Dieuwke Hupkes
Title
Cited by
Cited by
Year
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
992018
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
552018
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
452019
Compositionality Decomposed: How do Neural Networks Generalise?
D Hupkes, V Dankers, M Mul, E Bruni
Journal of Artificial Intelligence Research 67, 757-795, 2020
22*2020
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
182018
Diagnostic Classifiers Revealing how Neural Networks Process Hierarchical Structure.
S Veldhoen, D Hupkes, WH Zuidema
CoCo@ NIPS, 69-77, 2016
162016
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
112019
Transcoding compositionally: using attention to find more generalizable solutions
K Korrel, D Hupkes, V Dankers, E Bruni
arXiv preprint arXiv:1906.01234, 2019
112019
Learning compositionally through attentive guidance
D Hupkes, A Singh, K Korrel, G Kruszewski, E Bruni
arXiv preprint arXiv:1805.09657, 2018
92018
POS-tagging of Historical Dutch
D Hupkes, R Bod
Proceedings of the Tenth International Conference on Language Resources and …, 2016
62016
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
52019
Diagnostic classification and symbolic guidance to understand and improve recurrent neural networks
D Hupkes, W Zuidema
Proceedings Workshop on Interpreting, Explaining and Visualizing Deep …, 2017
52017
Location attention for extrapolation to longer sequences
Y Dubois, G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:1911.03872, 2019
42019
Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue
D Hupkes, S Bouwmeester, R Fernández
arXiv preprint arXiv:1808.09178, 2018
42018
Co-evolution of language and agents in referential games
G Dagan, D Hupkes, E Bruni
arXiv preprint arXiv:2001.03361, 2020
32020
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
22020
Assessing incrementality in sequence-to-sequence models
D Ulmer, D Hupkes, E Bruni
arXiv preprint arXiv:1906.03293, 2019
22019
The Fast and the Flexible: training neural networks to learn to follow instructions from small data
R Leonandya, E Bruni, D Hupkes, G Kruszewski
arXiv preprint arXiv:1809.06194, 2018
22018
Formal models of structure building in music, language and animal song
W Zuidema, D Hupkes, G Wiggins, C Scharff, M Rohrmeier
The origins of musicality, 253, 2018
12018
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP
A Alishahi, Y Belinkov, G Chrupała, D Hupkes, Y Pinter, H Sajjad
Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting …, 2020
2020
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Articles 1–20