| Teaching Machines to Read and Comprehend KM Hermann, T Kočiský, E Grefenstette, L Espeholt, W Kay, M Suleyman, ... Advances in Neural Information Processing Systems 28 (NIPS 2015), 2015 | 3652 | 2015 |
| Reasoning About Entailment with Neural Attention T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom International Conference on Learning Representations (ICLR 2016), 2015 | 867 | 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 (TACL 2018) 6 …, 2017 | 556 | 2017 |
| Latent Predictor Networks for Code Generation W Ling, E Grefenstette, KM Hermann, T Kočiský, A Senior, F Wang, ... Association for Computational Linguistics (ACL 2016), 2016 | 402 | 2016 |
| Mogrifier lstm G Melis, T Kočiský, P Blunsom arXiv preprint arXiv:1909.01792, 2019 | 122 | 2019 |
| Learning Bilingual Word Representations by Marginalizing Alignments T Kočiský, KM Hermann, P Blunsom Association for Computational Linguistics (ACL 2014), 2014 | 103 | 2014 |
| Optimizing performance of recurrent neural networks on gpus J Appleyard, T Kocisky, P Blunsom arXiv preprint arXiv:1604.01946, 2016 | 102 | 2016 |
| Semantic Parsing with Semi-Supervised Sequential Autoencoders T Kočiský, G Melis, E Grefenstette, C Dyer, W Ling, P Blunsom, ... Conference on Empirical Methods in Natural Language Processing (EMNLP 2016), 2016 | 97 | 2016 |
| Aggregation and ordering in factorised databases N Bakibayev, T Kočiský, D Olteanu, J Závodný arXiv preprint arXiv:1307.0441, 2013 | 94 | 2013 |
| The Neural Noisy Channel L Yu, P Blunsom, C Dyer, E Grefenstette, T Kocisky International Conference on Learning Representations (ICLR 2017), 2016 | 76 | 2016 |
| Mind the gap: Assessing temporal generalization in neural language models A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ... Advances in Neural Information Processing Systems 34, 29348-29363, 2021 | 69 | 2021 |
| Learning and evaluating general linguistic intelligence D Yogatama, CM d'Autume, J Connor, T Kocisky, M Chrzanowski, L Kong, ... arXiv preprint arXiv:1901.11373, 2019 | 63 | 2019 |
| Dynamic integration of background knowledge in neural nlu systems D Weissenborn, T Kočiský, C Dyer arXiv preprint arXiv:1706.02596, 2017 | 62 | 2017 |
| Pitfalls of static language modelling A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ... arXiv preprint arXiv:2102.01951, 2021 | 25 | 2021 |
| Reading comprehension neural networks KM Hermann, T Kocisky, ET Grefenstette, L Espeholt, WT Kay, ... US Patent 10,628,735, 2020 | 23 | 2020 |
| Encoding Spatial Relations from Natural Language T Ramalho, T Kočiský, F Besse, SM Eslami, G Melis, F Viola, P Blunsom, ... arXiv preprint arXiv:1807.01670, 2018 | 14 | 2018 |
| Pushing the Bounds of Dropout G Melis, C Blundell, T Kočiský, KM Hermann, C Dyer, P Blunsom arXiv preprint arXiv:1805.09208, 2018 | 14 | 2018 |
| Streamingqa: A benchmark for adaptation to new knowledge over time in question answering models A Liska, T Kocisky, E Gribovskaya, T Terzi, E Sezener, D Agrawal, ... International Conference on Machine Learning, 13604-13622, 2022 | 13 | 2022 |
| Towards coherent and consistent use of entities in narrative generation P Papalampidi, K Cao, T Kocisky International Conference on Machine Learning, 17278-17294, 2022 | 8 | 2022 |
| Learning to encode spatial relations from natural language T Ramalho, T Kocisky, F Besse, SMA Eslami, G Melis, F Viola, P Blunsom, ... | 3 | 2018 |
Phil BlunsomOxford University & CohereVerified email at cs.ox.ac.uk
