| Deep learning and the information bottleneck principle N Tishby, N Zaslavsky 2015 IEEE Information Theory Workshop (ITW), 1-5, 2015 | 1671 | 2015 |
| Efficient compression in color naming and its evolution N Zaslavsky, C Kemp, T Regier, N Tishby Proceedings of the National Academy of Sciences 115 (31), 7937-7942, 2018 | 237 | 2018 |
| Color Naming Reflects Both Perceptual Structure and Communicative Need N Zaslavsky, C Kemp, N Tishby, T Regier Topics in Cognitive Science 11 (1), 207-219, 2019 | 49 | 2019 |
| The forms and meanings of grammatical markers support efficient communication F Mollica, G Bacon, N Zaslavsky, Y Xu, T Regier, C Kemp Proceedings of the National Academy of Sciences 118 (49), 2021 | 40 | 2021 |
| A Rate-Distortion view of human pragmatic reasoning N Zaslavsky, J Hu, RP Levy Proceedings of the Society for Computation in Linguistics, 2020 | 40 | 2020 |
| Trading off Utility, Informativeness, and Complexity in Emergent Communication M Tucker, R Levy, J Shah, N Zaslavsky Neural Information Processing Systems (NeurIPS), 2022 | 37* | 2022 |
| Communicative need in colour naming N Zaslavsky, C Kemp, N Tishby, T Regier Cognitive Neuropsychology, 1-13, 2019 | 37 | 2019 |
| Let's talk (efficiently) about us: Person systems achieve near-optimal compression N Zaslavsky, M Maldonado, J Culbertson CogSci 2021, 2021 | 36 | 2021 |
| Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction T Eisape, N Zaslavsky, R Levy Proceedings of the 24th Conference on Computational Natural Language …, 2020 | 34* | 2020 |
| Beyond linear regression: mapping models in cognitive neuroscience should align with research goals AA Ivanova, M Schrimpf, S Anzellotti, N Zaslavsky, E Fedorenko, L Isik Neurons, Behavior, Data analysis, and Theory (NBDT), 2022 | 32* | 2022 |
| Semantic categories of artifacts and animals reflect efficient coding N Zaslavsky, T Regier, N Tishby, C Kemp 41st Annual Meeting of the Cognitive Science Society, 2019 | 32 | 2019 |
| Artificial neural network language models align neurally and behaviorally with humans even after a developmentally realistic amount of training EA Hosseini, M Schrimpf, Y Zhang, S Bowman, N Zaslavsky, E Fedorenko BioRxiv, 2022.10. 04.510681, 2022 | 30 | 2022 |
| The evolution of color naming reflects pressure for efficiency: Evidence from the recent past N Zaslavsky, K Garvin, C Kemp, N Tishby, T Regier Journal of Language Evolution, 2022 | 25 | 2022 |
| Efficient encoding of motion is mediated by gap junctions in the fly visual system S Wang, A Borst, N Zaslavsky, N Tishby, I Segev PLoS Computational Biology 13 (12), e1005846, 2017 | 19 | 2017 |
| Probing artificial neural networks: insights from neuroscience AA Ivanova, J Hewitt, N Zaslavsky ICLR 2021 Brain2AI Workshop, 2021 | 14 | 2021 |
| Information-Theoretic Principles in the Evolution of Semantic Systems N Zaslavsky Ph.D. Thesis, The Hebrew University of Jerusalem, 2020 | 11 | 2020 |
| Efficient human-like semantic representations via the Information Bottleneck principle N Zaslavsky, C Kemp, T Regier, N Tishby NeuIPS 2017 Cognitively Informed AI workshop, 2017 | 11 | 2017 |
| Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training EA Hosseini, M Schrimpf, Y Zhang, S Bowman, N Zaslavsky, E Fedorenko Neurobiology of Language 5 (1), 43-63, 2024 | 10 | 2024 |
| Generalization and Translatability in Emergent Communication via Informational Constraints M Tucker, R Levy, J Shah, N Zaslavsky NeurIPS 2022 Workshop on Information-Theoretic Principles in Cognitive Systems, 2022 | 5 | 2022 |
| Teasing apart models of pragmatics using optimal reference game design I Zhou, J Hu, R Levy, N Zaslavsky Proceedings of the Annual Meeting of the Cognitive Science Society 44 (44), 2022 | 5 | 2022 |
Charles KempMelbourne School of Psychological Sciences在 unimelb.edu.au 的电子邮件经过验证
