Pages that link to "Item:Q4641709"

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

Displaying 50 items.

• Stochastic proximal linear method for structured non-convex problems (Q5858986) (← links)
• Splitting proximal with penalization schemes for additive convex hierarchical minimization problems (Q5858997) (← links)
• Modes of Homogeneous Gradient Flows (Q5860347) (← links)
• An elastic net penalized small area model combining unit- and area-level data for regional hypertension prevalence estimation (Q5861599) (← links)
• Linear convergence of proximal incremental aggregated gradient method for nonconvex nonsmooth minimization problems (Q5865360) (← links)
• Classification, inference and segmentation of anomalous diffusion with recurrent neural networks (Q5874039) (← links)
• LSOS: Line-search second-order stochastic optimization methods for nonconvex finite sums (Q5879118) (← links)
• Stochastic algorithms for self-consistent calculations of electronic structures (Q5886872) (← links)
• Lower bounds for non-convex stochastic optimization (Q6038643) (← links)
• An adaptive stochastic sequential quadratic programming with differentiable exact augmented Lagrangians (Q6038658) (← links)
• Stochastic gradient descent with noise of machine learning type. I: Discrete time analysis (Q6038848) (← links)
• ALMOND: Adaptive Latent Modeling and Optimization via Neural Networks and Langevin Diffusion (Q6040682) (← links)
• A stochastic gradient method for a class of nonlinear PDE-constrained optimal control problems under uncertainty (Q6041823) (← links)
• Convergence analysis of a subsampled Levenberg-Marquardt algorithm (Q6047687) (← links)
• Gauss-Newton method for solving linear inverse problems with neural network coders (Q6049832) (← links)
• A nonlinear conjugate gradient method using inexact first-order information (Q6051169) (← links)
• SCORE: approximating curvature information under self-concordant regularization (Q6051307) (← links)
• A dual-based stochastic inexact algorithm for a class of stochastic nonsmooth convex composite problems (Q6051310) (← links)
• Inequality constrained stochastic nonlinear optimization via active-set sequential quadratic programming (Q6052061) (← links)
• A trust region method for noisy unconstrained optimization (Q6052069) (← links)
• Principled deep neural network training through linear programming (Q6054389) (← links)
• A distributed optimisation framework combining natural gradient with Hessian-free for discriminative sequence training (Q6055115) (← links)
• Epistemic uncertainty quantification in deep learning classification by the delta method (Q6055168) (← links)
• Convergence analysis of AdaBound with relaxed bound functions for non-convex optimization (Q6055172) (← links)
• Stochastic momentum methods for non-convex learning without bounded assumptions (Q6057975) (← links)
• Improved variance reduction extragradient method with line search for stochastic variational inequalities (Q6064028) (← links)
• Three ways to solve partial differential equations with neural networks — A review (Q6068232) (← links)
• An introduction to deep generative modeling (Q6068234) (← links)
• An adaptive sampling augmented Lagrangian method for stochastic optimization with deterministic constraints (Q6072951) (← links)
• Subgradient Sampling for Nonsmooth Nonconvex Minimization (Q6076858) (← links)
• Parameter estimation in a 3‐parameter <i>p</i>‐star random graph model (Q6087121) (← links)
• Finite-time convergence rates of distributed local stochastic approximation (Q6088356) (← links)
• Structured learning of rigid‐body dynamics: A survey and unified view from a robotics perspective (Q6089645) (← links)
• Adaptive stochastic gradient descent for optimal control of parabolic equations with random parameters (Q6090392) (← links)
• Solving Elliptic Problems with Singular Sources Using Singularity Splitting Deep Ritz Method (Q6095431) (← links)
• Time discretization in the solution of parabolic PDEs with ANNs (Q6096361) (← links)
• Scaling up stochastic gradient descent for non-convex optimisation (Q6097095) (← links)
• An overview of stochastic quasi-Newton methods for large-scale machine learning (Q6097379) (← links)
• A mini-batch proximal stochastic recursive gradient algorithm with diagonal Barzilai-Borwein stepsize (Q6097380) (← links)
• A framework of convergence analysis of mini-batch stochastic projected gradient methods (Q6097385) (← links)
• Derivation of coordinate descent algorithms from optimal control theory (Q6097432) (← links)
• A New Certified Hierarchical and Adaptive RB-ML-ROM Surrogate Model for Parametrized PDEs (Q6097873) (← links)
• Neural network-based limiter with transfer learning (Q6098315) (← links)
• Stochastic mirror descent method for linear ill-posed problems in Banach spaces (Q6101039) (← links)
• On mathematical optimization for clustering categories in contingency tables (Q6106171) (← links)
• Globally Convergent Multilevel Training of Deep Residual Networks (Q6108152) (← links)
• Convergence rates of the stochastic alternating algorithm for bi-objective optimization (Q6108978) (← links)
• On the Generalized Langevin Equation for Simulated Annealing (Q6109158) (← links)
• A gradient-based reinforcement learning model of market equilibration (Q6111438) (← links)
• New metrics and tests for subject prevalence in documents based on topic modeling (Q6114043) (← links)