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Found 3 papers, 1 papers with code
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism
We present scalable hybrid-parallel algorithms for training large-scale 3D convolutional neural networks.
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism
We also see an emerging need to handle datasets with very large samples, where memory requirements for training are large.
Effective Quantization Approaches for Recurrent Neural Networks
These proposed approaches are evaluated on different datasets for sentiment analysis on IMDB and video frame predictions on the moving MNIST dataset.
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