[ICML] Differentiable Almost Everything: Differentiable Relaxations, Algorithms, Operators, and Simulators Workshop at ICML 2024, Vienna
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Felix Petersen
Apr 10, 2024, 8:30:04 AM4/10/24
to Machine Learning News
Dear all,
we are organizing the second edition of the Differentiable Almost Everything workshop at ICML 2024 in Vienna, Austria.
If you are working on something differentiable, consider submitting your work.
Call for Papers
This workshop encourages submissions on novel research results, benchmarks, frameworks, and work-in-progress research on differentiating through conventionally non-differentiable operations. The format of submission are 4-page papers (excluding references) submitted to OpenReview. The review-process will not be open.
Scope
The technical topics of interest at this workshop include (but are not limited to):
Continuous relaxations of discrete operations and algorithms (e.g., argmax, sorting, ranking, rendering, shortest-path, optimizers, if-else constructs, indexing, top-k, logics, etc.)
Stochastic relaxations and gradient estimation methods (e.g., stochastic smoothing)
Weakly- and self-supervised learning with differentiable algorithms, e.g., ranking supervision
Optimization with diff. algorithms, e.g., regression of scene parameters via diff. rendering
Systematic techniques for making discrete structures differentiable, e.g., smoothing
Differentiable simulators such as differentiable fluid dynamics, differentiable particle simulators, differentiable optics, differentiable protein-folding, differentiable cloth simulations, etc.
Differentiable architecture search, e.g., convolutions with diff. and learnable kernel sizes
Applications of differentiable relaxations, e.g., in learning-to-rank and computer vision
Submission
The submission deadline is: May 28 ‘24 23:59 PT
The format of submission are 4-page papers (+ references) using the workshop’s LaTeX style files. Supplementary materials / appendices after the references are allowed and do not count towards the page limit.
This workshop encourages submissions on novel research results, benchmarks, frameworks, and work-in-progress research on differentiating through conventionally non-differentiable operations. The format of submission are 4-page papers (excluding references) submitted to OpenReview. The review-process will not be open.
Scope
The technical topics of interest at this workshop include (but are not limited to):
Continuous relaxations of discrete operations and algorithms (e.g., argmax, sorting, ranking, rendering, shortest-path, optimizers, if-else constructs, indexing, top-k, logics, etc.)
Stochastic relaxations and gradient estimation methods (e.g., stochastic smoothing)
Weakly- and self-supervised learning with differentiable algorithms, e.g., ranking supervision
Optimization with diff. algorithms, e.g., regression of scene parameters via diff. rendering
Systematic techniques for making discrete structures differentiable, e.g., smoothing
Differentiable simulators such as differentiable fluid dynamics, differentiable particle simulators, differentiable optics, differentiable protein-folding, differentiable cloth simulations, etc.
Differentiable architecture search, e.g., convolutions with diff. and learnable kernel sizes
Applications of differentiable relaxations, e.g., in learning-to-rank and computer vision
Submission
The submission deadline is: May 28 ‘24 23:59 PT
The format of submission are 4-page papers (+ references) using the workshop’s LaTeX style files. Supplementary materials / appendices after the references are allowed and do not count towards the page limit.
Notifications: Mid of June, 2024.
Mailing list:
Consider joining the fully differentiable mailing list for more updates: https://forms.gle/JuriPKno48nyTbYq5
We are looking forward to meeting you in Vienna in July.
Best regards,
Felix Petersen, Marco Cuturi, Hilde Kuehne, Christian Borgelt, Lawrence Stewart, Michael Kagan, Stefano Ermon
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