for this workshop
American Institute of Mathematics, San Jose, California
Asja Fischer, Guido Montufar, and Jason Morton
This workshop, sponsored by AIM and the NSF, will be devoted to the mathematics of Boltzmann machines. Since their debut in the context of deep learning, Boltzmann machines have been studied intensively, with significant leaps over the past six years, giving them a rare combination of mathematical traction and practical machine learning relevance. Researchers have been pushing tools in information theory, algebra, combinatorics, geometry, and optimization to their limits to achieve the understanding we currently have of these models. The result is a situation where further progress probably requires a more intimate merger of these tools.
The workshop will analyze Boltzmann machines in terms of
- their representational power (compact representations, approximation errors, geometry),
- parameter leaning and optimization (discrete dynamics, sampling, structure of the loss function), and
- their connections to other models and generalizations (deep/ quantum/temporal models).
The workshop will differ from typical conferences in some regards. Participants will be invited to suggest open problems and questions before the workshop begins, and these will be posted on the workshop website. These include specific problems on which there is hope of making some progress during the workshop, as well as more ambitious problems which may influence the future activity of the field. Lectures at the workshop will be focused on familiarizing the participants with the background material leading up to specific problems, and the schedule will include discussion and parallel working sessions.
The deadline to apply for support to participate in this workshop has passed.
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