Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks. - Yochai Zur; Chaim Baskin; Evgenii Zheltonozhskii; Brian Chmiel; Itay Evron; Alexander M. Bronstein; Avi Mendelson (CoRR, 2019)

Smoothed Inference for Adversarially-Trained Models. - Yaniv Nemcovsky; Evgenii Zheltonozhskii; Chaim Baskin; Brian Chmiel; Alexander M. Bronstein; Avi Mendelson (CoRR, 2019)

Feature Map Transform Coding for Energy-Efficient CNN Inference. - Brian Chmiel; Chaim Baskin; Evgenii Zheltonozhskii; Ron Banner; Yevgeny Yermolin; Alex Karbachevsky; Alex M. Bronstein; Avi Mendelson (IJCNN, 2020)

Robust Quantization: One Model to Rule Them All. - Moran Shkolnik; Brian Chmiel; Ron Banner; Gil Shomron; Yury Nahshan; Alex M. Bronstein; Uri C. Weiser (NeurIPS, 2020)

Colored Noise Injection for Training Adversarially Robust Neural Networks. - Evgenii Zheltonozhskii; Chaim Baskin; Yaniv Nemcovsky; Brian Chmiel; Avi Mendelson; Alex M. Bronstein (CoRR, 2020)

Neural gradients are lognormally distributed: understanding sparse and quantized training. - Brian Chmiel; Liad Ben-Uri; Moran Shkolnik; Elad Hoffer; Ron Banner; Daniel Soudry (CoRR, 2020)

Neural gradients are near-lognormal: improved quantized and sparse training. - Brian Chmiel; Liad Ben-Uri; Moran Shkolnik; Elad Hoffer; Ron Banner; Daniel Soudry (ICLR, 2021)

Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. - Itay Hubara; Brian Chmiel; Moshe Island; Ron Banner; Joseph Naor; Daniel Soudry (NeurIPS, 2021)

Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. - Brian Chmiel; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Daniel Soudry (CoRR, 2021)

CAT: Compression-Aware Training for bandwidth reduction. - Chaim Baskin; Brian Chmiel; Evgenii Zheltonozhskii; Ron Banner; Alex M. Bronstein; Avi Mendelson (J. Mach. Learn. Res., 2021)

Loss aware post-training quantization. - Yury Nahshan; Brian Chmiel; Chaim Baskin; Evgenii Zheltonozhskii; Ron Banner; Alex M. Bronstein; Avi Mendelson (Mach. Learn., 2021)

Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients. - Brian Chmiel; Itay Hubara; Ron Banner; Daniel Soudry (CoRR, 2022)

Bimodal Distributed Binarized Neural Networks. - Tal Rozen; Moshe Kimhi; Brian Chmiel; Avi Mendelson; Chaim Baskin (CoRR, 2022)

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. - Brian Chmiel; Ron Banner; Elad Hoffer; Hilla Ben-Yaacov; Daniel Soudry (ICLR, 2023)

Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. - Brian Chmiel; Itay Hubara; Ron Banner; Daniel Soudry (ICLR, 2023)

Adversarial robustness via noise injection in smoothed models. - Yaniv Nemcovsky; Evgenii Zheltonozhskii; Chaim Baskin; Brian Chmiel; Alex M. Bronstein; Avi Mendelson (Appl. Intell., 2023)

EXAQ: Exponent Aware Quantization For LLMs Acceleration. - Moran Shkolnik; Maxim Fishman; Brian Chmiel; Hilla Ben-Yaacov; Ron Banner; Kfir Yehuda Levy (CoRR, 2024)

Scaling FP8 training to trillion-token LLMs. - Maxim Fishman; Brian Chmiel; Ron Banner; Daniel Soudry (ICLR, 2025)

FP4 All the Way: Fully Quantized Training of LLMs. - Brian Chmiel; Maxim Fishman; Ron Banner; Daniel Soudry (CoRR, 2025)