Noga Zaslavsky

Orcid: 0000-0003-3941-3518

According to our database1, Noga Zaslavsky authored at least 17 papers between 2015 and 2023.

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Bibliography

2023
Human-Guided Complexity-Controlled Abstractions.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Towards Human-Agent Communication via the Information Bottleneck Principle.
CoRR, 2022

Trading off Utility, Informativeness, and Complexity in Emergent Communication.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

The information geometry of pragmatic reasoning.
Proceedings of the International Symposium on Artificial Intelligence and Mathematics 2022 (ISAIM 2022), 2022

2021
Scalable pragmatic communication via self-supervision.
CoRR, 2021

Probing artificial neural networks: insights from neuroscience.
CoRR, 2021

2020
A Rate-Distortion view of human pragmatic reasoning.
CoRR, 2020

Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction.
Proceedings of the 24th Conference on Computational Natural Language Learning, 2020

2019
Color Naming Reflects Both Perceptual Structure and Communicative Need.
Top. Cogn. Sci., 2019

Semantic categories of artifacts and animals reflect efficient coding.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Communicative need and color naming.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

Evolution and efficiency in color naming: The case of Nafaanra.
Proceedings of the 41th Annual Meeting of the Cognitive Science Society, 2019

2018
Efficient compression in color naming and its evolution.
Proc. Natl. Acad. Sci. USA, 2018

Efficient human-like semantic representations via the Information Bottleneck principle.
CoRR, 2018

Information-theoretic efficiency and semantic variation: The case of color naming.
Proceedings of the 40th Annual Meeting of the Cognitive Science Society, 2018

2017
Efficient encoding of motion is mediated by gap junctions in the fly visual system.
PLoS Comput. Biol., 2017

2015
Deep learning and the information bottleneck principle.
Proceedings of the 2015 IEEE Information Theory Workshop, 2015


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