Követés
Felix Hill
Felix Hill
Research Scientist, Deepmind
E-mail megerősítve itt: google.com - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Glue: A multi-task benchmark and analysis platform for natural language understanding
A Wang
arXiv preprint arXiv:1804.07461, 2018
80682018
Superglue: A stickier benchmark for general-purpose language understanding systems
A Wang, Y Pruksachatkun, N Nangia, A Singh, J Michael, F Hill, O Levy, ...
Advances in neural information processing systems 32, 2019
24702019
Simlex-999: Evaluating semantic models with (genuine) similarity estimation
F Hill, R Reichart, A Korhonen
Computational Linguistics 41 (4), 665-695, 2015
16342015
Multimodal few-shot learning with frozen language models
M Tsimpoukelli, JL Menick, S Cabi, SM Eslami, O Vinyals, F Hill
Advances in Neural Information Processing Systems 34, 200-212, 2021
7852021
The goldilocks principle: Reading children's books with explicit memory representations
F Hill, A Bordes, S Chopra, J Weston
arXiv preprint arXiv:1511.02301, 2015
7612015
Learning distributed representations of sentences from unlabelled data
F Hill, K Cho, A Korhonen
arXiv preprint arXiv:1602.03483, 2016
7382016
Analysing mathematical reasoning abilities of neural models
D Saxton, E Grefenstette, F Hill, P Kohli
arXiv preprint arXiv:1904.01557, 2019
4582019
Measuring abstract reasoning in neural networks
A Santoro, F Hill, D Barrett, A Morcos, T Lillicrap
International conference on machine learning, 4477-4486, 2018
390*2018
Grounded language learning in a simulated 3d world
KM Hermann, F Hill, S Green, F Wang, R Faulkner, H Soyer, D Szepesvari, ...
arXiv preprint arXiv:1706.06551, 2017
351*2017
Simverb-3500: A large-scale evaluation set of verb similarity
D Gerz, I Vulić, F Hill, R Reichart, A Korhonen
arXiv preprint arXiv:1608.00869, 2016
3262016
Data distributional properties drive emergent in-context learning in transformers
S Chan, A Santoro, A Lampinen, J Wang, A Singh, P Richemond, ...
Advances in Neural Information Processing Systems 35, 18878-18891, 2022
2892022
Can language models learn from explanations in context?
AK Lampinen, I Dasgupta, SCY Chan, K Matthewson, MH Tessler, ...
arXiv preprint arXiv:2204.02329, 2022
2872022
Neural arithmetic logic units
A Trask, F Hill, SE Reed, J Rae, C Dyer, P Blunsom
Advances in neural information processing systems 31, 2018
2592018
Learning to understand phrases by embedding the dictionary
F Hill, K Cho, A Korhonen, Y Bengio
Transactions of the Association for Computational Linguistics 4, 17-30, 2016
2382016
Learning to understand goal specifications by modelling reward
D Bahdanau, F Hill, J Leike, E Hughes, A Hosseini, P Kohli, ...
arXiv preprint arXiv:1806.01946, 2018
210*2018
Specializing word embeddings for similarity or relatedness
D Kiela, F Hill, S Clark
Proceedings of the 2015 conference on empirical methods in natural language …, 2015
1862015
Language models show human-like content effects on reasoning
I Dasgupta, AK Lampinen, SCY Chan, A Creswell, D Kumaran, ...
arXiv preprint arXiv:2207.07051 2 (3), 2022
1782022
Placing language in an integrated understanding system: Next steps toward human-level performance in neural language models
JL McClelland, F Hill, M Rudolph, J Baldridge, H Schütze
Proceedings of the National Academy of Sciences 117 (42), 25966-25974, 2020
149*2020
The representational geometry of word meanings acquired by neural machine translation models
F Hill, K Cho, S Jean, Y Bengio
Machine Translation 31, 3-18, 2017
149*2017
Environmental drivers of systematicity and generalization in a situated agent
F Hill, A Lampinen, R Schneider, S Clark, M Botvinick, JL McClelland, ...
arXiv preprint arXiv:1910.00571, 2019
137*2019
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Cikkek 1–20