Chaim Baskin
Chaim Baskin
Postdoctoral Fellow at Computer Science department,Technion
Verified email at
Cited by
Cited by
Uniq: Uniform noise injection for non-uniform quantization of neural networks
C Baskin, N Liss, E Schwartz, E Zheltonozhskii, R Giryes, AM Bronstein, ...
ACM Transactions on Computer Systems (TOCS) 37 (1--4), 1-15, 2021
Nice: Noise injection and clamping estimation for neural network quantization
C Baskin, E Zheltonozhkii, T Rozen, N Liss, Y Chai, E Schwartz, R Giryes, ...
Mathematics 9 (17), 2144, 2021
Streaming architecture for large-scale quantized neural networks on an FPGA-based dataflow platform
C Baskin, N Liss, E Zheltonozhskii, AM Bronstein, A Mendelson
2018 IEEE International Parallel and Distributed Processing Symposium …, 2018
Loss aware post-training quantization
Y Nahshan, B Chmiel, C Baskin, E Zheltonozhskii, R Banner, ...
Machine Learning, 1-18, 2021
Early-stage neural network hardware performance analysis
A Karbachevsky, C Baskin, E Zheltonozhskii, Y Yermolin, F Gabbay, ...
Sustainability 13 (2), 717, 2021
Beholder-GAN: Generation and beautification of facial images with conditioning on their beauty level
N Diamant, D Zadok, C Baskin, E Schwartz, AM Bronstein
2019 IEEE International Conference on Image Processing (ICIP), 739-743, 2019
Feature map transform coding for energy-efficient cnn inference
B Chmiel, C Baskin, E Zheltonozhskii, R Banner, Y Yermolin, ...
2020 International Joint Conference on Neural Networks (IJCNN), 1-9, 2020
CAT: Compression-Aware Training for bandwidth reduction
C Baskin, B Chmiel, E Zheltonozhskii, R Banner, AM Bronstein, ...
arXiv preprint arXiv:1909.11481, 2019
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
N Liss, C Baskin, A Mendelson, AM Bronstein, R Giryes
arXiv preprint arXiv:1811.10869, 2018
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
Y Zur, C Baskin, E Zheltonozhskii, B Chmiel, I Evron, AM Bronstein
ICML 2019 AutoML Workshop, 2019
Efficient Horizon Line Detection Using an Energy Function
E Gershikov, C Baskin
RACS '17 Proceedings of the International Conference on Research in Adaptive …, 2017
Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
E Zheltonozhskii, C Baskin, A Mendelson, AM Bronstein, O Litany
arXiv preprint arXiv:2103.13646, 2021
Single-Node Attack for Fooling Graph Neural Networks
B Finkelshtein, C Baskin, E Zheltonozhskii, U Alon
arXiv preprint arXiv:2011.03574, 2020
Self-Supervised Learning for Large-Scale Unsupervised Image Clustering
E Zheltonozhskii, C Baskin, AM Bronstein, A Mendelson
NeurIPS 2020 Workshop: Self-Supervised Learning - Theory and Practice, 2020
Colored Noise Injection for Training Adversarially Robust Neural Networks
E Zheltonozhskii, C Baskin, Y Nemcovsky, B Chmiel, A Mendelson, ...
arXiv preprint arXiv:2003.02188, 2020
Smoothed Inference for Adversarially-Trained Models
Y Nemcovsky, E Zheltonozhskii, C Baskin, B Chmiel, M Fishman, ...
arXiv preprint arXiv:1911.07198, 2019
System and method for emulating quantization noise for a neural network
C Baskin, E Schwartz, E Zheltonozhskii, A Bronstein, L Natan, ...
US Patent App. 17/049,651, 2021
Weakly Supervised Recovery of Semantic Attributes
A Ali, T Galanti, E Zheltonozhskiy, C Baskin, L Wolf
arXiv preprint arXiv:2103.11888, 2021
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