← Keywords

stochastic gradient descent

1088 papers

Explore in graph

Also known as

SGD ASGD SAGA SGM SGDA PSGD SKGD

Co-occurring keywords

non-convex optimization (546) distributed learning (563) neural network optimization (1293) convergence rate (606) convex optimization (1320) neural network (6616) variance reduction (520) stochastic optimization (1060) convergence analysis (394) differential privacy (1010)

Papers

Pipe-SGD: A Decentralized Pipelined SGD Framework for Distributed Deep Net Training NIPS 2018
New Insight into Hybrid Stochastic Gradient Descent: Beyond With-Replacement Sampling and Convexity NIPS 2018
Weighted SGD for $\ell_p$ Regression with Randomized Preconditioning JMLR 2018
Variance-Reduced Stochastic Gradient Descent on Streaming Data NIPS 2018
Sparsified SGD with Memory NIPS 2018
Stochastic Chebyshev Gradient Descent for Spectral Optimization NIPS 2018
How SGD Selects the Global Minima in Over-parameterized Learning: A Dynamical Stability Perspective NIPS 2018
On Markov Chain Gradient Descent NIPS 2018
Katyusha: The First Direct Acceleration of Stochastic Gradient Methods JMLR 2018
Average Stability is Invariant to Data Preconditioning. Implications to Exp-concave Empirical Risk Minimization JMLR 2018
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation EMNLP 2018
Sliced Wasserstein Distance for Learning Gaussian Mixture Models CVPR 2018
Accelerating Convolutional Networks via Global & Dynamic Filter Pruning IJCAI 2018
Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification JMLR 2018
Gradient Descent Learns Linear Dynamical Systems JMLR 2018
An efficient distributed learning algorithm based on effective local functional approximations JMLR 2018
Improved Asynchronous Parallel Optimization Analysis for Stochastic Incremental Methods JMLR 2018
Distributed Stochastic Optimization via Adaptive SGD NIPS 2018
On Truly Block Eigensolvers via Riemannian Optimization AISTATS 2018
SADAGRAD: Strongly Adaptive Stochastic Gradient Methods ICML 2018
Not All Samples Are Created Equal: Deep Learning with Importance Sampling ICML 2018
Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients ICML 2018
MISSION: Ultra Large-Scale Feature Selection using Count-Sketches ICML 2018
How Much Restricted Isometry is Needed In Nonconvex Matrix Recovery? NIPS 2018
Parsimonious Bayesian deep networks NIPS 2018