Tomáš Kočiský
Tomáš Kočiský
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Cited by
Cited by
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
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
Reasoning About Entailment with Neural Attention
T Rocktäschel, E Grefenstette, KM Hermann, T Kočiský, P Blunsom
International Conference on Learning Representations (ICLR 2016), 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
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
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
Mogrifier lstm
G Melis, T Kočiský, P Blunsom
arXiv preprint arXiv:1909.01792, 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
Optimizing performance of recurrent neural networks on gpus
J Appleyard, T Kocisky, P Blunsom
arXiv preprint arXiv:1604.01946, 2016
Learning Bilingual Word Representations by Marginalizing Alignments
T Kočiský, KM Hermann, P Blunsom
Association for Computational Linguistics (ACL 2014), 2014
Aggregation and ordering in factorised databases
N Bakibayev, T Kočiský, D Olteanu, J Závodný
arXiv preprint arXiv:1307.0441, 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
The Neural Noisy Channel
L Yu, P Blunsom, C Dyer, E Grefenstette, T Kocisky
International Conference on Learning Representations (ICLR 2017), 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
Dynamic integration of background knowledge in neural nlu systems
D Weissenborn, T Kočiský, C Dyer
arXiv preprint arXiv:1706.02596, 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
Pitfalls of static language modelling
A Lazaridou, A Kuncoro, E Gribovskaya, D Agrawal, A Liska, T Terzi, ...
arXiv preprint arXiv:2102.01951, 2021
Reading comprehension neural networks
KM Hermann, T Kocisky, ET Grefenstette, L Espeholt, WT Kay, ...
US Patent 10,628,735, 2020
Pushing the Bounds of Dropout
G Melis, C Blundell, T Kočiský, KM Hermann, C Dyer, P Blunsom
arXiv preprint arXiv:1805.09208, 2018
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
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