| 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 | 4053 | 2015 |
| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ... arXiv preprint arXiv:2312.11805, 2023 | 1480 | 2023 |
| Reasoning About Entailment with Neural Attention T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom International Conference on Learning Representations (ICLR 2016), 2015 | 899 | 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 | 717 | 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 | 449 | 2016 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ... arXiv preprint arXiv:2403.05530, 2024 | 350 | 2024 |
| Mogrifier lstm G Melis, T Kočiský, P Blunsom arXiv preprint arXiv:1909.01792, 2019 | 169 | 2019 |
| 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 | 128 | 2021 |
| Optimizing performance of recurrent neural networks on gpus J Appleyard, T Kocisky, P Blunsom arXiv preprint arXiv:1604.01946, 2016 | 113 | 2016 |
| Learning Bilingual Word Representations by Marginalizing Alignments T Kočiský, KM Hermann, P Blunsom Association for Computational Linguistics (ACL 2014), 2014 | 106 | 2014 |
| Aggregation and ordering in factorised databases N Bakibayev, T Kočiský, D Olteanu, J Závodný arXiv preprint arXiv:1307.0441, 2013 | 104 | 2013 |
| 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 | 99 | 2016 |
| The Neural Noisy Channel L Yu, P Blunsom, C Dyer, E Grefenstette, T Kocisky International Conference on Learning Representations (ICLR 2017), 2016 | 77 | 2016 |
| 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 | 73 | 2019 |
| Dynamic integration of background knowledge in neural nlu systems D Weissenborn, T Kočiský, C Dyer arXiv preprint arXiv:1706.02596, 2017 | 65 | 2017 |
| 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 | 47 | 2022 |
| Gemma 2: Improving open language models at a practical size G Team, M Riviere, S Pathak, PG Sessa, C Hardin, S Bhupatiraju, ... arXiv preprint arXiv:2408.00118, 2024 | 39 | 2024 |
| Pitfalls of static language modelling A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ... arXiv preprint arXiv:2102.01951, 2021 | 30 | 2021 |
| Reading comprehension neural networks KM Hermann, T Kocisky, ET Grefenstette, L Espeholt, WT Kay, ... US Patent 10,628,735, 2020 | 25 | 2020 |
| Pushing the Bounds of Dropout G Melis, C Blundell, T Kočiský, KM Hermann, C Dyer, P Blunsom arXiv preprint arXiv:1805.09208, 2018 | 15 | 2018 |
Phil BlunsomCohere & Oxford UniversityVerified email at cs.ox.ac.uk
