Loss aware post-training quantization Y Nahshan, B Chmiel, C Baskin, E Zheltonozhskii, R Banner, ... Machine Learning 110 (11), 3245-3262, 2021 | 173 | 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 | 101 | 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 | 81 | 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 | 50 | 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 | 27 | 2020 |
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 | 15 | 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 | 12 | 2022 |
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 | 12 | 2021 |
Logarithmic unbiased quantization: Practical 4-bit training in deep learning B Chmiel, R Banner, E Hoffer, HB Yaacov, D Soudry | 11 | 2021 |
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 | 8 | 2023 |
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 | 7 | 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 |
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 | 6 | 2019 |
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 |
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, 2023 | 5 | 2023 |
Bimodal-distributed binarized neural networks T Rozen, M Kimhi, B Chmiel, A Mendelson, C Baskin Mathematics 10 (21), 4107, 2022 | 5 | 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 |
EXAQ: Exponent Aware Quantization For LLMs Acceleration M Shkolnik, M Fishman, B Chmiel, H Ben-Yaacov, R Banner, KY Levy arXiv preprint arXiv:2410.03185, 2024 | | 2024 |
Scaling FP8 training to trillion-token LLMs M Fishman, B Chmiel, R Banner, D Soudry arXiv preprint arXiv:2409.12517, 2024 | | 2024 |
Compressing neural networks through unbiased minimum variance pruning B Chmiel, I Hubara, R Banner US Patent App. 18/164,875, 2024 | | 2024 |