Shared Visual Representations in Human & Machine Intelligence

2021 NeurIPS Workshop (To Be Confirmed)— December 13/14, 2021.

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

Organizers

Arturo Deza 🇵🇪

MIT

Center for Brains, Minds and Machines

Joshua Peterson 🇺🇸

Princeton University

Department of Computer Science

Apurva Ratan Murty 🇮🇳

MIT

McGovern Institute for Brain Research

Thomas Griffiths 🇺🇸

Princeton University

Departments of Computer Science and Psychology

Sponsors

MIT Center for Brains, Minds and Machines (CBMM)

National Science Foundation (NSF)

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.