フォロー
Mariya Toneva
タイトル
引用先
引用先
An Empirical Study of Example Forgetting during Deep Neural Network Learning
M Toneva, A Sordoni, R Tachet des Combes, A Trischler, Y Bengio, ...
International Conference on Learning Representations, 2019
6382019
The physical presence of a robot tutor increases cognitive learning gains
D Leyzberg, S Spaulding, M Toneva, B Scassellati
Proceedings of the annual meeting of the cognitive science society 34 (34), 2012
3672012
Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain)
M Toneva, L Wehbe
Neural Information Processing Systems 33 (33), 2019
2142019
Robot gaze does not reflexively cue human attention
H Admoni, C Bank, J Tan, M Toneva, B Scassellati
Proceedings of the Annual Meeting of the Cognitive Science Society 33 (33), 2011
922011
Inducing brain-relevant bias in natural language processing models
D Schwartz, M Toneva, L Wehbe
Neural Information Processing Systems 33 (33), 2019
852019
Combining computational controls with natural text reveals aspects of meaning composition
M Toneva, TM Mitchell, L Wehbe
Nature computational science 2 (11), 745-757, 2022
522022
Getting aligned on representational alignment
I Sucholutsky, L Muttenthaler, A Weller, A Peng, A Bobu, B Kim, BC Love, ...
arXiv preprint arXiv:2310.13018, 2023
242023
Language models and brain alignment: beyond word-level semantics and prediction
G Merlin, M Toneva
arXiv preprint arXiv:2212.00596, 2022
222022
Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction
M Toneva, O Stretcu, B Poczos, L Wehbe, TM Mitchell
Neural Information Processing Systems 34 (34), 2020
222020
Training language models to summarize narratives improves brain alignment
KL Aw, M Toneva
arXiv preprint arXiv:2212.10898, 2022
16*2022
Joint processing of linguistic properties in brains and language models
SR Oota, M Gupta, M Toneva
Advances in Neural Information Processing Systems 36, 2024
142024
Large language models can segment narrative events similarly to humans
S Michelmann, M Kumar, KA Norman, M Toneva
arXiv preprint arXiv:2301.10297, 2023
122023
Same cause; different effects in the brain
M Toneva, J Williams, A Bollu, C Dann, L Wehbe
arXiv preprint arXiv:2202.10376, 2022
122022
Does injecting linguistic structure into language models lead to better alignment with brain recordings?
M Abdou, AV González, M Toneva, D Hershcovich, A Søgaard
arXiv preprint arXiv:2101.12608, 2021
122021
An exploration of social grouping in robots: Effects of behavioral mimicry, appearance, and eye gaze
A Nawroj, M Toneva, H Admoni, B Scassellati
Proceedings of the Annual Meeting of the Cognitive Science Society 36 (36), 2014
112014
Applying artificial vision models to human scene understanding
EM Aminoff, M Toneva, A Shrivastava, X Chen, I Misra, A Gupta, MJ Tarr
Frontiers in computational neuroscience 9, 8, 2015
92015
Bridging Language in Machines with Language in the Brain
M Toneva
Carnegie Mellon University, 2021
52021
Interpreting multimodal video transformers using brain recordings
DT Dong, M Toneva
ICLR 2023 Workshop on Multimodal Representation Learning: Perks and Pitfalls, 2023
42023
A roadmap to reverse engineering real-world generalization by combining naturalistic paradigms, deep sampling, and predictive computational models
P Herholz, E Fortier, M Toneva, N Farrugia, L Wehbe, V Borghesani
arXiv preprint arXiv:2108.10231, 2021
42021
Deep learning for brain encoding and decoding
SR Oota, J Arora, M Gupta, RS Bapi, M Toneva
Proceedings of the Annual Meeting of the Cognitive Science Society 44 (44), 2022
32022
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