Convergence of continuous-time stochastic gradient descent with applications to deep neural networks
PositiveArtificial Intelligence
A recent study explores a continuous-time approach to stochastic gradient descent, revealing important conditions for its convergence. This research builds on previous work by Chatterjee and highlights its relevance in training overparametrized neural networks. This is significant as it could enhance the efficiency and effectiveness of machine learning models, making them more reliable in minimizing expected loss.
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