Markov chain stochastic DCA and applications in deep learning with PDEs regularization
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Publication:6143666
DOI10.1016/j.neunet.2023.11.032MaRDI QIDQ6143666
Hoai An Le Thi, Hoang Phuc Hau Luu, Hoai Minh Le
Publication date: 24 January 2024
Published in: Neural Networks (Search for Journal in Brave)
Nonconvex programming, global optimization (90C26) Stochastic programming (90C15) Normed linear spaces and Banach spaces; Banach lattices (46B99) Limit theorems in probability theory (60F99)
Cites Work
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- Training restricted Boltzmann machines: an introduction
- Exponential convergence of Langevin distributions and their discrete approximations
- Convex analysis approach to d. c. programming: Theory, algorithms and applications
- DC programming and DCA: thirty years of developments
- Stochastic DCA for minimizing a large sum of DC functions with application to multi-class logistic regression
- Variance reduction for Markov chains with application to MCMC
- Deep relaxation: partial differential equations for optimizing deep neural networks
- Entropy penalization methods for Hamilton-Jacobi equations
- Nonasymptotic convergence analysis for the unadjusted Langevin algorithm
- Distributionally robust optimization under endogenous uncertainty with an application in retrofitting planning
- On difference convexity of locally Lipschitz functions
- Composite Difference-Max Programs for Modern Statistical Estimation Problems
- Ergodic Mirror Descent
- Two-Stage Stochastic Programming with Linearly Bi-parameterized Quadratic Recourse
- MultiComposite Nonconvex Optimization for Training Deep Neural Networks
- Entropy-SGD: biasing gradient descent into wide valleys
- Stochastic Difference-of-Convex-Functions Algorithms for Nonconvex Programming
- Brownian Motion
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