29 citations · 91 across the 43 of their papers we have counts for
5 papers · 1 filter
On the Optimal Memorization Power of ReLU Neural Networks
Gal Vardi, Gilad Yehudai, Ohad Shamir
We study the memorization power of feedforward ReLU neural networks. We show that such networks can memorize any points that satisfy a mild separability assumption using $\tild…
On Margin Maximization in Linear and ReLU Networks
Gal Vardi, Ohad Shamir, Nathan Srebro
The implicit bias of neural networks has been extensively studied in recent years. Lyu and Li [2019] showed that in homogeneous networks trained with the exponential or the logisti…
Learning a Single Neuron with Bias Using Gradient Descent
Gal Vardi, Gilad Yehudai, Ohad Shamir
We theoretically study the fundamental problem of learning a single neuron with a bias term () in the realizable setting with the…
Size and Depth Separation in Approximating Benign Functions with Neural Networks
Gal Vardi, Daniel Reichman, Toniann Pitassi +1
When studying the expressive power of neural networks, a main challenge is to understand how the size and depth of the network affect its ability to approximate real functions. How…
From Local Pseudorandom Generators to Hardness of Learning
Amit Daniely, Gal Vardi
We prove hardness-of-learning results under a well-studied assumption on the existence of local pseudorandom generators. As we show, this assumption allows us to surpass the curren…