4 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2021
Sparse Coding Frontend for Robust Neural Networks
Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer +1
Deep Neural Networks are known to be vulnerable to small, adversarially crafted, perturbations. The current most effective defense methods against these adversarial attacks are var…
eess.SP2019
Interference management and capacity analysis for mm-wave picocells in urban canyons
Zhinus Marzi, Upamanyu Madhow
Millimeter (mm) wave picocellular networks are a promising approach for delivering the 1000-fold capacity increase required to keep up with projected demand for wireless data: the…
stat.ML2018★ 4 cited
Robust Adversarial Learning via Sparsifying Front Ends
Soorya Gopalakrishnan, Zhinus Marzi, Metehan Cekic +2
It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks. In this paper, we take a bottom-up signal processing perspect…