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← Optimization & Theory
Deep Learning
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Optimization & Theory
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Stochastic Methods
97 directly classified papers
Papers per year
2007: 1
2010: 1
2013: 2
2014: 1
2015: 6
2016: 4
2017: 4
2018: 9
2019: 12
2020: 9
2021: 17
2022: 14
2023: 5
2024: 9
2025: 3
Papers
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
Gradient Estimation with Stochastic Softmax Tricks
NIPS 2020
Minibatch vs Local SGD for Heterogeneous Distributed Learning
NIPS 2020
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
Parallel Restarted SGD with Faster Convergence and Less Communication: Demystifying Why Model Averaging Works for Deep Learning
AAAI 2019
Scalable and Efficient Pairwise Learning to Achieve Statistical Accuracy
AAAI 2019
Communication-Efficient Stochastic Gradient MCMC for Neural Networks
AAAI 2019
Making Asynchronous Stochastic Gradient Descent Work for Transformers
EMNLP 2019
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
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
LAG: Lazily Aggregated Gradient for Communication-Efficient Distributed Learning
NIPS 2018
Accelerating Asynchronous Stochastic Gradient Descent for Neural Machine Translation
EMNLP 2018
Statistical Tomography of Microscopic Life
CVPR 2018
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