| A convolutional neural network for modelling sentences N Kalchbrenner, E Grefenstette, P Blunsom arXiv preprint arXiv:1404.2188, 2014 | 4539 | 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 | 3353 | 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 | 1657 | 2016 |
| Reasoning about entailment with neural attention T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom arXiv preprint arXiv:1509.06664, 2015 | 849 | 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 | 481 | 2018 |
| Learning explanatory rules from noisy data R Evans, E Grefenstette Journal of Artificial Intelligence Research 61, 1-64, 2018 | 456 | 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 | 375 | 2016 |
| Experimental support for a categorical compositional distributional model of meaning E Grefenstette, M Sadrzadeh arXiv preprint arXiv:1106.4058, 2011 | 372 | 2011 |
| Learning to transduce with unbounded memory E Grefenstette, KM Hermann, M Suleyman, P Blunsom Advances in neural information processing systems 28, 2015 | 310 | 2015 |
| Discovering discrete latent topics with neural variational inference Y Miao, E Grefenstette, P Blunsom International Conference on Machine Learning, 2410-2419, 2017 | 275 | 2017 |
| Analysing mathematical reasoning abilities of neural models D Saxton, E Grefenstette, F Hill, P Kohli arXiv preprint arXiv:1904.01557, 2019 | 255 | 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 | 205 | 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 | 179 | 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 | 150 | 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 | 149* | 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 | 132* | 2023 |
| Can neural networks understand logical entailment? R Evans, D Saxton, D Amos, P Kohli, E Grefenstette arXiv preprint arXiv:1802.08535, 2018 | 129 | 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 | 115 | 2019 |
| Towards a formal distributional semantics: Simulating logical calculi with tensors E Grefenstette arXiv preprint arXiv:1304.5823, 2013 | 113 | 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 | 108 | 2013 |
Phil BlunsomOxford University & CohereVerified email at cs.ox.ac.uk
