Követés
Noga Zaslavsky
Noga Zaslavsky
Assistant Professor, NYU
E-mail megerősítve itt: nyu.edu - Kezdőlap
Cím
Hivatkozott rá
Hivatkozott rá
Év
Deep learning and the information bottleneck principle
N Tishby, N Zaslavsky
2015 IEEE Information Theory Workshop (ITW), 1-5, 2015
19702015
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
2622018
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
562019
Trading off Utility, Informativeness, and Complexity in Emergent Communication
M Tucker, R Levy, J Shah, N Zaslavsky
Neural Information Processing Systems (NeurIPS), 2022
54*2022
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
492021
A Rate-Distortion view of human pragmatic reasoning
N Zaslavsky, J Hu, RP Levy
Proceedings of the Society for Computation in Linguistics, 2020
442020
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
43*2022
Let's talk (efficiently) about us: Person systems achieve near-optimal compression
N Zaslavsky, M Maldonado, J Culbertson
CogSci 2021, 2021
392021
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
39*2020
Communicative need in colour naming
N Zaslavsky, C Kemp, N Tishby, T Regier
Cognitive Neuropsychology, 1-13, 2019
382019
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
342022
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
342019
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
262022
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
212024
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
202017
Probing artificial neural networks: insights from neuroscience
AA Ivanova, J Hewitt, N Zaslavsky
ICLR 2021 Brain2AI Workshop, 2021
192021
Information-Theoretic Principles in the Evolution of Semantic Systems
N Zaslavsky
Ph.D. Thesis, The Hebrew University of Jerusalem, 2020
112020
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
112017
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
102022
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
52022
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Cikkek 1–20