Noga Zaslavsky
Noga Zaslavsky
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Towards Human-Agent Communication via the Information Bottleneck Principle
Mycal Tucker, Julie Shah, Roger Levy, Noga Zaslavsky.
RSS Workshop on Social Intelligence in Humans and Robots
, 2022.
RSS Workshop
Teasing apart models of pragmatics using optimal reference game design
Irene Zhou, Jennifer Hu, Roger Levy, Noga Zaslavsky.
CogSci
, 2022.
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Proceedings
The emergence of discrete and systematic communication in a continuous signal-meaning space
Alicia Chen, Matthias Hofer, Moshe Poliak, Roger Levy, Noga Zaslavsky.
CogSci
, 2022.
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Proceedings
The evolution of color naming reflects pressure for efficiency: Evidence from the recent past
Noga Zaslavsky*, Karee Garvin*, Charles Kemp, Naftali Tishby, Terry Regier.
Journal of Language Evolution
, 2022.
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The forms and meanings of grammatical markers support efficient communication
Francis Mollica, Geoffrey Bacon, Noga Zaslavsky, Yang Xu, Terry Regier, Charles Kemp.
PNAS
, 2021.
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DOI
Preprint
Scalable pragmatic communication via self-supervision
Jennifer Hu, Roger Levy, Noga Zaslavsky.
ICML Workshop on Self-Supervised Learning for Reasoning and Perception
, 2021.
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ICML Workshop
Let's talk (efficiently) about us: Person systems achieve near-optimal compression
Noga Zaslavsky*, Mora Maldonado*, Jennifer Culbertson.
CogSci
, 2021.
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Empirical support for a Rate-Distortion account of pragmatic reasoning
Irene Zhou, Jennifer Hu, Roger P. Levy, Noga Zaslavsky.
CogSci
, 2021.
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Competition from novel features drives scalar inferences in reference games
Jennifer Hu, Noga Zaslavsky, Roger P. Levy.
CogSci
, 2021.
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Code
Is it that simple? Linear mapping models in cognitive neuroscience
Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik.
bioRxiv
, 2021.
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DOI
Probing artificial neural networks: insights from neuroscience
Anna A. Ivanova, John Hewitt, Noga Zaslavsky.
ICLR Brain2AI Workshop
, 2021.
PDF
Crosslinguistic patterns in person systems reflect efficient coding
Mora Maldonado*, Noga Zaslavsky*, Jennifer Culbertson.
CUNY
, 2021.
PDF
A Rate–Distortion view of human pragmatic reasoning
Noga Zaslavsky, Jennifer Hu, Roger Levy.
SCiL
, 2021.
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DOI
Bayesian Approaches to Color Category Learning
Thomas Griffiths, Noga Zaslavsky.
Encyclopedia of Color Science and Technology
, 2021.
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DOI
Cloze Distillation: Improving Neural Language Models with Human Next-Word Prediction
Tiwalayo Eisape, Noga Zaslavsky, Roger Levy.
CoNLL
, 2020.
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DOI
A Rate–Distortion view of human pragmatic reasoning
Noga Zaslavsky, Jennifer Hu, Roger Levy.
arXiv preprint
, 2020.
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Toward human-like object naming in artificial neural systems
Tiwalayo Eisape, Roger Levy, Joshua Tenenbaum, Noga Zaslavsky.
BAICS @ ICLR
, 2020.
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Video
Emergence of pragmatic reasoning from least-effort optimization
Noga Zaslavsky, Jennifer Hu, Roger Levy.
EvoLang XIII
, 2020.
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DOI
Information-Theoretic Principles in the Evolution of Semantic Systems
Noga Zaslavsky.
PhD Thesis, The Hebrew University
, 2020.
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Deterministic annealing and the evolution of Information Bottleneck representations
Noga Zaslavsky, Naftali Tishby.
Technical report
, 2019.
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Evolution and efficiency in color naming: The case of Nafaanra
Noga Zaslavsky*, Karee Garvin*, Charles Kemp, Naftali Tishby, Terry Regier.
CogSci
, 2019.
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Semantic categories of artifacts and animals reflect efficient coding
Noga Zaslavsky, Terry Regier, Naftali Tishby, Charles Kemp.
CogSci
, 2019.
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Communicative need in color naming
Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier.
CNP
, 2019.
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DOI
Color naming reflects both perceptual structure and communicative need
Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier.
topiCS
, 2019.
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DOI
Efficient compression in color naming and its evolution
ELSC Prize for Outstanding Publication
Noga Zaslavsky, Charles Kemp, Terry Regier, Naftali Tishby.
PNAS
, 2018.
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DOI
SI
Movie 1
Movie 2
Color naming reflects both perceptual structure and communicative need (earlier version)
Best paper award for computational modeling of language
Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier.
CogSci
, 2018.
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Efficient human-like semantic representations via the Information Bottleneck principle
Noga Zaslavsky, Charles Kemp, Terry Regier, Naftali Tishby.
CIAI @ NeurIPS
, 2017.
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Efficient encoding of motion is mediated by gap junctions in the fly visual system
Siwei Wang, Alexander Borst, Noga Zaslavsky, Naftali Tishby, Idan Segev.
PLoS Comput Biol
, 2017.
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Early motion processing circuit uses gap junctions to achieve efficient stimuli encoding
Siwei Wang, Noga Zaslavsky, Alexander Borst, Naftali Tishby, Idan Segev.
COSYNE
, 2017.
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Deep learning and the Information Bottleneck principle
Naftali Tishby, Noga Zaslavsky.
ITW
, 2015.
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