1 citations · 2 across the 10 of their papers we have counts for
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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…