Shared Visual Representations in Human & Machine Intelligence

NeurIPS 2022 Workshop | Location: Room 397 @ New Orleans Ernest N. Morial Convention Center

December 2nd, 2022

New Orleans, Louisiana

The goal of the 4th 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 methodsโ€”especially deep neural networksโ€”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

Kate Storrs ๐Ÿ‡ฆ๐Ÿ‡บ

University of Auckland

School of Psychology

Dirk B. Walther ๐Ÿ‡จ๐Ÿ‡ฆ/๐Ÿ‡ฉ๐Ÿ‡ช

University of Toronto

Department of Psychology

Katharina Dobs ๐Ÿ‡ฉ๐Ÿ‡ช

Justus-Liebig University Giessen


Tyler Bonnen ๐Ÿ‡บ๐Ÿ‡ธ

Stanford University

Wu Tsai Neurosciences Institute

Tom White ๐Ÿ‡ณ๐Ÿ‡ฟ

Victoria University of Wellington

School of Design

Aenne Brielmann ๐Ÿ‡ฉ๐Ÿ‡ช

Max-Planck Institute for Biological Cybernetics


Colin Conwell ๐Ÿ‡บ๐Ÿ‡ธ

Harvard University

Department of Psychology

Piotr Mirowski ๐Ÿ‡ซ๐Ÿ‡ท/๐Ÿ‡ต๐Ÿ‡ฑ

DeepMind


Phillip Isola ๐Ÿ‡บ๐Ÿ‡ธ

MIT

Computer Science & Artificial Intelligence Laboratory

Margaret Livingstone ๐Ÿ‡บ๐Ÿ‡ธ

Harvard Medical School


Josue Ortega-Caro ๐Ÿ‡ต๐Ÿ‡ช

Yale University

Wu Tsai Institute


Patrick Mineault ๐Ÿ‡จ๐Ÿ‡ฆ

Xcorr Consulting


Jenelle Feather ๐Ÿ‡บ๐Ÿ‡ธ

Flatiron Institute

Center for Computational Neuroscience

Iris Groen ๐Ÿ‡ณ๐Ÿ‡ฑ

University of Amsterdam

Informatics Institute

Tal Golan ๐Ÿ‡บ๐Ÿ‡ธ/๐Ÿ‡ฎ๐Ÿ‡ฑ

Columbia University

Zuckerman Institute

Wenxuan Guo ๐Ÿ‡จ๐Ÿ‡ณ

Columbia University

Zuckerman Institute

Dawn Finzi ๐Ÿ‡บ๐Ÿ‡ธ/๐Ÿ‡ฌ๐Ÿ‡ง

Stanford University

Wu Tsai Neurosciences Institute

Balรกzs Meszรฉna ๐Ÿ‡ญ๐Ÿ‡บ

Wigner Research Centre for Physics

Department of Computational Sciences

Ching Fang ๐Ÿ‡บ๐Ÿ‡ธ

Columbia University

Center for Theoretical Neuroscience

Matthew Koichi Grimes ๐Ÿ‡บ๐Ÿ‡ธ

DeepMind


Michael Cohen ๐Ÿ‡บ๐Ÿ‡ธ

MIT/Amherst

McGovern Institute for Brain Research

Jim DiCarlo ๐Ÿ‡บ๐Ÿ‡ธ

MIT & Quest for Intelligence

McGovern Institute for Brain Research

Organizers

Arturo Deza ๐Ÿ‡ต๐Ÿ‡ช

Artificio

Joshua Peterson ๐Ÿ‡บ๐Ÿ‡ธ

Princeton University

Department of Computer Science

Apurva Ratan Murty ๐Ÿ‡ฎ๐Ÿ‡ณ

MIT

McGovern Institute for Brain Research

Thomas Griffiths ๐Ÿ‡บ๐Ÿ‡ธ/๐Ÿ‡ฌ๐Ÿ‡ง/๐Ÿ‡ฆ๐Ÿ‡บ

Princeton University

Departments of Computer Science and Psychology

Sponsors

MIT Center for Brains, Minds and Machines (CBMM)

National Science Foundation (NSF)

Artificio

NVIDIA

MIT Quest for Intelligence

Common Sense Machines

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.