Feb 22, 2022 · Leveraging the Uniform-LGI, we first derive convergence rates for gradient flow algorithm, then we give generalization bounds for a large class ...
''The paper's analysis focuses on the gradient flow (Equation 3) instead of the standard gradient descent steps. It is a little unclear whether the analysis can ...
Optimization and generalization are two essential aspects of statistical machine learning. In this paper, we propose a framework to connect optimization ...
A framework to connect optimization with generalization by analyzing the generalization error based on the optimization trajectory under the gradient flow ...
From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality. This repository contains the official codes that used for generating ...
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From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality ... From Generalization Analysis to Optimization Designs for State ...
Official code for ''From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality'' (TMLR). Python 5.
Deep Learning Optimization. [4] F. Liu, H. Yang, S. Hayou, Q. Li*, . From Optimization Dynamics to Generalization Bounds via Łojasiewicz Gradient Inequality.
In this paper, we propose a framework to connect optimization with generalization by analyzing the generalization error based on the optimization trajectory ...
Through our approach, we show that, with a proper initialization, gradient flow converges following a short path with an explicit length estimate. BIG-bench ...