206 citations · 239 across the 40 of their papers we have counts for
9 papers · 1 filter
Private Inference in Quantized Models
Zirui Deng, Vinayak Ramkumar, Rawad Bitar +1
A typical setup in many machine learning scenarios involves a server that holds a model and a user that possesses data, and the challenge is to perform inference while safeguarding…
Gram-Schmidt Methods for Unsupervised Feature Extraction and Selection
Bahram Yaghooti, Netanel Raviv, Bruno Sinopoli
Feature extraction and selection in the presence of nonlinear dependencies among the data is a fundamental challenge in unsupervised learning. We propose using a Gram-Schmidt (GS)…
Error Correction for DNA Storage
Jin Sima, Netanel Raviv, Moshe Schwartz +1
DNA-based storage is an emerging storage technology that provides high information density and long duration. Due to the physical constraints in the reading and writing processes,…
Break-Resilient Codes
Canran Wang, Jin Sima, Netanel Raviv
We investigate the problem of encoding data into an -break-resilient code (-BRC), i.e., a collections of sequences of length~ from which the original data can be…
Robust Indexing for the Sliced Channel: Almost Optimal Codes for Substitutions and Deletions
Jin Sima, Netanel Raviv, Jehoshua Bruck
Encoding data as a set of unordered strings is receiving great attention as it captures one of the basic features of DNA storage systems. However, the challenge of constructing opt…
Transaction Confirmation in Coded Blockchain
Ilan Tennenhouse, Netanel Raviv
As blockchains continue to seek to scale to a larger number of nodes, the communication complexity of protocols has become a significant priority as the network can quickly become…
A Generalized Covering Algorithm for Chained Codes
Ben Langton, Netanel Raviv
The covering radius is a fundamental property of linear codes that characterizes the trade-off between storage and access in linear data-query protocols. The generalized covering r…
Approximate Private Inference in Quantized Models
Zirui Deng, Netanel Raviv
Private inference refers to a two-party setting in which one has a model (e.g., a linear classifier), the other has data, and the model is to be applied over the data while safegua…
Access-Redundancy Tradeoffs in Quantized Linear Computations
Vinayak Ramkumar, Netanel Raviv, Itzhak Tamo
Linear real-valued computations over distributed datasets are common in many applications, most notably as part of machine learning inference. In particular, linear computations th…