Call for Papers

Scope

Mathematics and deep learning shape each other. This workshop invites submissions working in either direction, or across both.

The mathematical scope is intentionally open: any area of mathematics, pure or applied, plus work that turns deep learning toward mathematical discovery.

Topics of Interest

See the Overview for the full topic list. In brief:

Mathematics → Deep Learning: approximation theory, optimization, statistical learning theory, geometry and topology of representations, differential equations and dynamical systems, information-theoretic foundations.

Deep Learning → Mathematics: theorem proving and formalization, conjecture generation, symbolic regression, neural solvers for PDEs, pattern discovery in mathematical structures.

On the bridge: provable guarantees feeding back into algorithm design, co-design of theory and methods, benchmarks and reproducibility.

Submission

Format: TBD (short papers / extended abstracts / full papers).
Portal: OpenReview (link TBD).
Anonymity: TBD.

Important Dates

Contact

Questions? Write to workshop-email@tbd.org.