2.2k citations · 5.3k across the 18 of their papers we have counts for
4 papers · 1 filter
Training of Quantized Deep Neural Networks using a Magnetic Tunnel Junction-Based Synapse
Tzofnat Greenberg Toledo, Ben Perach, Itay Hubara +2
Quantized neural networks (QNNs) are being actively researched as a solution for the computational complexity and memory intensity of deep neural networks. This has sparked efforts…
The Knowledge Within: Methods for Data-Free Model Compression
Matan Haroush, Itay Hubara, Elad Hoffer +1
Recently, an extensive amount of research has been focused on compressing and accelerating Deep Neural Networks (DNN). So far, high compression rate algorithms require part of the…
Mix & Match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency
Elad Hoffer, Berry Weinstein, Itay Hubara +3
Convolutional neural networks (CNNs) are commonly trained using a fixed spatial image size predetermined for a given model. Although trained on images of aspecific size, it is well…
Augment your batch: better training with larger batches
Elad Hoffer, Tal Ben-Nun, Itay Hubara +3
Large-batch SGD is important for scaling training of deep neural networks. However, without fine-tuning hyperparameter schedules, the generalization of the model may be hampered. W…