143 citations · 145 across the 3 of their papers we have counts for
3 papers
cs.LG2022★ 2 cited
Locally Asynchronous Stochastic Gradient Descent for Decentralised Deep Learning
Tomer Avidor, Nadav Tal Israel
Distributed training algorithms of deep neural networks show impressive convergence speedup properties on very large problems. However, they inherently suffer from communication re…
cs.LG2021
An Exploration into why Output Regularization Mitigates Label Noise
Neta Shoham, Tomer Avidor, Nadav Israel
Label noise presents a real challenge for supervised learning algorithms. Consequently, mitigating label noise has attracted immense research in recent years. Noise robust losses i…
cs.LG2019★ 143 cited
Overcoming Forgetting in Federated Learning on Non-IID Data
Neta Shoham, Tomer Avidor, Aviv Keren +4
We tackle the problem of Federated Learning in the non i.i.d. case, in which local models drift apart, inhibiting learning. Building on an analogy with Lifelong Learning, we adapt…