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Distributed Training of Deep Neural Networks: Theoretical and Practical Limits of Parallel Scalability

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-319-58943-5_64openalex-snapshot
Theano-MPI: A Theano-Based Distributed Training Framework
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Large-Scale Machine Learning with Stochastic Gradient Descent
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ImageNet Large Scale Visual Recognition Challenge
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Deep learning in neural networks: An overview
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Deep learning
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Going deeper with convolutions
inferredcites →https://doi.org/10.1109/cvpr.2016.284openalex-snapshot
FireCaffe: Near-Linear Acceleration of Deep Neural Network Training on Compute Clusters
inferredcites →https://doi.org/10.1109/icassp.2014.6853593openalex-snapshot
On parallelizability of stochastic gradient descent for speech DNNS
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Communication-Optimal Parallel Recursive Rectangular Matrix Multiplication
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Caffe
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ImageNet classification with deep convolutional neural networks
inferredcites →https://doi.org/10.1145/3097983.3098035openalex-snapshot
Scalable and Sustainable Deep Learning via Randomized Hashing
inferredcites →https://doi.org/10.48550/arxiv.1404.5997openalex-snapshot
One weird trick for parallelizing convolutional neural networks
inferredcites →https://doi.org/10.48550/arxiv.1408.5093openalex-snapshot
Caffe: Convolutional Architecture for Fast Feature Embedding
inferredcites →https://doi.org/10.48550/arxiv.1409.1556openalex-snapshot
Very Deep Convolutional Networks for Large-Scale Image Recognition
inferredcites →https://doi.org/10.48550/arxiv.1502.02551openalex-snapshot
Deep Learning with Limited Numerical Precision
inferredcites →https://doi.org/10.48550/arxiv.1602.07360openalex-snapshot
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
inferredcites →https://doi.org/10.48550/arxiv.1603.04467openalex-snapshot
TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems
inferredcites →https://doi.org/10.48550/arxiv.1609.04836openalex-snapshot
On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima

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