1 citations · 2 across the 10 of their papers we have counts for
12 papers
PathFM: Fast Generative Forecasting of Multivariate Trajectories
Jiachen Yao, Xiyuan Zhang, Annan Yu +5
Time series foundation models are becoming increasingly popular for general-purpose forecasting. However, most leading models predict marginal quantiles, leaving the dependence str…
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…
Block-Biased Mamba for Long-Range Sequence Processing
Annan Yu, N. Benjamin Erichson
Mamba extends earlier state space models (SSMs) by introducing input-dependent dynamics, and has demonstrated strong empirical performance across a range of domains, including lang…
A Deep State Space Model for Rainfall-Runoff Simulations
Yihan Wang, Lujun Zhang, Annan Yu +2
The classical way of studying the rainfall-runoff processes in the water cycle relies on conceptual or physically-based hydrologic models. Deep learning (DL) has recently emerged a…
Tuning Frequency Bias of State Space Models
Annan Yu, Dongwei Lyu, Soon Hoe Lim +2
State space models (SSMs) leverage linear, time-invariant (LTI) systems to effectively learn sequences with long-range dependencies. By analyzing the transfer functions of LTI syst…
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…
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…