3 citations · 10 across the 11 of their papers we have counts for
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cs.SE2026
CONQuER: Hardware-Aware Mixed-Precision Quantisation with Online-Calibrated Surrogates
Aidan Dakhama, Ajitha Rajan
Deploying deep neural networks on resource-constrained hardware relies on mixed-precision quantisation (MPQ). current deployment toolchains severely fragment this process. Quantisa…
cs.SE2023★ 1 cited
FetaFix: Automatic Fault Localization and Repair of Deep Learning Model Conversions
Nikolaos Louloudakis, Perry Gibson, José Cano +1
Converting deep learning models between frameworks is a common step to maximize model compatibility across devices and leverage optimization features that may be exclusively provid…
cs.SE2023
Hawkeye: Change-targeted Testing for Android Apps based on Deep Reinforcement Learning
Chao Peng, Zhengwei Lv, Jiarong Fu +4
Android Apps are frequently updated to keep up with changing user, hardware, and business demands. Ensuring the correctness of App updates through extensive testing is crucial to a…