| Mix & match: training convnets with mixed image sizes for improved accuracy, speed and scale resiliency E Hoffer, B Weinstein, I Hubara, T Ben-Nun, T Hoefler, D Soudry arXiv preprint arXiv:1908.08986, 2019 | 28 | 2019 |
| Infer2train: leveraging inference for better training of deep networks E Hoffer, B Weinstein, I Hubara, S Gofman, D Soudry NeurIPS 2018 Workshop on Systems for ML, 2018 | 3 | 2018 |
| Margin-based regularization and selective sampling in deep neural networks B Weinstein, S Fine, Y Hel-Or arXiv preprint arXiv:2009.06011, 2020 | 2 | 2020 |
| Pairwise Margin Maximization for Deep Neural Networks B Weinstein, S Fine, Y Hel-Or 2021 20th IEEE International Conference on Machine Learning and Applications …, 2021 | | 2021 |
| Selective Sampling and Margin-Based Regularization in Deep Neural Networks B Weinstein PQDT-Global, 2020 | | 2020 |
| Discovering Discrete Hidden Variables in Mixed Networks: A Statistical Hypothesis-Testing Approach A Peled PQDT-Global, 2020 | | 2020 |
| Variance Pruning: Pruning Language Models via Temporal Neuron Variance B Weinstein, Y Belinkov | | |
