The following pages link to AdaGrad (Q33997):
Displaying 50 items.
- An adaptive Polyak heavy-ball method (Q2102380) (← links)
- Tackling algorithmic bias in neural-network classifiers using Wasserstein-2 regularization (Q2103876) (← links)
- Variational learning the SDC quantum protocol with gradient-based optimization (Q2106013) (← links)
- Variational inference with vine copulas: an efficient approach for Bayesian computer model calibration (Q2110192) (← links)
- On stochastic accelerated gradient with convergence rate (Q2111814) (← links)
- An inexact restoration-nonsmooth algorithm with variable accuracy for stochastic nonsmooth convex optimization problems in machine learning and stochastic linear complementarity problems (Q2112678) (← links)
- A hybrid stochastic optimization framework for composite nonconvex optimization (Q2118109) (← links)
- An efficient neural network method with plane wave activation functions for solving Helmholtz equation (Q2122592) (← links)
- Structure probing neural network deflation (Q2124019) (← links)
- Stronger data poisoning attacks break data sanitization defenses (Q2127214) (← links)
- SelectNet: self-paced learning for high-dimensional partial differential equations (Q2131038) (← links)
- On obtaining sparse semantic solutions for inverse problems, control, and neural network training (Q2132578) (← links)
- An augmented Lagrangian model for signal segmentation (Q2134637) (← links)
- Stochastic approximation method using diagonal positive-definite matrices for convex optimization with fixed point constraints (Q2138441) (← links)
- A general neural particle method for hydrodynamics modeling (Q2138776) (← links)
- Adaptive primal-dual stochastic gradient method for expectation-constrained convex stochastic programs (Q2146450) (← links)
- Improved architectures and training algorithms for deep operator networks (Q2149522) (← links)
- A stochastic extra-step quasi-Newton method for nonsmooth nonconvex optimization (Q2149551) (← links)
- Finite-sum smooth optimization with SARAH (Q2149950) (← links)
- Riemannian stochastic fixed point optimization algorithm (Q2159421) (← links)
- The machine learning in lithium-ion batteries: a review (Q2161664) (← links)
- Online active classification via margin-based and feature-based label queries (Q2163262) (← links)
- Interpreting rate-distortion of variational autoencoder and using model uncertainty for anomaly detection (Q2163846) (← links)
- Physics-informed distribution transformers via molecular dynamics and deep neural networks (Q2168329) (← links)
- The computational asymptotics of Gaussian variational inference and the Laplace approximation (Q2172111) (← links)
- Block layer decomposition schemes for training deep neural networks (Q2173515) (← links)
- Nonlinear approximation via compositions (Q2185653) (← links)
- Data science applications to string theory (Q2187812) (← links)
- Novel convolutional neural network architecture for improved pulmonary nodule classification on computed tomography (Q2192932) (← links)
- Inference, learning and attention mechanisms that exploit and preserve sparsity in CNNs (Q2193592) (← links)
- Monte Carlo co-ordinate ascent variational inference (Q2195834) (← links)
- A heuristic adaptive fast gradient method in stochastic optimization problems (Q2207619) (← links)
- Parallel sequential Monte Carlo for stochastic gradient-free nonconvex optimization (Q2209727) (← links)
- Robust unsupervised domain adaptation for neural networks via moment alignment (Q2212554) (← links)
- A brief introduction to manifold optimization (Q2218094) (← links)
- Optimization for deep learning: an overview (Q2218095) (← links)
- A review on deep learning in medical image reconstruction (Q2218098) (← links)
- How can machine learning and optimization help each other better? (Q2218099) (← links)
- Bi-fidelity stochastic gradient descent for structural optimization under uncertainty (Q2221705) (← links)
- Machine learning for fast and reliable solution of time-dependent differential equations (Q2222523) (← links)
- Model reduction of dynamical systems on nonlinear manifolds using deep convolutional autoencoders (Q2223001) (← links)
- Coercing machine learning to output physically accurate results (Q2223280) (← links)
- A linearly convergent stochastic recursive gradient method for convex optimization (Q2228399) (← links)
- Stochastic optimization with momentum: convergence, fluctuations, and traps avoidance (Q2233558) (← links)
- A nonlocal physics-informed deep learning framework using the peridynamic differential operator (Q2237731) (← links)
- Deep autoencoders for physics-constrained data-driven nonlinear materials modeling (Q2237774) (← links)
- Deep learning for quantile regression under right censoring: deepquantreg (Q2242148) (← links)
- A modular analysis of adaptive (non-)convex optimization: optimism, composite objectives, variance reduction, and variational bounds (Q2290691) (← links)
- Scale-invariant unconstrained online learning (Q2290692) (← links)
- Accelerating deep neural network training with inconsistent stochastic gradient descent (Q2292210) (← links)
