no code implementations • 23 Aug 2022 • Anna A. Ivanova, Martin Schrimpf, Stefano Anzellotti, Noga Zaslavsky, Evelina Fedorenko, Leyla Isik
Moreover, we argue that, instead of categorically treating the mapping models as linear or nonlinear, we should instead aim to estimate the complexity of these models.
no code implementations • 30 Jun 2022 • Mycal Tucker, Julie Shah, Roger Levy, Noga Zaslavsky
Emergent communication research often focuses on optimizing task-specific utility as a driver for communication.
no code implementations • 12 Aug 2021 • Jennifer Hu, Roger Levy, Noga Zaslavsky
Models of context-sensitive communication often use the Rational Speech Act framework (RSA; Frank & Goodman, 2012), which formulates listeners and speakers in a cooperative reasoning process.
no code implementations • 16 Apr 2021 • Anna A. Ivanova, John Hewitt, Noga Zaslavsky
A major challenge in both neuroscience and machine learning is the development of useful tools for understanding complex information processing systems.
no code implementations • CONLL 2020 • Tiwalayo Eisape, Noga Zaslavsky, Roger Levy
Contemporary autoregressive language models (LMs) trained purely on corpus data have been shown to capture numerous features of human incremental processing.
no code implementations • 13 May 2020 • Noga Zaslavsky, Jennifer Hu, Roger P. Levy
What computational principles underlie human pragmatic reasoning?
no code implementations • SCiL 2020 • Noga Zaslavsky, Terry Regier, Naftali Tishby, Charles Kemp
Recently, this idea has been cast in terms of a general information-theoretic principle of efficiency, the Information Bottleneck (IB) principle, and it has been shown that this principle accounts for the emergence and evolution of named color categories across languages, including soft structure and patterns of inconsistent naming.
no code implementations • 9 Aug 2018 • Noga Zaslavsky, Charles Kemp, Terry Regier, Naftali Tishby
This work thus identifies a computational principle that characterizes human semantic systems, and that could usefully inform semantic representations in machines.
no code implementations • 16 May 2018 • Noga Zaslavsky, Charles Kemp, Naftali Tishby, Terry Regier
We show that greater communicative precision for warm than for cool colors, and greater communicative need, may both be explained by perceptual structure.
1 code implementation • 9 Mar 2015 • Naftali Tishby, Noga Zaslavsky
Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle.