32 citations · 100 across the 47 of their papers we have counts for
5 papers · 1 filter
DeepShare: Sharing ReLU Across Channels and Layers for Efficient Private Inference
Yonathan Bornfeld, Shai Avidan
Private Inference (PI) uses cryptographic primitives to perform privacy preserving machine learning. In this setting, the owner of the network runs inference on the data of the cli…
Coordinate Descent for Network Linearization
Vlad Rakhlin, Amir Jevnisek, Shai Avidan
ReLU activations are the main bottleneck in Private Inference that is based on ResNet networks. This is because they incur significant inference latency. Reducing ReLU count is a d…
kNet: A Deep kNN Network To Handle Label Noise
Itzik Mizrahi, Shai Avidan
Deep Neural Networks require large amounts of labeled data for their training. Collecting this data at scale inevitably causes label noise.Hence,the need to develop learning algori…
Proximity Preserving Binary Code using Signed Graph-Cut
Inbal Lav, Shai Avidan, Yoram Singer +1
We introduce a binary embedding framework, called Proximity Preserving Code (PPC), which learns similarity and dissimilarity between data points to create a compact and affinity-pr…
The Resistance to Label Noise in K-NN and DNN Depends on its Concentration
Amnon Drory, Oria Ratzon, Shai Avidan +1
We investigate the classification performance of K-nearest neighbors (K-NN) and deep neural networks (DNNs) in the presence of label noise. We first show empirically that a DNN's p…