Brian Chmiel - RankMe

25 papers A* 6B 1Journal 18

Year	Rank	Type	Title / Venue / Authors
2025	J	`jnl`	FP4 All the Way: Fully Quantized Training of LLMs. CoRR Brian Chmiel, Maxim Fishman, Ron Banner, Daniel Soudry
2025	A*	`conf`	Scaling FP8 training to trillion-token LLMs. ICLR Maxim Fishman, Brian Chmiel, Ron Banner, Daniel Soudry
2024	J	`jnl`	EXAQ: Exponent Aware Quantization For LLMs Acceleration. CoRR Moran Shkolnik, Maxim Fishman, Brian Chmiel, Hilla Ben-Yaacov, Ron Banner, Kfir Yehuda Levy
2024	J	`jnl`	Scaling FP8 training to trillion-token LLMs. CoRR Maxim Fishman, Brian Chmiel, Ron Banner, Daniel Soudry
2023	A*	`conf`	Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. ICLR Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry
2023	J	`jnl`	Adversarial robustness via noise injection in smoothed models. Appl. Intell. Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alex M. Bronstein, Avi Mendelson
2023	A*	`conf`	Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. ICLR Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry
2022	J	`jnl`	Bimodal Distributed Binarized Neural Networks. CoRR Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin
2022	J	`jnl`	Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients. CoRR Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry
2021	A*	`conf`	Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. NeurIPS Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Joseph Naor, Daniel Soudry
2021	J	`jnl`	Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N: M Transposable Masks. CoRR Itay Hubara, Brian Chmiel, Moshe Island, Ron Banner, Seffi Naor, Daniel Soudry
2021	J	`jnl`	CAT: Compression-Aware Training for bandwidth reduction. J. Mach. Learn. Res. Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson
2021	J	`jnl`	Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. CoRR Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry
2021	J	`jnl`	Loss aware post-training quantization. Mach. Learn. Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson
2021	A*	`conf`	Neural gradients are near-lognormal: improved quantized and sparse training. ICLR Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry
2020	J	`jnl`	Colored Noise Injection for Training Adversarially Robust Neural Networks. CoRR Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein
2020	B	`conf`	Feature Map Transform Coding for Energy-Efficient CNN Inference. IJCNN Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Yevgeny Yermolin, Alex Karbachevsky, Alex M. Bronstein, Avi Mendelson
2020	J	`jnl`	Neural gradients are lognormally distributed: understanding sparse and quantized training. CoRR Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry
2020	A*	`conf`	Robust Quantization: One Model to Rule Them All. NeurIPS Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alex M. Bronstein, Uri C. Weiser
2020	J	`jnl`	Robust Quantization: One Model to Rule Them All. CoRR Moran Shkolnik, Brian Chmiel, Ron Banner, Gil Shomron, Yury Nahshan, Alexander M. Bronstein, Uri C. Weiser
2019	J	`jnl`	CAT: Compression-Aware Training for bandwidth reduction. CoRR Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson
2019	J	`jnl`	Feature Map Transform Coding for Energy-Efficient CNN Inference. CoRR Brian Chmiel, Chaim Baskin, Ron Banner, Evgenii Zheltonozhskii, Yevgeny Yermolin, Alex Karbachevsky, Alexander M. Bronstein, Avi Mendelson
2019	J	`jnl`	Loss Aware Post-training Quantization. CoRR Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson
2019	J	`jnl`	Smoothed Inference for Adversarially-Trained Models. CoRR Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alexander M. Bronstein, Avi Mendelson
2019	J	`jnl`	Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks. CoRR Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Brian Chmiel, Itay Evron, Alexander M. Bronstein, Avi Mendelson