3 citations · 7 across the 3 of their papers we have counts for
4 papers
Pairwise Margin Maximization for Deep Neural Networks
Berry Weinstein, Shai Fine, Yacov Hel-Or
The weight decay regularization term is widely used during training to constrain expressivity, avoid overfitting, and improve generalization. Historically, this concept was borrowe…
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…
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…
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…