Pages that link to "Item:Q4641709"
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The following pages link to Optimization Methods for Large-Scale Machine Learning (Q4641709):
Displaying 50 items.
- SABRINA: a stochastic subspace majorization-minimization algorithm (Q2095568) (← links)
- Adaptive sampling line search for local stochastic optimization with integer variables (Q2097662) (← links)
- Finite-sample analysis of nonlinear stochastic approximation with applications in reinforcement learning (Q2097782) (← links)
- On the convergence of a block-coordinate incremental gradient method (Q2100401) (← links)
- Triangularized orthogonalization-free method for solving extreme eigenvalue problems (Q2103436) (← links)
- Tackling algorithmic bias in neural-network classifiers using Wasserstein-2 regularization (Q2103876) (← links)
- A stochastic gradient algorithm with momentum terms for optimal control problems governed by a convection-diffusion equation with random diffusivity (Q2104094) (← links)
- A subsampling approach for Bayesian model selection (Q2105562) (← links)
- Convergence analysis of machine learning algorithms for the numerical solution of mean field control and games. II: The finite horizon case (Q2108885) (← links)
- A stochastic first-order trust-region method with inexact restoration for finite-sum minimization (Q2111466) (← links)
- A nested primal-dual FISTA-like scheme for composite convex optimization problems (Q2111467) (← links)
- Inertial accelerated SGD algorithms for solving large-scale lower-rank tensor CP decomposition problems (Q2112682) (← links)
- A deep domain decomposition method based on Fourier features (Q2112697) (← links)
- Generating Nesterov's accelerated gradient algorithm by using optimal control theory for optimization (Q2112702) (← links)
- Accelerating variance-reduced stochastic gradient methods (Q2118092) (← links)
- Adaptive two-layer ReLU neural network. I: Best least-squares approximation (Q2122629) (← links)
- Adaptive two-layer ReLU neural network. II: Ritz approximation to elliptic PDEs (Q2122635) (← links)
- Stochastic gradient descent for semilinear elliptic equations with uncertainties (Q2127008) (← links)
- Accelerating mini-batch SARAH by step size rules (Q2127094) (← links)
- ODE-RU: a dynamical system view on recurrent neural networks (Q2127486) (← links)
- Stochastic quasi-subgradient method for stochastic quasi-convex feasibility problems (Q2129140) (← links)
- An online conjugate gradient algorithm for large-scale data analysis in machine learning (Q2131556) (← links)
- On obtaining sparse semantic solutions for inverse problems, control, and neural network training (Q2132578) (← links)
- Self-adaptive deep neural network: numerical approximation to functions and PDEs (Q2133768) (← links)
- The mixed deep energy method for resolving concentration features in finite strain hyperelasticity (Q2134762) (← links)
- Adaptive deep density approximation for Fokker-Planck equations (Q2135831) (← links)
- Feasibility-based fixed point networks (Q2138454) (← links)
- Online statistical inference for parameters estimation with linear-equality constraints (Q2146461) (← links)
- A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization (Q2149551) (← links)
- Finite-sum smooth optimization with SARAH (Q2149950) (← links)
- Ritz-like values in steplength selections for stochastic gradient methods (Q2156893) (← links)
- Model order reduction method based on (r)POD-ANNs for parameterized time-dependent partial differential equations (Q2158140) (← links)
- Retracted: Model order reduction method based on machine learning for parameterized time-dependent partial differential equations (Q2161825) (← links)
- Sub-linear convergence of a stochastic proximal iteration method in Hilbert space (Q2162529) (← links)
- Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection (Q2163846) (← links)
- A proof of convergence for stochastic gradient descent in the training of artificial neural networks with ReLU activation for constant target functions (Q2167333) (← links)
- Laplacian smoothing gradient descent (Q2168883) (← links)
- Block layer decomposition schemes for training deep neural networks (Q2173515) (← links)
- A distributed conjugate gradient online learning method over networks (Q2173726) (← links)
- Subsampled nonmonotone spectral gradient methods (Q2178981) (← links)
- A regularization interpretation of the proximal point method for weakly convex functions (Q2179443) (← links)
- SHOPPER: a probabilistic model of consumer choice with substitutes and complements (Q2179937) (← links)
- Accelerating incremental gradient optimization with curvature information (Q2181597) (← links)
- Two-point step size gradient method for solving a deep learning problem (Q2190628) (← links)
- Inexact restoration with subsampled trust-region methods for finite-sum minimization (Q2191786) (← links)
- Recursive estimation for sparse Gaussian process regression (Q2203074) (← links)
- A Bayesian perspective of statistical machine learning for big data (Q2203387) (← links)
- Newton-type methods for non-convex optimization under inexact Hessian information (Q2205970) (← links)
- Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization (Q2209727) (← links)
- ROCKET: exceptionally fast and accurate time series classification using random convolutional kernels (Q2212518) (← links)
