Connectionists: Few more days for NeurIPS 2023 Gaze Meets ML Workshop 2nd Edition

[Alex Karargyris]

Dear all,

The deadline for paper submission to the 2nd edition of Gaze Meets ML
workshop is fast approaching (i.e., 27th September).

The 1st edition of the workshop drew interest from a diverse group of
experts in the areas of neuroscience, machine learning, reinforcement
learning etc. demonstrating the strong potential of visual attention in
various tasks spanning from data collection and annotation to causality and
medical imaging reading. This year’s workshop will continue upon last
year’s success and bring together experts from various backgrounds (e.g.
neuroscience, machine learning, computer vision, medical imaging, NLP,
etc.) to discuss ideas and ways to bridge human and machine attention that
can help make machine learning more efficient. Please find more information
in the Call for Papers below.


Sincerely,

Alexandros Karargyris on behalf of the organizing committee




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The 2023 Gaze Meets ML workshop in conjunction with NeurIPS 2023

*****************************************************************
***************

Webpage: https://gaze-meets-ml.github.io/
<https://gaze-meets-ml.github.io/gaze-meets-ml/>

Twitter Handle: https://twitter.com/Gaze_Meets_ML

Submission site:  <https://cmt3.research.microsoft.com/OpenEDS2019>
https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/Gaze_Meets_ML

Submission deadline: September 27th, 2023

Date: December 16th, 2023

Location: New Orleans Convention Center, New Orleans, LA

** Overview **

We are excited to host the second edition of Gaze Meets ML workshop in
December, 2023 in conjunction with NeurIPS 2023. The workshop will take
place in-person at New Orleans! We’ve got a great lineup of speakers
<https://gaze-meets-ml.github.io/gaze_ml_2023/speakers/>.

** Background **

Eye gaze has proven to be a cost-efficient way to collect large-scale
physiological data that can reveal the underlying human attentional
patterns in real life workflows, and thus has long been explored as a
signal to directly measure human-related cognition in various domains
Physiological data (including but not limited to eye gaze) offer new
perception capabilities, which could be used in several ML domains, e.g.,
egocentric perception, embodiedAI, NLP, etc. They can help infer human
perception, intentions, beliefs, goals and other cognition properties that
are much needed for human-AI interactions and agent coordination. In
addition, large collections of eye-tracking data have enabled data-driven
modeling of human visual attention mechanisms, both for saliency or
scanpath prediction, with twofold advantages: from the neuroscientific
perspective to understand biological mechanisms better, from the AI
perspective to equip agents with the ability to mimic or predict human
behavior and improve interpretability and interactions.

With the emergence of immersive technologies, now more than any time there
is a need for experts of various backgrounds (e.g., machine learning,
vision, and neuroscience communities) to share expertise and contribute to
a deeper understanding of the intricacies of cost-efficient human
supervision signals (e.g., eye-gaze) and their utilization towards bridging
human cognition and AI in machine learning research and development. The
goal of this workshop is to bring together an active research community to
collectively drive progress in defining and addressing core problems
in gaze-assisted
machine learning.



** Call for Papers **

We welcome submissions that present aspects of eye-gaze in regards to
cognitive science, psychophysiology and computer science, propose methods
on integrating eye gaze into machine learning, and application domains from
radiology, AR/VR, autonomous driving, etc. that introduce methods and
models utilizing eye gaze technology in their respective domains.

Topics of interest include but are not limited to the following:

   -

   Understanding the neuroscience of eye-gaze and perception.
   -

   State of the art in incorporating machine learning and eye-tracking.
   -

   Annotation and ML supervision with eye-gaze.
   -

   Attention mechanisms and their correlation with eye-gaze.
   -

   Methods for gaze estimation and prediction using machine learning.
   -

   Unsupervised ML using eye gaze information for feature
   importance/selection.
   -

   Understanding human intention and goal inference.
   -

   Using saccadic vision for ML applications.
   -

   Use of gaze for human-AI interaction and agent coordination in
   multi-agent environments.
   -

   Eye gaze used for AI, e.g., NLP, Computer Vision, RL, Explainable AI,
   Embodied AI, Trustworthy AI.
   -

   Ethics of Eye Gaze in AI
   -

   Gaze applications in cognitive psychology, radiology, neuroscience,
   AR/VR, autonomous cars, privacy, etc.


** Submission Guidelines **

The workshop will feature two tracks for submission: a full, archival
proceedings track with accepted papers published in the Proceedings for
Machine Learning Research (PMLR) <https://proceedings.mlr.press/>; and a
non-archival, extended abstract track. Submissions to either track will
undergo the same double-blind peer review. Full proceedings papers can be
up to 15 pages and extended abstract papers can be up to 8 pages (both
excluding references and appendices). Authors of accepted extended
abstracts (non-archival submissions) retain full copyright of their work,
and acceptance of such a submission to Gaze Meets ML does not preclude
publication of the same material in another archival venue (e.g., journal
or conference).

Please submit your paper at
https://openreview.net/group?id=NeurIPS.cc/2023/Workshop/Gaze_Meets_ML

** Awards and Funding **

Possibly award prizes for best papers or cover registration fees of
presenting authors with a focus on underrepresented minorities.

** Important dates for Workshop paper submission **

   -

   Paper submission deadline: September 27, 2023
   -

   Reviewing starts: September 30, 2023
   -

   Reviewing ends: October 16, 2023
   -

   Notification of acceptance: October 27, 2023
   -

   Workshop: 16 December 2023 (in person)


** Organizing Committee **

Amarachi Mbakwe (Virginia Tech)

Joy Wu (Stanford, IBM Research)

Dario Zanca (FAU Erlangen-Nürnberg)

Elizabeth Krupinski (Emory University)

Satyananda Kashyap (IBM Research)

Alex Karargyris (MLCommons)

** Contact **

Organizing Committee gaze.neurips at gmail.com
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