Pages that link to "Item:Q4641709"

The following pages link to Optimization Methods for Large-Scale Machine Learning (Q4641709):

Displaying 50 items.

• A deep learning algorithm for high-dimensional exploratory item factor analysis (Q823855) (← links)
• On large batch training and sharp minima: a Fokker-Planck perspective (Q828491) (← links)
• Mathematical optimization in classification and regression trees (Q828748) (← links)
• A discussion on variational analysis in derivative-free optimization (Q829491) (← links)
• A Levenberg-Marquardt method for large nonlinear least-squares problems with dynamic accuracy in functions and gradients (Q1616028) (← links)
• On principal components regression, random projections, and column subsampling (Q1616329) (← links)
• Stochastic optimization using a trust-region method and random models (Q1646570) (← links)
• On variance reduction for stochastic smooth convex optimization with multiplicative noise (Q1739038) (← links)
• Parallel decomposition methods for linearly constrained problems subject to simple bound with application to the SVMs training (Q1790674) (← links)
• Stochastic gradient descent with Polyak's learning rate (Q1983178) (← links)
• Forward stability of ResNet and its variants (Q1988347) (← links)
• ADMM-softmax: an ADMM approach for multinomial logistic regression (Q1988494) (← links)
• Stochastic variance reduced gradient methods using a trust-region-like scheme (Q1995995) (← links)
• Selection dynamics for deep neural networks (Q2003969) (← links)
• Convergence of stochastic proximal gradient algorithm (Q2019902) (← links)
• PPINN: parareal physics-informed neural network for time-dependent PDEs (Q2020276) (← links)
• Analysis of biased stochastic gradient descent using sequential semidefinite programs (Q2020610) (← links)
• A variation of Broyden class methods using Householder adaptive transforms (Q2023660) (← links)
• Convergence of stochastic gradient descent in deep neural network (Q2025203) (← links)
• Convergence analysis of neural networks for solving a free boundary problem (Q2027584) (← links)
• Stochastic proximal gradient methods for nonconvex problems in Hilbert spaces (Q2028468) (← links)
• Convergence rates for optimised adaptive importance samplers (Q2029096) (← links)
• Optimization problems for machine learning: a survey (Q2029894) (← links)
• A unified convergence analysis of stochastic Bregman proximal gradient and extragradient methods (Q2031928) (← links)
• General convergence analysis of stochastic first-order methods for composite optimization (Q2032020) (← links)
• Non-convergence of stochastic gradient descent in the training of deep neural networks (Q2034567) (← links)
• Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning (Q2039229) (← links)
• Computing Lyapunov functions using deep neural networks (Q2043422) (← links)
• A stochastic subspace approach to gradient-free optimization in high dimensions (Q2044475) (← links)
• Bias of homotopic gradient descent for the hinge loss (Q2045131) (← links)
• Regularization parameter selection for the low rank matrix recovery (Q2046538) (← links)
• Incremental without replacement sampling in nonconvex optimization (Q2046568) (← links)
• Stochastic generalized gradient methods for training nonconvex nonsmooth neural networks (Q2058689) (← links)
• Sequential convergence of AdaGrad algorithm for smooth convex optimization (Q2060570) (← links)
• The generalized equivalence of regularization and min-max robustification in linear mixed models (Q2062416) (← links)
• Adaptive optimization with periodic dither signals (Q2070015) (← links)
• On large-scale unconstrained optimization and arbitrary regularization (Q2070329) (← links)
• On the inexact scaled gradient projection method (Q2070333) (← links)
• Finding best approximation pairs for two intersections of closed convex sets (Q2070342) (← links)
• LSPIA, (stochastic) gradient descent, and parameter correction (Q2074869) (← links)
• Quasi-convex feasibility problems: subgradient methods and convergence rates (Q2076909) (← links)
• Remove the salt and pepper noise based on the high order total variation and the nuclear norm regularization (Q2079108) (← links)
• Quantized convolutional neural networks through the lens of partial differential equations (Q2079526) (← links)
• Convergence results of a nested decentralized gradient method for non-strongly convex problems (Q2082236) (← links)
• On the local convergence of a stochastic semismooth Newton method for nonsmooth nonconvex optimization (Q2082285) (← links)
• Dimension independent excess risk by stochastic gradient descent (Q2084455) (← links)
• Variable metric proximal stochastic variance reduced gradient methods for nonconvex nonsmooth optimization (Q2086938) (← links)
• SRKCD: a stabilized Runge-Kutta method for stochastic optimization (Q2088772) (← links)
• Stopping criteria for, and strong convergence of, stochastic gradient descent on Bottou-Curtis-Nocedal functions (Q2089787) (← links)
• Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery (Q2095535) (← links)