10 citations · 19 across the 5 of their papers we have counts for
6 papers
Learning to Score
Yogev Kriger, Shai Fine
Common machine learning settings range from supervised tasks, where accurately labeled data is accessible, through semi-supervised and weakly-supervised tasks, where target labels…
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…
Actigraphy-based Sleep/Wake Pattern Detection using Convolutional Neural Networks
Lena Granovsky, Gabi Shalev, Nancy Yacovzada +2
Common medical conditions are often associated with sleep abnormalities. Patients with medical disorders often suffer from poor sleep quality compared to healthy individuals, which…
On the Blindspots of Convolutional Networks
Elad Hoffer, Shai Fine, Daniel Soudry
Deep convolutional network has been the state-of-the-art approach for a wide variety of tasks over the last few years. Its successes have, in many cases, turned it into the default…