| A convolutional neural network for modelling sentences N Kalchbrenner, E Grefenstette, P Blunsom arXiv preprint arXiv:1404.2188, 2014 | 5049 | 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 | 4145 | 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 | 1991 | 2016 |
| Reasoning about entailment with neural attention T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom arXiv preprint arXiv:1509.06664, 2015 | 906 | 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 | 753 | 2018 |
| Learning explanatory rules from noisy data R Evans, E Grefenstette Journal of Artificial Intelligence Research 61, 1-64, 2018 | 609 | 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 | 458 | 2016 |
| Analysing mathematical reasoning abilities of neural models D Saxton, E Grefenstette, F Hill, P Kohli arXiv preprint arXiv:1904.01557, 2019 | 443 | 2019 |
| Experimental support for a categorical compositional distributional model of meaning E Grefenstette, M Sadrzadeh arXiv preprint arXiv:1106.4058, 2011 | 402 | 2011 |
| 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 | 387 | 2023 |
| Discovering discrete latent topics with neural variational inference Y Miao, E Grefenstette, P Blunsom International conference on machine learning, 2410-2419, 2017 | 382 | 2017 |
| Learning to transduce with unbounded memory E Grefenstette, KM Hermann, M Suleyman, P Blunsom Advances in neural information processing systems 28, 2015 | 356 | 2015 |
| 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 | 321 | 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 | 206 | 2016 |
| 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 | 201* | 2018 |
| The nethack learning environment H Küttler, N Nardelli, A Miller, R Raileanu, M Selvatici, E Grefenstette, ... Advances in Neural Information Processing Systems 33, 7671-7684, 2020 | 184 | 2020 |
| 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 | 177 | 2019 |
| Prioritized level replay M Jiang, E Grefenstette, T Rocktäschel International Conference on Machine Learning, 4940-4950, 2021 | 170 | 2021 |
| Compile: Compositional imitation learning and execution T Kipf, Y Li, H Dai, V Zambaldi, A Sanchez-Gonzalez, E Grefenstette, ... International Conference on Machine Learning, 3418-3428, 2019 | 160* | 2019 |
| Learning with amigo: Adversarially motivated intrinsic goals A Campero, R Raileanu, H Küttler, JB Tenenbaum, T Rocktäschel, ... arXiv preprint arXiv:2006.12122, 2020 | 159 | 2020 |
Phil BlunsomCohere & St Hugh’s College OxfordVerified email at cs.ox.ac.uk
