128 citations · 130 across the 5 of their papers we have counts for
5 papers · 1 filter
Towards Certifying L-infinity Robustness using Neural Networks with L-inf-dist Neurons
Bohang Zhang, Tianle Cai, Zhou Lu +2
It is well-known that standard neural networks, even with a high classification accuracy, are vulnerable to small -norm bounded adversarial perturbations. Although man…
A Note on the Representation Power of GHHs
Zhou Lu
In this note we prove a sharp lower bound on the necessary number of nestings of nested absolute-value functions of generalized hinging hyperplanes (GHH) to represent arbitrary CPW…
A Tight Lower Bound for Uniformly Stable Algorithms
Qinghua Liu, Zhou Lu
Leveraging algorithmic stability to derive sharp generalization bounds is a classic and powerful approach in learning theory. Since Vapnik and Chervonenkis [1974] first formalized…
Boosting for Control of Dynamical Systems
Naman Agarwal, Nataly Brukhim, Elad Hazan +1
We study the question of how to aggregate controllers for dynamical systems in order to improve their performance. To this end, we propose a framework of boosting for online contro…
The Expressive Power of Neural Networks: A View from the Width
Zhou Lu, Hongming Pu, Feicheng Wang +2
The expressive power of neural networks is important for understanding deep learning. Most existing works consider this problem from the view of the depth of a network. In this pap…