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Found 5 papers, 1 papers with code
Optimization dependent generalization bound for ReLU networks based on sensitivity in the tangent bundle
Recent advances in deep learning have given us some very promising results on the generalization ability of deep neural networks, however literature still lacks a comprehensive theory explaining why heavily over-parametrized models are able to generalize well while fitting the training data.
Theoretical Evaluation of Asymmetric Shapley Values for Root-Cause Analysis
In this work, we examine Asymmetric Shapley Values (ASV), a variant of the popular SHAP additive local explanation method.
PAC bounds of continuous Linear Parameter-Varying systems related to neural ODEs
We consider the problem of learning Neural Ordinary Differential Equations (neural ODEs) within the context of Linear Parameter-Varying (LPV) systems in continuous-time.
Learning stability of partially observed switched linear systems
In this paper, we propose an algorithm for deciding stability of switched linear systems under arbitrary switching based purely on observed output data.
LPV Modeling of Nonlinear Systems: A Multi-Path Feedback Linearization Approach
In the SISO case, all nonlinearities of the original system are embedded into one NL function, which is factorized, based on a proposed algorithm, to construct an LPV representation of the original NL system.
