2 citations · 4 across the 2 of their papers we have counts for
3 papers
cs.LG2020★ 2 cited
Margin-Based Regularization and Selective Sampling in Deep Neural Networks
Berry Weinstein, Shai Fine, Yacov Hel-Or
We derive a new margin-based regularization formulation, termed multi-margin regularization (MMR), for deep neural networks (DNNs). The MMR is inspired by principles that were appl…
cs.LG2019★ 2 cited
Selective sampling for accelerating training of deep neural networks
Berry Weinstein, Shai Fine, Yacov Hel-Or
We present a selective sampling method designed to accelerate the training of deep neural networks. To this end, we introduce a novel measurement, the minimal margin score (MMS), w…
cs.CV2019
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…