May 4, 2024 · Abstract:A State Space Model (SSM) is a foundation model in time series analysis, which has recently been shown as an alternative to ...
Nov 22, 2023 · The original model S4-Legs used in this paper is unidirectional, which is closest to our problem setting but is not the state-of-the-art model.
May 4, 2024 · A State Space Model (SSM) is a foundation model in time series analysis, which has recently been shown as an alternative to transformers in ...
Abstract. A State Space Model (SSM) is a foundation model in time series analysis, which has recently been shown as an alternative to transformers in se-.
By Tri Dao and Albert Gu. From generalization analysis to optimization designs for state space models (https://arxiv.org/abs/2405.02670). The Illusion of State ...
Jun 29, 2010 · Abstract. In this thesis, I propose to derive entirely data dependent generalization error bounds for state space models.
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient ... From Generalization Analysis to Optimization Designs for State Space Models.
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[2024_044]From generalization analysis to optimization designs for state space models, Fusheng Liu, Qianxiao Li [Paper]. [2024_043]A 2-Dimensional State Space ...
Generalizations of the state space model to include the possibility of changes oc- ... 6.12 Bayesian Analysis of State Space Models. We now consider some Bayesian ...
Graph neural networks (GNNs) [27, 52] are an emerging deep learning model for analyzing graph structured-data. They have achieved state-of-the-art performances ...