7 papers · 1 filter
Learning Permutation Distributions via Reflected Diffusion on Ranks
Sizhuang He, Yangtian Zhang, Shiyang Zhang +1
The finite symmetric group S_n provides a natural domain for permutations, yet learning probability distributions on S_n is challenging due to its factorially growing size and disc…
Variational Learning for Insertion-based Generation
Yangtian Zhang, Zhe Wang, Arthur Gretton +4
Non-monotonic sequence generation methods, such as masked diffusion models, provide a flexible alternative to left-to-right autoregressive modeling by allowing tokens to be generat…
SciDesignBench: Benchmarking and Improving Language Models for Scientific Inverse Design
David van Dijk, Ivan Vrkic
Many of the most important problems in science and engineering are inverse problems: given a desired outcome, find a design that achieves it. Evaluating whether a candidate meets t…
Non-Markovian Discrete Diffusion with Causal Language Models
Yangtian Zhang, Sizhuang He, Daniel Levine +7
Discrete diffusion models offer a flexible, controllable approach to structured sequence generation, yet they still lag behind causal language models in expressive power. A key lim…
TANTE: Time-Adaptive Operator Learning via Neural Taylor Expansion
Zhikai Wu, Sifan Wang, Shiyang Zhang +5
Operator learning for time-dependent partial differential equations (PDEs) has seen rapid progress in recent years, enabling efficient approximation of complex spatiotemporal dynam…
GeoFunFlow: Geometric Function Flow Matching for Inverse Operator Learning over Complex Geometries
Sifan Wang, Zhikai Wu, David van Dijk +1
Inverse problems governed by partial differential equations (PDEs) are crucial in science and engineering. They are particularly challenging due to ill-posedness, data sparsity, an…