Search Results for author:
Found 3 papers, 0 papers with code
Extended convexity and smoothness and their applications in deep learning
Furthermore, we establish descent lemmas for the $\mathcal{H}(\phi)$-convex and $\mathcal{H}(\Phi)$-smooth objective functions when utilizing these four methods.
Distribution Learning and Its Application in Deep Learning
Departing from the traditional statistical learning framework, PD learning focuses on learning the underlying probability distribution, which is modeled as a random variable within the probability simplex.
Error Bounds of Supervised Classification from Information-Theoretic Perspective
Fitting error measures the disparity between the empirical risk and model risk.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
