17 citations · 72 across the 14 of their papers we have counts for
5 papers · 1 filter
Most ReLU Networks Suffer from Adversarial Perturbations
Amit Daniely, Hadas Schacham
We consider ReLU networks with random weights, in which the dimension decreases at each layer. We show that for most such networks, most examples admit an adversarial perturbat…
Hardness of Learning Neural Networks with Natural Weights
Amit Daniely, Gal Vardi
Neural networks are nowadays highly successful despite strong hardness results. The existing hardness results focus on the network architecture, and assume that the network's weigh…
Memorizing Gaussians with no over-parameterizaion via gradient decent on neural networks
Amit Daniely
We prove that a single step of gradient decent over depth two network, with hidden neurons, starting from orthogonal initialization, can memorize $Ω\left(\frac{dq}{\log^4(d)}\r…
On the Complexity of Minimizing Convex Finite Sums Without Using the Indices of the Individual Functions
Yossi Arjevani, Amit Daniely, Stefanie Jegelka +1
Recent advances in randomized incremental methods for minimizing -smooth -strongly convex finite sums have culminated in tight complexity of $\tilde{O}((n+\sqrt{n L/μ})\log(1…
Learning Parities with Neural Networks
Amit Daniely, Eran Malach
In recent years we see a rapidly growing line of research which shows learnability of various models via common neural network algorithms. Yet, besides a very few outliers, these r…