| 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 | 406 | 2019 |
| 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 | 335 | 2012 |
| 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 | 161 | 2019 |
| 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 | 90 | 2011 |
| Inducing brain-relevant bias in natural language processing models D Schwartz, M Toneva, L Wehbe Neural Information Processing Systems 33 (33), 2019 | 69 | 2019 |
| 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 | 36 | 2022 |
| 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 | 20 | 2020 |
| Language models and brain alignment: beyond word-level semantics and prediction G Merlin, M Toneva arXiv preprint arXiv:2212.00596, 2022 | 12 | 2022 |
| Same cause; different effects in the brain M Toneva, J Williams, A Bollu, C Dann, L Wehbe arXiv preprint arXiv:2202.10376, 2022 | 11 | 2022 |
| 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 | 10 | 2014 |
| 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 | 9 | 2021 |
| 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 | 9 | 2015 |
| Training language models to summarize narratives improves brain alignment KL Aw, M Toneva Eleventh International Conference on Learning Representations, 2023 | 8* | 2023 |
| Joint processing of linguistic properties in brains and language models SR Oota, M Gupta, M Toneva arXiv preprint arXiv:2212.08094, 2022 | 7 | 2022 |
| Large language models can segment narrative events similarly to humans S Michelmann, M Kumar, KA Norman, M Toneva arXiv preprint arXiv:2301.10297, 2023 | 3 | 2023 |
| Proceedings of the 33rd Annual Conference of the Cognitive Science Society H Admoni, C Bank, J Tan, M Toneva, B Scassellati | 3 | 2011 |
| 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 | 2 | 2021 |
| Interpreting Multimodal Video Transformers Using Brain Recordings DT Dong, M Toneva ICLR 2023 Workshop on Multimodal Representation Learning: Perks and Pitfalls, 2023 | 1 | 2023 |
| 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 | 1 | 2022 |
| Bridging Language in Machines with Language in the Brain M Toneva Carnegie Mellon University, 2021 | 1 | 2021 |
Leila WehbeCarnegie Mellon UniversityVerified email at cmu.edu
