Shared Visual Representations in Human & Machine Intelligence

2021 NeurIPS Workshop — December 13, 2021 (Virtual).

The goal of the 3rd Shared Visual Representations in Human and Machine Intelligence (SVRHM) workshop is to disseminate relevant, parallel findings in the fields of computational neuroscience, psychology, and cognitive science that may inform modern machine learning methods.

In the past few years, machine learning methods—especially deep neural networks—have widely permeated the vision science, cognitive science, and neuroscience communities. As a result, scientific modeling in these fields has greatly benefited, producing a swath of potentially critical new insights into human learning and intelligence, which remains the gold standard for many tasks. However, the machine learning community has been largely unaware of these cross-disciplinary insights and analytical tools, which may help to solve many of the current problems that ML theorists and engineers face today (e.g., adversarial attacks, compression, continual learning, and self-supervised learning).

Thus we propose to invite leading cognitive scientists with strong computational backgrounds to disseminate their findings to the machine learning community with the hope of closing the loop by nourishing new ideas and creating cross-disciplinary collaborations.

Please see the About page for a more detailed description of the motivation of the workshop.

Invited Speakers & Panelists

Yalda Mohsenzadeh 🇮🇷

Western University

Department of Computer Science & Brain and Mind Institute

Ruben Coen-Cagli 🇮🇹

Albert Einstein College of Medicine

Department of Systems and Computational Biology

Zoya Bylinskii 🇨🇦

Adobe Research

Creative Intelligence Lab

Gemma Roig 🇪🇸

Goethe University Frankfurt

Department of Computer Science

Yukiyasu Kamitani 🇯🇵

Kyoto University & ATR

Department of Information Sciences & Technologies

Maryam Vaziri-Pashkam 🇮🇷

National Institute of Health

Laboratory of Brain and Cognition

Michelle Greene 🇺🇸

Bates College

Department of Neuroscience

Roland Fleming 🇩🇪/🇬🇧

Giessen University

Department of Psychology

Ruth Rosenholtz 🇺🇸


Computer Science and Artificial Intelligence Lab (CSAIL) & Brain and Cognitive Sciences Department (BCS)

Xavier Boix 🇪🇸/Catalonia


Brain and Cognitive Sciences Department

Wieland Brendel 🇩🇪

Universität Tübingen & Layer 7AI

International Max Planck Research School for Intelligent Systems

Stéphane Deny 🇫🇷 /🇺🇸/🇫🇮/

Facebook AI Research & Aalto University

Kohitij Kar 🇮🇳


McGovern Institute for Brain Research

Tiago Marques 🇵🇹


McGovern Institute for Brain Research

Chaz Firestone 🇨🇦

Johns Hopkins University

Department of Psychological and Brain Sciences & Department of Philosophy

Donsuk Lee 🇰🇷

Indiana University

Informatics and Cognitive Sciences Department

Andy Keller 🇺🇸

University of Amsterdam

Machine Learning Department

Bhavin Choksi 🇮🇳

French National Center for Scientific Research (CNRS)

Brain and Cognition Research Center (CerCO)

Tushar Arora 🇮🇳

University of Tokyo

International Research Center for Neurointelligence (IRCN)


Arturo Deza 🇵🇪


Center for Brains, Minds and Machines

Joshua Peterson 🇺🇸

Princeton University

Department of Computer Science

Apurva Ratan Murty 🇮🇳


McGovern Institute for Brain Research

Thomas Griffiths 🇺🇸/🇬🇧/🇦🇺

Princeton University

Departments of Computer Science and Psychology


MIT Center for Brains, Minds and Machines (CBMM)

National Science Foundation (NSF)


Facebook Reality Labs (FRL)

MIT Quest for Intelligence

Brain Score Initiative

Adobe Research

This material/activity is funded, in full or in part, by the Center for Brains, Minds and Machines (CBMM), funded by NSF STC award CCF-1231216.Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.