no code implementations • 5 Mar 2024 • Netanel Raviv
One of the long-standing challenges in HDC is factoring a compositional representation to its constituent factors, also known as the recovery problem.
no code implementations • 15 Nov 2023 • Bahram Yaghooti, Netanel Raviv, Bruno Sinopoli
Specifically, by applying the GS process over a family of functions which presumably captures the nonlinear dependencies in the data, we construct a series of covariance matrices that can either be used to identify new large-variance directions, or to remove those dependencies from the principal components.
no code implementations • 8 Jun 2021 • Netanel Raviv, Aidan Kelley, Michael Guo, Yevgeny Vorobeychik
The choice of which neurons to stabilize in a neural network is then a combinatorial optimization problem, and we propose several methods for approximately solving it.
no code implementations • 22 Apr 2020 • Netanel Raviv, Siddharth Jain, Pulakesh Upadhyaya, Jehoshua Bruck, Anxiao Jiang
By our approach, either the data or internal layers of the DNN are coded with error correcting codes, and successful computation under noise is guaranteed.
no code implementations • 9 Jan 2020 • Netanel Raviv, Siddharth Jain, Jehoshua Bruck
Data is one of the most important assets of the information age, and its societal impact is undisputed.
no code implementations • 4 Jun 2018 • Qian Yu, Songze Li, Netanel Raviv, Seyed Mohammadreza Mousavi Kalan, Mahdi Soltanolkotabi, Salman Avestimehr
We consider a scenario involving computations over a massive dataset stored distributedly across multiple workers, which is at the core of distributed learning algorithms.
no code implementations • ICML 2018 • Netanel Raviv, Itzhak Tamo, Rashish Tandon, Alexandros G. Dimakis
Gradient coding is a technique for straggler mitigation in distributed learning.