3 citations · 5 across the 5 of their papers we have counts for
6 papers
On the Robustness of Randomized Ensembles to Adversarial Perturbations
Hassan Dbouk, Naresh R. Shanbhag
Randomized ensemble classifiers (RECs), where one classifier is randomly selected during inference, have emerged as an attractive alternative to traditional ensembling methods for…
Adversarial Vulnerability of Randomized Ensembles
Hassan Dbouk, Naresh R. Shanbhag
Despite the tremendous success of deep neural networks across various tasks, their vulnerability to imperceptible adversarial perturbations has hindered their deployment in the rea…
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks
Hassan Dbouk, Naresh R. Shanbhag
Despite their tremendous successes, convolutional neural networks (CNNs) incur high computational/storage costs and are vulnerable to adversarial perturbations. Recent works on rob…
Fundamental Limits on Energy-Delay-Accuracy of In-memory Architectures in Inference Applications
Sujan Kumar Gonugondla, Charbel Sakr, Hassan Dbouk +1
This paper obtains fundamental limits on the computational precision of in-memory computing architectures (IMCs). An IMC noise model and associated SNR metrics are defined and thei…
The Twelvefold Way of Non-Sequential Lossless Compression
Taha Ameen ur Rahman, Alton S. Barbehenn, Xinan Chen +27
Many information sources are not just sequences of distinguishable symbols but rather have invariances governed by alternative counting paradigms such as permutations, combinations…
DBQ: A Differentiable Branch Quantizer for Lightweight Deep Neural Networks
Hassan Dbouk, Hetul Sanghvi, Mahesh Mehendale +1
Deep neural networks have achieved state-of-the art performance on various computer vision tasks. However, their deployment on resource-constrained devices has been hindered due to…