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Stochasticity helps to navigate rough landscapes: comparing gradient-descent-based algorithms in the phase retrieval problem., , и . Mach. Learn. Sci. Technol., 2 (3): 35029 (сентября 2021)Stochastic Gradient Descent outperforms Gradient Descent in recovering a high-dimensional signal in a glassy energy landscape., и . CoRR, (2023)Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification., , , и . NeurIPS, (2020)Approximate Survey Propagation for Statistical Inference., , , и . CoRR, (2018)The effective noise of Stochastic Gradient Descent., и . CoRR, (2021)The Role of Regularization in Classification of High-dimensional Noisy Gaussian Mixture., , , , и . ICML, том 119 из Proceedings of Machine Learning Research, стр. 6874-6883. PMLR, (2020)Fractal free energy landscapes in structural glasses, , , , и . Nature Communications, (24.04.2014)A note on weakly discontinuous dynamical transitions, , , , и . The Journal of Chemical Physics, 138 (6): 064504+ (07.11.2013)Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models., , , и . ICML, том 97 из Proceedings of Machine Learning Research, стр. 4333-4342. PMLR, (2019)Marvels and Pitfalls of the Langevin Algorithm in Noisy High-dimensional Inference., , , , , и . CoRR, (2018)