Brian Chmiel

Publication Activity (10 Years)

Years Active: 2019-2023
Publications (10 Years): 21

Top Topics

Minimum Variance

Avoid Overfitting

Training Process

Top Venues

Publications

Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry
Accurate Neural Training with 4-bit Matrix Multiplications at Standard Formats. ICLR (2023)
Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry
Minimum Variance Unbiased N: M Sparsity for the Neural Gradients. ICLR (2023)
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) (2023)
Brian Chmiel, Itay Hubara, Ron Banner, Daniel Soudry
Optimal Fine-Grained N: M sparsity for Activations and Neural Gradients. CoRR (2022)
Tal Rozen, Moshe Kimhi, Brian Chmiel, Avi Mendelson, Chaim Baskin
Bimodal Distributed Binarized Neural Networks. CoRR (2022)
Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alex M. Bronstein, Avi Mendelson
Loss aware post-training quantization. Mach. Learn. 110 (11) (2021)
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)
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 (2021)
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 (2021)
Brian Chmiel, Ron Banner, Elad Hoffer, Hilla Ben-Yaacov, Daniel Soudry
Logarithmic Unbiased Quantization: Practical 4-bit Training in Deep Learning. CoRR (2021)
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)
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)
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 (2020)
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)
Brian Chmiel, Liad Ben-Uri, Moran Shkolnik, Elad Hoffer, Ron Banner, Daniel Soudry
Neural gradients are lognormally distributed: understanding sparse and quantized training. CoRR (2020)
Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky, Brian Chmiel, Avi Mendelson, Alex M. Bronstein
Colored Noise Injection for Training Adversarially Robust Neural Networks. CoRR (2020)
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 (2019)
Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson
CAT: Compression-Aware Training for bandwidth reduction. CoRR (2019)
Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin, Brian Chmiel, Alexander M. Bronstein, Avi Mendelson
Smoothed Inference for Adversarially-Trained Models. CoRR (2019)
Yury Nahshan, Brian Chmiel, Chaim Baskin, Evgenii Zheltonozhskii, Ron Banner, Alexander M. Bronstein, Avi Mendelson
Loss Aware Post-training Quantization. CoRR (2019)
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 (2019)