13 citations · 26 across the 9 of their papers we have counts for
12 papers
Probabilistic Invariant Learning with Randomized Linear Classifiers
Leonardo Cotta, Gal Yehuda, Assaf Schuster +1
Designing models that are both expressive and preserve known invariances of tasks is an increasingly hard problem. Existing solutions tradeoff invariance for computational or memor…
Unsupervised Frequent Pattern Mining for CEP
Guy Shapira, Assaf Schuster
Complex Event Processing (CEP) is a set of methods that allow efficient knowledge extraction from massive data streams using complex and highly descriptive patterns. Numerous appli…
Coin Flipping Neural Networks
Yuval Sieradzki, Nitzan Hodos, Gal Yehuda +1
We show that neural networks with access to randomness can outperform deterministic networks by using amplification. We call such networks Coin-Flipping Neural Networks, or CFNNs.…
Learning Under Delayed Feedback: Implicitly Adapting to Gradient Delays
Rotem Zamir Aviv, Ido Hakimi, Assaf Schuster +1
We consider stochastic convex optimization problems, where several machines act asynchronously in parallel while sharing a common memory. We propose a robust training method for th…
It's Not What Machines Can Learn, It's What We Cannot Teach
Gal Yehuda, Moshe Gabel, Assaf Schuster
Can deep neural networks learn to solve any task, and in particular problems of high complexity? This question attracts a lot of interest, with recent works tackling computationall…
Adaptive Communication Bounds for Distributed Online Learning
Michael Kamp, Mario Boley, Michael Mock +3
We consider distributed online learning protocols that control the exchange of information between local learners in a round-based learning scenario. The learning performance of su…