5 papers
Koopman Observers for Diffusion Acceleration: Correcting Feature Forecasts with Shallow Measurements
Hanru Bai, Yuanchao Xu, Fengyi Li
Feature caching accelerates diffusion sampling by replacing expensive network evaluations with predictions from previously computed activations. However, forecasts based only on pa…
Two-Variable Logic with Arbitrarily Many Successor Relations
Jakub Michaliszyn, Piotr Witkowski
We study two-variable first-order logic with a finite, input-dependent number of distinguished successor relations and arbitrary additional unary and binary predicates. Each distin…
Efficient Reasoning with Flow Language Models
Hanru Bai, Faissal Izermine, Oscar Davis +1
Flow Language Models (FLMs) have emerged as a continuous-state alternative to discrete diffusion language models, yet the role of their continuous representations in reasoning rema…
Physics-Informed Neural PDE Solvers via Spatio-Temporal MeanFlow
Hanru Bai, Yuncheng Zhou, Difan Zou
Deep learning paradigms, such as PINNs and neural operators, have significantly advanced the solving of PDEs. However, they often struggle to capture the continuous integral nature…
Hierarchical Koopman Diffusion: Fast Generation with Interpretable Diffusion Trajectory
Hanru Bai, Weiyang Ding, Difan Zou
Diffusion models have achieved impressive success in high-fidelity image generation but suffer from slow sampling due to their inherently iterative denoising process. While recent…