10 citations · 11 across the 4 of their papers we have counts for
7 papers · 1 filter
Bimodal Distributed Binarized Neural Networks
Tal Rozen, Moshe Kimhi, Brian Chmiel +2
Binary Neural Networks (BNNs) are an extremely promising method to reduce deep neural networks' complexity and power consumption massively. Binarization techniques, however, suffer…
Weisfeiler and Leman Go Infinite: Spectral and Combinatorial Pre-Colorings
Or Feldman, Amit Boyarski, Shai Feldman +3
Graph isomorphism testing is usually approached via the comparison of graph invariants. Two popular alternatives that offer a good trade-off between expressive power and computatio…
Colored Noise Injection for Training Adversarially Robust Neural Networks
Evgenii Zheltonozhskii, Chaim Baskin, Yaniv Nemcovsky +3
Even though deep learning has shown unmatched performance on various tasks, neural networks have been shown to be vulnerable to small adversarial perturbations of the input that le…
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…
Efficient non-uniform quantizer for quantized neural network targeting reconfigurable hardware
Natan Liss, Chaim Baskin, Avi Mendelson +2
Convolutional Neural Networks (CNN) has become more popular choice for various tasks such as computer vision, speech recognition and natural language processing. Thanks to their la…