most citedEREBA: Black-box Energy Testing of Adaptive Neural Networks

10 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.LG2022

DeepPerform: An Efficient Approach for Performance Testing of Resource-Constrained Neural Networks

Simin Chen, Mirazul Haque, Cong Liu +1

Today, an increasing number of Adaptive Deep Neural Networks (AdNNs) are being used on resource-constrained embedded devices. We observe that, similar to traditional software, redu…

cs.SE20228 cited

TestAug: A Framework for Augmenting Capability-based NLP Tests

Guanqun Yang, Mirazul Haque, Qiaochu Song +2

The recently proposed capability-based NLP testing allows model developers to test the functional capabilities of NLP models, revealing functional failures that cannot be detected…

cs.LG2022

CorrGAN: Input Transformation Technique Against Natural Corruptions

Mirazul Haque, Christof J. Budnik, Wei Yang

Because of the increasing accuracy of Deep Neural Networks (DNNs) on different tasks, a lot of real times systems are utilizing DNNs. These DNNs are vulnerable to adversarial pertu…

cs.CV2022

NICGSlowDown: Evaluating the Efficiency Robustness of Neural Image Caption Generation Models

Simin Chen, Zihe Song, Mirazul Haque +2

Neural image caption generation (NICG) models have received massive attention from the research community due to their excellent performance in visual understanding. Existing work…

cs.LG202210 cited

EREBA: Black-box Energy Testing of Adaptive Neural Networks

Mirazul Haque, Yaswanth Yadlapalli, Wei Yang +1

Recently, various Deep Neural Network (DNN) models have been proposed for environments like embedded systems with stringent energy constraints. The fundamental problem of determini…