| Loss aware post-training quantization Y Nahshan, B Chmiel, C Baskin, E Zheltonozhskii, R Banner, ... Machine Learning 110 (11), 3245-3262, 2021 | 136 | 2021 |
| Accelerated sparse neural training: A provable and efficient method to find n: m transposable masks I Hubara, B Chmiel, M Island, R Banner, J Naor, D Soudry Advances in neural information processing systems 34, 21099-21111, 2021 | 74 | 2021 |
| Robust quantization: One model to rule them all B Chmiel, R Banner, G Shomron, Y Nahshan, A Bronstein, U Weiser Advances in neural information processing systems 33, 5308-5317, 2020 | 72 | 2020 |
| Neural gradients are near-lognormal: improved quantized and sparse training B Chmiel, L Ben-Uri, M Shkolnik, E Hoffer, R Banner, D Soudry arXiv preprint arXiv:2006.08173, 2020 | 42 | 2020 |
| Feature map transform coding for energy-efficient CNN inference B Chmiel, C Baskin, E Zheltonozhskii, R Banner, Y Yermolin, ... 2020 International Joint Conference on Neural Networks (IJCNN), 1-9, 2020 | 26 | 2020 |
| Logarithmic unbiased quantization: Practical 4-bit training in deep learning B Chmiel, R Banner, E Hoffer, HB Yaacov, D Soudry | 11 | 2021 |
| CAT: Compression-Aware Training for bandwidth reduction C Baskin, B Chmiel, E Zheltonozhskii, R Banner, AM Bronstein, ... Journal of Machine Learning Research 22 (269), 1-20, 2021 | 11 | 2021 |
| Optimal fine-grained n: M sparsity for activations and neural gradients B Chmiel, I Hubara, R Banner, D Soudry arXiv preprint arXiv:2203.10991, 2022 | 10 | 2022 |
| Logarithmic unbiased quantization: Simple 4-bit training in deep learning B Chmiel, R Banner, E Hoffer, HB Yaacov, D Soudry arXiv preprint arXiv:2112.10769, 2021 | 10 | 2021 |
| Colored noise injection for training adversarially robust neural networks E Zheltonozhskii, C Baskin, Y Nemcovsky, B Chmiel, A Mendelson, ... arXiv preprint arXiv:2003.02188, 2020 | 6 | 2020 |
| 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 | 6 | 2020 |
| Adversarial robustness via noise injection in smoothed models Y Nemcovsky, E Zheltonozhskii, C Baskin, B Chmiel, AM Bronstein, ... Applied Intelligence 53 (8), 9483-9498, 2023 | 5 | 2023 |
| Towards learning of filter-level heterogeneous compression of convolutional neural networks Y Zur, C Baskin, E Zheltonozhskii, B Chmiel, I Evron, AM Bronstein, ... arXiv preprint arXiv:1904.09872, 2019 | 5 | 2019 |
| Bimodal-distributed binarized neural networks T Rozen, M Kimhi, B Chmiel, A Mendelson, C Baskin Mathematics 10 (21), 4107, 2022 | 4 | 2022 |
| Minimum variance unbiased n: M sparsity for the neural gradients B Chmiel, I Hubara, R Banner, D Soudry The Eleventh International Conference on Learning Representations, 2022 | 3 | 2022 |
| 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, 2022 | 2 | 2022 |
| Smoothed inference for adversarially-trained models Y Nemcovsky, E Zheltonozhskii, C Baskin, B Chmiel, M Fishman, ... arXiv preprint arXiv:1911.07198, 2019 | 2 | 2019 |
Ron BannerMachine Learning Researcher @ Intel & TechnionVerified email at intel.com
