no code implementations • 22 Aug 2023 • Rotem Elimelech, Ofir David, Carlos De la Cruz Mengual, Rotem Kalisch, Wolfgang Berndt, Michael Shalyt, Mark Silberstein, Yaron Hadad, Ido Kaminer
In recent decades, a growing number of discoveries in fields of mathematics have been assisted by computer algorithms, primarily for exploring large parameter spaces that humans would take too long to investigate.
1 code implementation • USENIX Annual Technical Conference 2021 • Saar Eliad, Ido Hakimi, Alon De Jager, Mark Silberstein, Assaf Schuster
Fine-tuning is an increasingly common technique that leverages transfer learning to dramatically expedite the training of huge, high-quality models.
1 code implementation • 10 Feb 2020 • Alon Rashelbach, Ori Rottenstreich, Mark Silberstein
To achieve high throughput and low latency, state-of-the-art algorithms strive to fit the rule lookup data structures into on-die caches; however, they do not scale well with the number of rules.
1 code implementation • 24 May 2019 • Oleksii Oleksenko, Bohdan Trach, Mark Silberstein, Christof Fetzer
SpecFuzz is the first tool that enables dynamic testing for speculative execution vulnerabilities (e. g., Spectre).
Cryptography and Security
1 code implementation • NeurIPS 2021 • Menachem Adelman, Kfir Y. Levy, Ido Hakimi, Mark Silberstein
We propose a novel technique for faster deep neural network training which systematically applies sample-based approximation to the constituent tensor operations, i. e., matrix multiplications and convolutions.