 76, 201-264, 2023
Can neural networks understand logical entailment?
R Evans, D Saxton, D Amos, P Kohli, E Grefenstette
arXiv preprint arXiv:1802.08535, 2018
Generalized inner loop meta-learning
E Grefenstette, B Amos, D Yarats, PM Htut, A Molchanov, F Meier, D Kiela, ...
arXiv preprint arXiv:1910.01727, 2019
Towards a formal distributional semantics: Simulating logical calculi with tensors
E Grefenstette
arXiv preprint arXiv:1304.5823, 2013
Lambek vs. Lambek: Functorial vector space semantics and string diagrams for Lambek calculus
B Coecke, E Grefenstette, M Sadrzadeh
Annals of pure and applied logic 164 (11), 1079-1100, 2013
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