8 papers
Sharpen Your Flow: Sharpness-Aware Sampling for Flow Matching
Aditi Gupta, Soon Hoe Lim, Annan Yu +1
Flow matching models generate samples by numerically integrating a learned velocity field, with each integration step requiring a neural network evaluation. Fast generation therefo…
Continuity Laws for Sequential Models
Annan Yu, Dongwei Lyu, N. Benjamin Erichson
Inductive biases influence the behavior and performance of sequential models. In this work, we study an underexplored inductive bias in sequential modeling: continuity in time. We…
HydroDiffusion: Diffusion-Based Probabilistic Streamflow Forecasting with a State Space Backbone
Yihan Wang, Annan Yu, Lujun Zhang +2
Recent advances have introduced diffusion models for probabilistic streamflow forecasting, demonstrating strong early flood-warning skill. However, current implementations rely on…
Understanding the Implicit Biases of Design Choices for Time Series Foundation Models
Annan Yu, Danielle C. Maddix, Boran Han +7
Time series foundation models (TSFMs) are a class of potentially powerful, general-purpose tools for time series forecasting and related temporal tasks, but their behavior is stron…
Understanding Transformers for Time Series: Rank Structure, Flow-of-ranks, and Compressibility
Annan Yu, Danielle C. Maddix, Boran Han +7
Transformers are widely used across data modalities, and yet the principles distilled from text models often transfer imperfectly to models trained to other modalities. In this pap…
Elucidating the Design Choice of Probability Paths in Flow Matching for Forecasting
Soon Hoe Lim, Yijin Wang, Annan Yu +4
Flow matching has recently emerged as a powerful paradigm for generative modeling and has been extended to probabilistic time series forecasting in latent spaces. However, the impa…