419 citations · 3.1k across the 71 of their papers we have counts for
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SGD Learns Over-parameterized Networks that Provably Generalize on Linearly Separable Data
Alon Brutzkus, Amir Globerson, Eran Malach +1
Neural networks exhibit good generalization behavior in the over-parameterized regime, where the number of network parameters exceeds the number of observations. Nonetheless, curre…
On a Formal Model of Safe and Scalable Self-driving Cars
Shai Shalev-Shwartz, Shaked Shammah, Amnon Shashua
In recent years, car makers and tech companies have been racing towards self driving cars. It seems that the main parameter in this race is who will have the first car on the road.…
Weight Sharing is Crucial to Succesful Optimization
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
Exploiting the great expressive power of Deep Neural Network architectures, relies on the ability to train them. While current theoretical work provides, mostly, results showing th…
Decoupling "when to update" from "how to update"
Eran Malach, Shai Shalev-Shwartz
Deep learning requires data. A useful approach to obtain data is to be creative and mine data from various sources, that were created for different purposes. Unfortunately, this ap…
Failures of Gradient-Based Deep Learning
Shai Shalev-Shwartz, Ohad Shamir, Shaked Shammah
In recent years, Deep Learning has become the go-to solution for a broad range of applications, often outperforming state-of-the-art. However, it is important, for both theoreticia…
Fast Rates for Empirical Risk Minimization of Strict Saddle Problems
Alon Gonen, Shai Shalev-Shwartz
We derive bounds on the sample complexity of empirical risk minimization (ERM) in the context of minimizing non-convex risks that admit the strict saddle property. Recent progress…