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2020 – today
- 2024
[i14]Maxim Fishman, Brian Chmiel, Ron Banner, Daniel Soudry:
Scaling FP8 training to trillion-token LLMs. CoRR abs/2409.12517 (2024)- [i13]Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy:
EXAQ: Exponent Aware Quantization For LLMs Acceleration. CoRR abs/2410.03185 (2024) - 2023
- [j3]Yaniv Nemcovsky, Evgenii Zheltonozhskii
, Chaim Baskin, Brian Chmiel, Alex M. Bronstein, Avi Mendelson:
Adversarial robustness via noise injection in smoothed models. Appl. Intell. 53(8): 9483-9498 (2023) - [c6]Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry:
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. ICLR 2023 - [c5]Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry:
Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. ICLR 2023 - 2022
- [i12]Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry:
Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients. CoRR abs/2203.10991 (2022) - [i11]Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin:
Bimodal Distributed Binarized Neural Networks. CoRR abs/2204.02004 (2022) - 2021
- [j2]Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson:
CAT: Compression-Aware Training for bandwidth reduction. J. Mach. Learn. Res. 22: 269:1-269:20 (2021) - [j1]Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson:
Loss aware post-training quantization. Mach. Learn. 110(11): 3245-3262 (2021) - [c4]Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry:
Neural gradients are near-lognormal: improved quantized and sparse training. ICLR 2021 - [c3]Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, Daniel Soudry:
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. NeurIPS 2021: 21099-21111 - [i10]Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Seffi Naor, Daniel Soudry:
Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. CoRR abs/2102.08124 (2021) - [i9]Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry:
Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. CoRR abs/2112.10769 (2021) - 2020
- [c2]Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Yevgeny Yermolin, Alex Karbachevsky, Alex M. Bronstein, Avi Mendelson:
Feature Map Transform Coding for Energy-Efficient CNN Inference. IJCNN 2020: 1-9 - [c1]Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex M. Bronstein, Uri C. Weiser:
Robust Quantization: One Model to Rule Them All. NeurIPS 2020 - [i8]Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alexander M. Bronstein, Uri C. Weiser:
Robust Quantization: One Model to Rule Them All. CoRR abs/2002.07686 (2020) - [i7]Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein:
Colored Noise Injection for Training Adversarially Robust Neural Networks. CoRR abs/2003.02188 (2020) - [i6]Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry:
Neural gradients are lognormally distributed: understanding sparse and quantized training. CoRR abs/2006.08173 (2020)
2010 – 2019
- 2019
- [i5]Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alexander M. Bronstein, Avi Mendelson:
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks. CoRR abs/1904.09872 (2019) - [i4]Brian Chmiel, Chaim Baskin, Ron Banner, Evgenii Zheltonozhskii, Yevgeny Yermolin, Alex Karbachevsky, Alexander M. Bronstein, Avi Mendelson:
Feature Map Transform Coding for Energy-Efficient CNN Inference. CoRR abs/1905.10830 (2019) - [i3]Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson:
CAT: Compression-Aware Training for bandwidth reduction. CoRR abs/1909.11481 (2019) - [i2]Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson:
Loss Aware Post-training Quantization. CoRR abs/1911.07190 (2019) - [i1]Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alexander M. Bronstein, Avi Mendelson:
Smoothed Inference for Adversarially-Trained Models. CoRR abs/1911.07198 (2019)
Coauthor Index
aka: Alex M. Bronstein
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