state space that may not only unreasonably divide the space but also hinder ... Model- ensemble trust-region policy optimization. arXiv preprint. arXiv ...

May 30, 2024 · From Generalization Analysis to Optimization Designs for State Space Models. Fusheng Liu, Qianxiao Li. YC. 0. Reddit. 0. A State Space Model ( ...

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 ...

Deep neural networks have achieved great empirical successes in recent years. They form the backbone of many state-of-the-art algorithms in computer vision, ...

First, we intro- duce a class of models with numerous representations and properties that generalize the strengths of standard deep sequence models such as ...

each of these conditions a state space formulation with the above properties can be derived. ... simulation, control, or optimization of the technical system.

parameters on their generalization performance, providing some principles on model architecture design. ... In contrast to their simplified models, we analyze ...

ABSTRACT. Digital Signal Processing (DSP) is becoming increasingly widespread in portable devices. Due to harsh constraints on.

This paper studies the optimized quadratic integral of a generalized multi-linear state space system under free vibration. ... analysis, Writing – original draft, ...

... models and guide designing more effective reasoning architectures and ... space, the error bound is independent of the dimensionality of the state space.