conftrace_
← Optimization & Theory
Deep Learning › Optimization & Theory ›

Stochastic Methods

97 papers

Papers per year

1
6
9
3
'10
'15
'20
'25

Papers

Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC NIPS 2020
Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations NIPS 2020
Deep Reservoir Computing Meets 5G MIMO-OFDM Systems in Symbol Detection AAAI 2020
On the Discrepancy between the Theoretical Analysis and Practical Implementations of Compressed Communication for Distributed Deep Learning AAAI 2020
Bundle Adjustment on a Graph Processor CVPR 2020
An Investigation Into the Stochasticity of Batch Whitening CVPR 2020
Amortized variance reduction for doubly stochastic objective UAI 2020
DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation NIPS 2019
Communication trade-offs for Local-SGD with large step size NIPS 2019
Reducing Noise in GAN Training with Variance Reduced Extragradient NIPS 2019
Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond NIPS 2019
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback NIPS 2019
Momentum-Based Variance Reduction in Non-Convex SGD NIPS 2019
Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations NIPS 2019
Sampled Softmax with Random Fourier Features NIPS 2019
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy AAAI 2019
Communication-Efficient Stochastic Gradient MCMC for Neural Networks AAAI 2019
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning AAAI 2019
Making Asynchronous Stochastic Gradient Descent Work for Transformers EMNLP 2019
SPIDER: Near-Optimal Non-Convex Optimization via Stochastic Path-Integrated Differential Estimator NIPS 2018
A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication NIPS 2018
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks NIPS 2018
Training Deep Models Faster with Robust, Approximate Importance Sampling NIPS 2018
How To Make the Gradients Small Stochastically: Even Faster Convex and Nonconvex SGD NIPS 2018
Dimensionality Reduction for Stationary Time Series via Stochastic Nonconvex Optimization NIPS 2018