3 papers
cs.LG2026
SurF: A Generative Model for Multivariate Irregular Time Series Forecasting
Mohammad R. Rezaei, Tejas Balaji, Rahul G. Krishnan
Irregularly sampled multivariate event streams remain a stubbornly difficult modality for generative modeling: tokenization-based approaches break down when inter-event intervals v…
cs.LG2025
Flows and Diffusions on the Neural Manifold
Daniel Saragih, Deyu Cao, Tejas Balaji
Diffusion and flow-based generative models have achieved remarkable success in domains such as image synthesis, video generation, and natural language modeling. In this work, we ex…
cs.LG2025
Flow to Learn: Flow Matching on Neural Network Parameters
Daniel Saragih, Deyu Cao, Tejas Balaji +1
Foundational language models show a remarkable ability to learn new concepts during inference via context data. However, similar work for images lag behind. To address this challen…