1. Van Dijk, M., Nguyen, L., Nguyen, P.H., and Phan, D. (2019, January 9–15). Characterization of convex objective functions and optimal expected convergence rates for sgd. Proceedings of the International Conference on Machine Learning, Long Beach, CA, USA.
2. Kawaguchi, K., and Lu, H. (2020, January 26–28). Ordered sgd: A new stochastic optimization framework for empirical risk minimization. Proceedings of the International Conference on Artificial Intelligence and Statistics, Palermo, Italy.
3. Demuth, H.D., Beale, M.H., De Jess, O., and Hagan, M.T. (2014). Neural Network Design, Martin Hagan.
4. Robbins, H., and Monro, S. (1951). A stochastic approximation method. JSTOR, 400–407.
5. Adaptive subgradient methods for online learning and stochastic optimization;Duchi;J. Mach. Learn. Res.,2011