Search Results for author:
Found 4 papers, 3 papers with code
DropCompute: simple and more robust distributed synchronous training via compute variance reduction
Thus, these methods are limited by the delays caused by straggling workers.
Neural gradients are near-lognormal: improved quantized and sparse training
While training can mostly be accelerated by reducing the time needed to propagate neural gradients back throughout the model, most previous works focus on the quantization/pruning of weights and activations.
Robust Quantization: One Model to Rule Them All
Neural network quantization methods often involve simulating the quantization process during training, making the trained model highly dependent on the target bit-width and precise way quantization is performed.
Thanks for Nothing: Predicting Zero-Valued Activations with Lightweight Convolutional Neural Networks
Convolutional neural networks (CNNs) introduce state-of-the-art results for various tasks with the price of high computational demands.
