Call for Papers
The following areas provide a sense of suitable topics for paper submissions:
Biological inspiration and inductive bias
Human-relevant strategies for robustness and generalization
New datasets (e.g., for comparing humans/animals and machines)
Perceptual invariance and metamerism
Biologically-informed strategies to mitigate adversarial vulnerability
Foveation, active perception, and attention models
Biologically inspired Generative Models
Perceptual and cognitive robustness
Nuances and noise in perceptual and cognitive Systems
Differences and similarities between humans and deep neural networks
Canonical computations in biological and artificial systems
Alternative architectures for deep neural networks
Reverse engineering of the human visual system via deep neural networks
All submissions will be private and anonymous. Papers should be between 4-5 pages (excluding references), and will be formatted in NeurIPS style anonymously. Accepted papers will be presented as posters during the workshop, and will optionally be posted on the workshop website if the authors desire. Authors may optionally add appendixes in their submitted paper and the final submission including main paper, references and appendix should not exceed 15 pages. Supplementary Materials uploads are to only be used optionally for extra videos/code/data/figures and should be uploaded separately in the OpenReview submission website.
The paper submission process begins September 15th, 2020. Paper submission deadline is October 8th (extended!) 2020 11:59 pm PST.
Link to Paper submission: https://openreview.net/group?id=NeurIPS.cc/2020/Workshop/SVRHM
Authors can revise a submission multiple times before the final deadline, and are encouraged to update their submissions as many times as necessary before the deadline.
Papers accepted in this workshop will have non-archival status - thus researchers are encouraged to submit relevant on-going work in middle to late stages (or currently under review) in fields that span the domains of computer vision, machine learning, and vision science and cognitive science.
Papers that have been previously published in machine learning and computer vision venues (ex: CVPR, ICCV, ICLR, NeurIPS) should not be re-submitted as workshop papers -- though submitting ongoing work is highly encouraged. In addition, extending an abstract that has been previously accepted in other non-archival cognitive science and vision science venues that the machine learning community is not aware of such as: CCN, VSS, CogSci, SfN are highly encouraged! If such work is being re-submitted/extended please indicate so as a footnote in the abstract. Only works that have never been shown before in either arxival or non-arxival venues (with the exception of ArXiv) are eligible for awards/orals. Authors are allowed and encouraged to re-submit updated papers that did not get accepted at the main NeurIPS conference (or other computer vision/machine learning venue) that fits within the scope of this workshop.
Naturally, highly interdisciplinary papers that has never been presented before in any meeting/is not under review will receive higher consideration by the reviewers.
Here is the link for the Latex template style files for the submissions: svrhm_2020.tex
Please use this .sty template for the camera ready version: svrhm_2020.sty
Reviewers are also allowed to submit to the workshop. OpenReview will automatically handle potential conflicts of interest between reviewer assignments, authors and affiliations.
Note to first time authors and reviewers to submit to OpenReview (specially those in the fields of Psychology/Cognitive Science/Neuroscience): The reviewing policy is double-blind, but the reviews will be made anonymously and public -- the following reviewing scheme encourages full transparency between reviewers, encourages them to review at high standards, while also encouraging authors to submit their best work (as identities of authors will be de-anonymized post-reviews; reviewer identities will stay anonymous. See this link from ICLR 2019 as an example).
Acceptance Rate will likely be ~60% similar to last year. We are expecting roughly 50 submissions.
All submitted papers will be evaluated using the following standards:
Novelty of the idea with regards to human perception and cognition and how it may be relevant to modern machine learning.
Rigor in preliminary theoretical contribution and/or empirical finding.
Relevance at the intersection of the fields of machine learning and psychology.
Best Paper Awards & Prizes (Currently being updated)
This year for the first time we will have the 4 highest scoring papers give Recorded Oral Presentations to maximize the outreach and impact of the author's work.
Reviewing Committee (Currently being updated):
Vanessa D'Amario (MIT - Center for Brains, Minds and Machines)
NC Puneeth (UC Santa Barbara - Department of Psychology)
Colin Conwell (Harvard - Department of Psychology)
RT Pramod (MIT - McGovern Institute for Brain Research)
Yena Han (MIT - Center for Brains, Minds and Machines)
Daniel Janini (Harvard - Department of Psychology)
Tiago Marques (MIT- McGovern Institute for Brain Research)
Nicole Han (UC Santa Barbara - Department of Psychology)
Akshay Rangamani (MIT - Center for Brains, Minds and Machines)
Andrzej Banburski (MIT- Center for Brains, Minds and Machines)
Qianli Liao (MIT- Center for Brains, Minds and Machines)
Senthil Purushwalkam (CMU - Department of Computer Science)
Sophia Sanborn (UC Berkeley - Redwood Center for Theoretical Neuroscience)
Judy Borowski (University of Tübingen & International Max Planck Research School for Intelligent Systems)
Alex Berardino (Apple - Diplays and Imaging Technology)
Christina Funke (University of Tübingen & International Max Planck Research School for Intelligent Systems)
Katherine Hermann (Stanford University - Department of Psychology)
Jasmine Collins (UC Berkeley - Redwood Center for Theoretical Neuroscience)
Karan Grewal (Numenta)
Jenelle Feather (MIT - Center for Brains, Minds and Machines)
Ekta Prashnani (NVIDIA Research)
Robert Geirhos (University of Tübingen & International Max Planck Research School for Intelligent Systems)
Kate Storrs (Justus Liebig University Giessen - Department of Experimental Psychology)
Tal Golan (Columbia University - Zuckerman Institute)
Tom White (Victoria University of Wellington - School of Design)
Alex Hernandez-Garcia (University of Osnabrück - Institute of Cognitive Science)
Simon Kornblith (Google Brain)
Emilie Josephs (Harvard University - Department of Psychology)
Xue-Xin Wei (UT Austin- Department of Neuroscience & Psychology)
Caterina Magri (Johns Hopkins University - Department of Psychology)
Stéphane Deny (Facebook Artificial Intelligence Research)
Tom Wallis (Amazon Research)
Yalda Mohsenzadeh (University of Western Ontario - Department of Computer Science & Brain and Mind Institute)
Jonathon Hare (University of Southampton - School of Electronics and Computer Science)
Ethan Harris (University of Southampton - School of Electronics and Computer Science)
Nikhil Parthasarathy (NYU - Center for Neural Science)
Santiago Cadena (University of Tübingen & International Max Planck Research School for Intelligent Systems)
Yubei Chen (UC Berkeley - Redwood Center for Theoretical Neuroscience)
Anton Kaplanyan (Facebook Reality Labs)
Anjul Patney (Facebook Reality Labs)
Josue Ortega Caro (Baylor College of Medicine)
Johannes Balle (Google Machine Perception)
Pouya Bashivan (McGill University - Department of Physiology)
Erin Grant (UC Berkeley - Berkeley Artificial Intelligence Research (BAIR))
Bernhard Egger (MIT- Center for Brains, Minds and Machines)
Alessandro Achille (Amazon Research)
Drew Linsley (Brown University -Department of Cognitive, Linguistic and Psychological Sciences)
Kshitij Dwivedi (Goethe University - Department of Computer Science & Free University Berlin Department of Education and Psychology)
Fabian Soto (Florida International University - Department of Psychology)
Lynn Sörensen (University of Amsterdam - Department of Psychology)
Claudio Michaelis (University of Tübingen & International Max Planck Research School for Intelligent Systems)
Zahra Kadkhodaie (NYU - Data Science)
Will Xiao (Harvard University - Department of Neurobiology)
Matt Peterson (UC Santa Barbara - The Mellichamp Initiative in Mind & Machine Intelligence)
Arash Akbarinia (Justus Liebig University Giessen)
Alban Flachot (Justus Liebig University Giessen)
Rama Vedantam (Facebook Artificial Intelligence Research)
Dipan Pal (Carnegie Mellon University - Department of Computer Science)
Chaitanya Ryali (University of California, San Diego - Department of Computer Science & Engineering)
Ella Batty (Harvard Medical School)
Adrien Doerig (École Polytechnique Fédérale de Lausanne)
Michael Chang (UC Berkeley - Berkeley Artificial Intelligence Research (BAIR))
Shahab Bakhtiari (McGill - MILA Montréal )