10 citations · 18 across the 8 of their papers we have counts for
8 papers
Quantizing With Randomized Hadamard Transforms: Efficient Heuristic Now Proven
Ran Ben-Basat, William Kuszmaul, Michael Mitzenmacher +2
Uniform random rotations (URRs) are a common preprocessing step in modern quantization approaches used for gradient compression, inference acceleration, KV-cache compression, model…
A Note on TurboQuant and the Earlier DRIVE/EDEN Line of Work
Ran Ben-Basat, Yaniv Ben-Itzhak, Gal Mendelson +3
This note clarifies the relationship between the recent TurboQuant work and the earlier DRIVE (NeurIPS 2021) and EDEN (ICML 2022) schemes. DRIVE is a 1-bit quantizer that EDEN exte…
Towards Automatic Hands-on-Keyboard Attack Detection Using LLMs in EDR Solutions
Amit Portnoy, Ehud Azikri, Shay Kels
Endpoint Detection and Remediation (EDR) platforms are essential for identifying and responding to cyber threats. This study presents a novel approach using Large Language Models (…
QUIC-FL: Quick Unbiased Compression for Federated Learning
Ran Ben Basat, Shay Vargaftik, Amit Portnoy +3
Distributed Mean Estimation (DME), in which clients communicate vectors to a parameter server that estimates their average, is a fundamental building block in communication-eff…
SDR: Efficient Neural Re-ranking using Succinct Document Representation
Nachshon Cohen, Amit Portnoy, Besnik Fetahu +1
BERT based ranking models have achieved superior performance on various information retrieval tasks. However, the large number of parameters and complex self-attention operation co…
EDEN: Communication-Efficient and Robust Distributed Mean Estimation for Federated Learning
Shay Vargaftik, Ran Ben Basat, Amit Portnoy +3
Distributed Mean Estimation (DME) is a central building block in federated learning, where clients send local gradients to a parameter server for averaging and updating the model.…
DRIVE: One-bit Distributed Mean Estimation
Shay Vargaftik, Ran Ben Basat, Amit Portnoy +3
We consider the problem where clients transmit -dimensional real-valued vectors using bits each, in a manner that allows the receiver to approximately reconstruc…
Towards Federated Learning With Byzantine-Robust Client Weighting
Amit Portnoy, Yoav Tirosh, Danny Hendler
Federated Learning (FL) is a distributed machine learning paradigm where data is distributed among clients who collaboratively train a model in a computation process coordinated by…