60 citations · 69 across the 2 of their papers we have counts for
5 papers
Probing artificial neural networks: insights from neuroscience
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. One such tool is probes, i.…
A Rate-Distortion view of human pragmatic reasoning
Noga Zaslavsky, Jennifer Hu, Roger P. Levy
What computational principles underlie human pragmatic reasoning? A prominent approach to pragmatics is the Rational Speech Act (RSA) framework, which formulates pragmatic reasonin…
Efficient human-like semantic representations via the Information Bottleneck principle
Noga Zaslavsky, Charles Kemp, Terry Regier +1
Maintaining efficient semantic representations of the environment is a major challenge both for humans and for machines. While human languages represent useful solutions to this pr…
Color naming reflects both perceptual structure and communicative need
Noga Zaslavsky, Charles Kemp, Naftali Tishby +1
Gibson et al. (2017) argued that color naming is shaped by patterns of communicative need. In support of this claim, they showed that color naming systems across languages support…
Deep Learning and the Information Bottleneck Principle
Naftali Tishby, Noga Zaslavsky
Deep Neural Networks (DNNs) are analyzed via the theoretical framework of the information bottleneck (IB) principle. We first show that any DNN can be quantified by the mutual info…