271 citations · 841 across the 29 of their papers we have counts for
35 papers
Self-supervised Speaker Recognition Training Using Human-Machine Dialogues
Metehan Cekic, Ruirui Li, Zeya Chen +3
Speaker recognition, recognizing speaker identities based on voice alone, enables important downstream applications, such as personalization and authentication. Learning speaker re…
Neuro-Inspired Deep Neural Networks with Sparse, Strong Activations
Metehan Cekic, Can Bakiskan, Upamanyu Madhow
While end-to-end training of Deep Neural Networks (DNNs) yields state of the art performance in an increasing array of applications, it does not provide insight into, or control ov…
Generalized Likelihood Ratio Test for Adversarially Robust Hypothesis Testing
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are known to be susceptible to adversarial attacks which can cause misclassification by introducing small but well designed perturbations. In this paper, we…
All-Digital LoS MIMO with Low-Precision Analog-to-Digital Conversion
Ahmet Dundar Sezer, Upamanyu Madhow
Line-of-sight (LoS) multi-input multi-output (MIMO) systems exhibit attractive scaling properties with increase in carrier frequency: for a fixed form factor and range, the spatial…
A Neuro-Inspired Autoencoding Defense Against Adversarial Perturbations
Can Bakiskan, Metehan Cekic, Ahmet Dundar Sezer +1
Deep Neural Networks (DNNs) are vulnerable to adversarial attacks: carefully constructed perturbations to an image can seriously impair classification accuracy, while being imperce…
Adversarially Robust Classification based on GLRT
Bhagyashree Puranik, Upamanyu Madhow, Ramtin Pedarsani
Machine learning models are vulnerable to adversarial attacks that can often cause misclassification by introducing small but well designed perturbations. In this paper, we explore…