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Dieuwke Hupkes
Dieuwke Hupkes
Research Scientist at Facebook AI Research
Geverifieerd e-mailadres voor fb.com - Homepage
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Jaar
Visualisation and'diagnostic classifiers' reveal how recurrent and recursive neural networks process hierarchical structure
D Hupkes, S Veldhoen, W Zuidema
Journal of Artificial Intelligence Research 61, 907-926, 2018
1992018
Compositionality decomposed: How do neural networks generalise?
D Hupkes, V Dankers, M Mul, E Bruni
Journal of Artificial Intelligence Research 67, 757-795, 2020
150*2020
Under the hood: Using diagnostic classifiers to investigate and improve how language models track agreement information
M Giulianelli, J Harding, F Mohnert, D Hupkes, W Zuidema
arXiv preprint arXiv:1808.08079, 2018
1232018
The emergence of number and syntax units in LSTM language models
Y Lakretz, G Kruszewski, T Desbordes, D Hupkes, S Dehaene, M Baroni
arXiv preprint arXiv:1903.07435, 2019
1132019
Masked language modeling and the distributional hypothesis: Order word matters pre-training for little
K Sinha, R Jia, D Hupkes, J Pineau, A Williams, D Kiela
arXiv preprint arXiv:2104.06644, 2021
1072021
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
A Srivastava, A Rastogi, A Rao, AAM Shoeb, A Abid, A Fisch, AR Brown, ...
arXiv preprint arXiv:2206.04615, 2022
452022
Do language models understand anything? on the ability of lstms to understand negative polarity items
J Jumelet, D Hupkes
arXiv preprint arXiv:1808.10627, 2018
452018
Diagnostic classifiers revealing how neural networks process hierarchical structure
S Veldhoen, D Hupkes, WH Zuidema
CoCo@ NIPS, 2016
382016
Analysing neural language models: Contextual decomposition reveals default reasoning in number and gender assignment
J Jumelet, W Zuidema, D Hupkes
arXiv preprint arXiv:1909.08975, 2019
322019
Mechanisms for handling nested dependencies in neural-network language models and humans
Y Lakretz, D Hupkes, A Vergallito, M Marelli, M Baroni, S Dehaene
Cognition 213, 104699, 2021
30*2021
Transcoding compositionally: Using attention to find more generalizable solutions
K Korrel, D Hupkes, V Dankers, E Bruni
arXiv preprint arXiv:1906.01234, 2019
232019
Co-evolution of language and agents in referential games
G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:2001.03361, 2020
222020
Learning compositionally through attentive guidance
D Hupkes, A Singh, K Korrel, G Kruszewski, E Bruni
arXiv preprint arXiv:1805.09657, 2018
212018
The paradox of the compositionality of natural language: a neural machine translation case study
V Dankers, E Bruni, D Hupkes
arXiv preprint arXiv:2108.05885, 2021
162021
On the realization of compositionality in neural networks
J Baan, J Leible, M Nikolaus, D Rau, D Ulmer, T Baumgärtner, D Hupkes, ...
arXiv preprint arXiv:1906.01634, 2019
152019
Internal and external pressures on language emergence: least effort, object constancy and frequency
DR Luna, EM Ponti, D Hupkes, E Bruni
arXiv preprint arXiv:2004.03868, 2020
132020
Location attention for extrapolation to longer sequences
Y Dubois, G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:1911.03872, 2019
122019
POS-tagging of Historical Dutch
D Hupkes, R Bod
Proceedings of the Tenth International Conference on Language Resources and …, 2016
122016
Language models use monotonicity to assess NPI licensing
J Jumelet, M Denić, J Szymanik, D Hupkes, S Steinert-Threlkeld
arXiv preprint arXiv:2105.13818, 2021
102021
How bpe affects memorization in transformers
E Kharitonov, M Baroni, D Hupkes
arXiv preprint arXiv:2110.02782, 2021
82021
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Artikelen 1–20