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Stochastic Optimization

99 papers with code · Methodology

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Revisiting Distributed Synchronous SGD

4 Apr 2016tensorflow/models

Distributed training of deep learning models on large-scale training data is typically conducted with asynchronous stochastic optimization to maximize the rate of updates, at the cost of additional noise introduced from asynchrony.

STOCHASTIC OPTIMIZATION

Reducing the variance in online optimization by transporting past gradients

NeurIPS 2019 google-research/google-research

While variance reduction methods have shown that reusing past gradients can be beneficial when there is a finite number of datapoints, they do not easily extend to the online setting.

STOCHASTIC OPTIMIZATION

Lookahead Optimizer: k steps forward, 1 step back

NeurIPS 2019 rwightman/pytorch-image-models

The vast majority of successful deep neural networks are trained using variants of stochastic gradient descent (SGD) algorithms.

IMAGE CLASSIFICATION MACHINE TRANSLATION STOCHASTIC OPTIMIZATION

SGDR: Stochastic Gradient Descent with Warm Restarts

13 Aug 2016rwightman/pytorch-image-models

Partial warm restarts are also gaining popularity in gradient-based optimization to improve the rate of convergence in accelerated gradient schemes to deal with ill-conditioned functions.

EEG STOCHASTIC OPTIMIZATION

Adaptive Gradient Methods with Dynamic Bound of Learning Rate

ICLR 2019 Luolc/AdaBound

Adaptive optimization methods such as AdaGrad, RMSProp and Adam have been proposed to achieve a rapid training process with an element-wise scaling term on learning rates.

STOCHASTIC OPTIMIZATION

Large Batch Optimization for Deep Learning: Training BERT in 76 minutes

ICLR 2020 microsoft/DeepSpeed

In this paper, we first study a principled layerwise adaptation strategy to accelerate training of deep neural networks using large mini-batches.

#10 best model for Question Answering on SQuAD1.1 dev (F1 metric)

QUESTION ANSWERING STOCHASTIC OPTIMIZATION

On the Variance of the Adaptive Learning Rate and Beyond

8 Aug 2019LiyuanLucasLiu/RAdam

The learning rate warmup heuristic achieves remarkable success in stabilizing training, accelerating convergence and improving generalization for adaptive stochastic optimization algorithms like RMSprop and Adam.

IMAGE CLASSIFICATION LANGUAGE MODELLING MACHINE TRANSLATION STOCHASTIC OPTIMIZATION

Stochastic Gradient Methods with Layer-wise Adaptive Moments for Training of Deep Networks

27 May 2019NVIDIA/OpenSeq2Seq

We propose NovoGrad, an adaptive stochastic gradient descent method with layer-wise gradient normalization and decoupled weight decay.

CLASSIFICATION STOCHASTIC OPTIMIZATION

An Adaptive and Momental Bound Method for Stochastic Learning

27 Oct 2019jettify/pytorch-optimizer

The dynamic learning rate bounds are based on the exponential moving averages of the adaptive learning rates themselves, which smooth out unexpected large learning rates and stabilize the training of deep neural networks.

STOCHASTIC OPTIMIZATION

Adaptive Methods for Nonconvex Optimization

NeurIPS 2018 jettify/pytorch-optimizer

In this work, we provide a new analysis of such methods applied to nonconvex stochastic optimization problems, characterizing the effect of increasing minibatch size.

STOCHASTIC OPTIMIZATION