| A convolutional neural network for modelling sentences N Kalchbrenner, E Grefenstette, P Blunsom arXiv preprint arXiv:1404.2188, 2014 | 3993 | 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 | 2762 | 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 | 1416 | 2016 |
| Reasoning about entailment with neural attention T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom arXiv preprint arXiv:1509.06664, 2015 | 766 | 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 | 355 | 2018 |
| Experimental support for a categorical compositional distributional model of meaning E Grefenstette, M Sadrzadeh arXiv preprint arXiv:1106.4058, 2011 | 353 | 2011 |
| Learning explanatory rules from noisy data R Evans, E Grefenstette Journal of Artificial Intelligence Research 61, 1-64, 2018 | 352 | 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 | 305 | 2016 |
| Learning to transduce with unbounded memory E Grefenstette, KM Hermann, M Suleyman, P Blunsom Advances in neural information processing systems 28, 2015 | 291 | 2015 |
| Discovering discrete latent topics with neural variational inference Y Miao, E Grefenstette, P Blunsom International Conference on Machine Learning, 2410-2419, 2017 | 207 | 2017 |
| Analysing mathematical reasoning abilities of neural models D Saxton, E Grefenstette, F Hill, P Kohli arXiv preprint arXiv:1904.01557, 2019 | 166 | 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 | 161 | 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 | 148 | 2013 |
| 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 | 116 | 2019 |
| 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 | 110* | 2018 |
| Towards a formal distributional semantics: Simulating logical calculi with tensors E Grefenstette arXiv preprint arXiv:1304.5823, 2013 | 106 | 2013 |
| Concrete sentence spaces for compositional distributional models of meaning E Grefenstette, M Sadrzadeh, S Clark, B Coecke, S Pulman Proceedings of the 9th International Conference on Computational Semantics …, 2011 | 106 | 2011 |
| Can neural networks understand logical entailment? R Evans, D Saxton, D Amos, P Kohli, E Grefenstette arXiv preprint arXiv:1802.08535, 2018 | 104 | 2018 |
| 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 | 95 | 2013 |
| Semantic parsing with semi-supervised sequential autoencoders T Kočiský, G Melis, E Grefenstette, C Dyer, W Ling, P Blunsom, ... arXiv preprint arXiv:1609.09315, 2016 | 82 | 2016 |
Phil BlunsomOxford UniversityVerified email at cs.ox.ac.uk
