Brian Chmiel

According to our database, Brian Chmiel authored at least 16 papers between 2019 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

Adversarial robustness via noise injection in smoothed models.

Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alex M. Bronstein, Avi Mendelson

Appl. Intell., April, 2023

Minimum Variance Unbiased N: M Sparsity for the Neural Gradients.

Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats.

Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Bimodal Distributed Binarized Neural Networks.

Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin

CoRR, 2022

Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients.

Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry

CoRR, 2022

2021

Loss aware post-training quantization.

Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson

Mach. Learn., 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

Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning.

Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Neural gradients are near-lognormal: improved quantized and sparse training.

Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry

Proceedings of the 9th International Conference on Learning Representations, 2021

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

Colored Noise Injection for Training Adversarially Robust Neural Networks.

Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein

CoRR, 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

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

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

Proceedings of the 2020 International Joint Conference on Neural Networks, 2020

2019

Smoothed Inference for Adversarially-Trained Models.

Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alexander M. Bronstein, Avi Mendelson

CoRR, 2019

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