[PDF] Which Algorithmic Choices Matter at Which Batch Sizes? - NIPS
Abstract Increasing the batch size is a popular way to speed up neural network training, optimal learning rates and large batch training, making it a useful tool to generate testable More commonly (especially in deep learning), exponential
[PDF] Control Batch Size and Learning Rate to Generalize Well - NeurIPS
When employing SGD to train deep neural networks, we should control the batch batch size S Considering deep learning is usually utilized to process large- scale data, this batch size has an optimal value when the learning rate is fixed
[PDF] ONLINE BATCH SELECTION FOR FASTER TRAINING OF NEURAL
Deep neural networks (DNNs) are currently the best-performing method for many in a batch Instead, it is common in SGD to fix the batch size and iteratively
[PDF] Why Does Large Batch Training Result in Poor - HUSCAP
ponents of machine learning because a better solution generally leads to a more accurate prediction for ally optimal solution (Lundy Mees, 1986) In actual use, the Training with a large batch size in a neural network tends to result in
[PDF] CROSSBOW: Scaling Deep Learning with Small Batch Sizes on
tem for training deep learning models that enables users to freely choose their preferred batch size—however small—while scaling to multiple GPUs
[PDF] Train Deep Neural Networks with Small Batch Sizes - IJCAI
To the best of our knowledge, TRAlgo is the first to use such tech- nique to address noisy gradients, under the small batch size constraint The remainder of this
[PDF] On the Generalization Benefit of Noise in Stochastic Gradient Descent
batch gradient descent in deep neural networks However recent papers have hand, when the batch size is small, we expect the optimal learning rate to be
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