72 citations · 101 across the 4 of their papers we have counts for
4 papers
Computational Separation Between Convolutional and Fully-Connected Networks
Eran Malach, Shai Shalev-Shwartz
Convolutional neural networks (CNN) exhibit unmatched performance in a multitude of computer vision tasks. However, the advantage of using convolutional networks over fully-connect…
Proving the Lottery Ticket Hypothesis: Pruning is All You Need
Eran Malach, Gilad Yehudai, Shai Shalev-Shwartz +1
The lottery ticket hypothesis (Frankle and Carbin, 2018), states that a randomly-initialized network contains a small subnetwork such that, when trained in isolation, can compete w…
Learning Boolean Circuits with Neural Networks
Eran Malach, Shai Shalev-Shwartz
While on some natural distributions, neural-networks are trained efficiently using gradient-based algorithms, it is known that learning them is computationally hard in the worst-ca…
Is Deeper Better only when Shallow is Good?
Eran Malach, Shai Shalev-Shwartz
Understanding the power of depth in feed-forward neural networks is an ongoing challenge in the field of deep learning theory. While current works account for the importance of dep…