Publications (17)
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…
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…
On the stability of unevenly spaced samples for interpolation and quadrature
Annan Yu, Alex Townsend
Unevenly spaced samples from a periodic function are common in signal processing and can often be viewed as a perturbed equally spaced grid. In this paper, we analyze how the uneve…
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…
HOPE for a Robust Parameterization of Long-memory State Space Models
Annan Yu, Michael W. Mahoney, N. Benjamin Erichson
State-space models (SSMs) that utilize linear, time-invariant (LTI) systems are known for their effectiveness in learning long sequences. To achieve state-of-the-art performance, a…
Robustifying State-space Models for Long Sequences via Approximate Diagonalization
Annan Yu, Arnur Nigmetov, Dmitriy Morozov +2
State-space models (SSMs) have recently emerged as a framework for learning long-range sequence tasks. An example is the structured state-space sequence (S4) layer, which uses the…