18 citations · 24 across the 5 of their papers we have counts for
7 papers
Computing Anti-Derivatives using Deep Neural Networks
D. Chakraborty, S. Gopalakrishnan
This paper presents a novel algorithm to obtain the closed-form anti-derivative of a function using Deep Neural Network architecture. In the past, mathematicians have developed sev…
Polarizing Front Ends for Robust CNNs
Can Bakiskan, Soorya Gopalakrishnan, Metehan Cekic +2
The vulnerability of deep neural networks to small, adversarially designed perturbations can be attributed to their "excessive linearity." In this paper, we propose a bottom-up str…
Wireless Fingerprinting via Deep Learning: The Impact of Confounding Factors
Metehan Cekic, Soorya Gopalakrishnan, Upamanyu Madhow
Can we distinguish between two wireless transmitters sending exactly the same message, using the same protocol? The opportunity for doing so arises due to subtle nonlinear variatio…
Robust Wireless Fingerprinting via Complex-Valued Neural Networks
Soorya Gopalakrishnan, Metehan Cekic, Upamanyu Madhow
A "wireless fingerprint" which exploits hardware imperfections unique to each device is a potentially powerful tool for wireless security. Such a fingerprint should be able to dist…
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…
Combating Adversarial Attacks Using Sparse Representations
Soorya Gopalakrishnan, Zhinus Marzi, Upamanyu Madhow +1
It is by now well-known that small adversarial perturbations can induce classification errors in deep neural networks (DNNs). In this paper, we make the case that sparse representa…