10 citations · 11 across the 4 of their papers we have counts for
4 papers · 1 filter
Smoothed Inference for Adversarially-Trained Models
Yaniv Nemcovsky, Evgenii Zheltonozhskii, Chaim Baskin +4
Deep neural networks are known to be vulnerable to adversarial attacks. Current methods of defense from such attacks are based on either implicit or explicit regularization, e.g.,…
Loss Aware Post-training Quantization
Yury Nahshan, Brian Chmiel, Chaim Baskin +4
Neural network quantization enables the deployment of large models on resource-constrained devices. Current post-training quantization methods fall short in terms of accuracy for I…
CAT: Compression-Aware Training for bandwidth reduction
Chaim Baskin, Brian Chmiel, Evgenii Zheltonozhskii +3
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving visual processing tasks. One of the major obstacles hindering the ubiquitous u…
Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks
Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii +4
Recently, deep learning has become a de facto standard in machine learning with convolutional neural networks (CNNs) demonstrating spectacular success on a wide variety of tasks. H…