| Deep metric learning using triplet network E Hoffer, N Ailon Similarity-based pattern recognition: third international workshop, SIMBAD …, 2015 | 2486 | 2015 |
| Train longer, generalize better: closing the generalization gap in large batch training of neural networks E Hoffer, I Hubara, D Soudry Advances in neural information processing systems 30, 2017 | 965 | 2017 |
| The implicit bias of gradient descent on separable data D Soudry, E Hoffer, MS Nacson, S Gunasekar, N Srebro Journal of Machine Learning Research 19 (70), 1-57, 2018 | 953 | 2018 |
| Scalable methods for 8-bit training of neural networks R Banner, I Hubara, E Hoffer, D Soudry Advances in neural information processing systems 31, 2018 | 382 | 2018 |
| Augment your batch: Improving generalization through instance repetition E Hoffer, T Ben-Nun, I Hubara, N Giladi, T Hoefler, D Soudry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 302* | 2020 |
| Norm matters: efficient and accurate normalization schemes in deep networks E Hoffer, R Banner, I Golan, D Soudry Advances in Neural Information Processing Systems 31, 2018 | 183 | 2018 |
| Bayesian gradient descent: Online variational Bayes learning with increased robustness to catastrophic forgetting and weight pruning C Zeno, I Golan, E Hoffer, D Soudry arXiv preprint arXiv:1803.10123, 2018 | 127* | 2018 |
| The knowledge within: Methods for data-free model compression M Haroush, I Hubara, E Hoffer, D Soudry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 109 | 2020 |
| Fix your classifier: the marginal value of training the last weight layer E Hoffer, I Hubara, D Soudry arXiv preprint arXiv:1801.04540, 2018 | 108 | 2018 |
| Exponentially vanishing sub-optimal local minima in multilayer neural networks D Soudry, E Hoffer arXiv preprint arXiv:1702.05777, 2017 | 104 | 2017 |
| Aciq: Analytical clipping for integer quantization of neural networks R Banner, Y Nahshan, E Hoffer, D Soudry | 82 | 2018 |
| Neural gradients are lognormally distributed: understanding sparse and quantized training B Chmiel, L Ben-Uri, M Shkolnik, E Hoffer, R Banner, D Soudry arXiv, 2020 | 51* | 2020 |
| Task-agnostic continual learning using online variational bayes with fixed-point updates C Zeno, I Golan, E Hoffer, D Soudry Neural Computation 33 (11), 3139-3177, 2021 | 50* | 2021 |
| Semi-supervised deep learning by metric embedding E Hoffer, N Ailon arXiv preprint arXiv:1611.01449, 2016 | 40 | 2016 |
| Deep unsupervised learning through spatial contrasting E Hoffer, I Hubara, N Ailon arXiv preprint arXiv:1610.00243, 2016 | 34 | 2016 |
| 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 | 25 | 2019 |
| Logarithmic unbiased quantization: Practical 4-bit training in deep learning B Chmiel, R Banner, E Hoffer, HB Yaacov, D Soudry | 23* | 2021 |
| At Stability's Edge: How to Adjust Hyperparameters to Preserve Minima Selection in Asynchronous Training of Neural Networks? N Giladi, MS Nacson, E Hoffer, D Soudry arXiv preprint arXiv:1909.12340, 2019 | 19 | 2019 |
| Quantized back-propagation: Training binarized neural networks with quantized gradients I Hubara, E Hoffer, D Soudry | 6 | 2018 |
| Accurate neural training with 4-bit matrix multiplications at standard formats B Chmiel, R Banner, E Hoffer, H Ben-Yaacov, D Soudry The Eleventh International Conference on Learning Representations, 2023 | 4 | 2023 |
Daniel SoudryAssociate Professor在 technion.ac.il 的电子邮件经过验证
