177 citations · 439 across the 15 of their papers we have counts for
7 papers · 1 filter
Using voice note-taking to promote learners' conceptual understanding
Anam Ahmad Khan, Sadia Nawaz, Joshua Newn +3
Though recent technological advances have enabled note-taking through different modalities (e.g., keyboard, digital ink, voice), there is still a lack of understanding of the effec…
Divide and Learn: A Divide and Conquer Approach for Predict+Optimize
Ali Ugur Guler, Emir Demirovic, Jeffrey Chan +3
The predict+optimize problem combines machine learning ofproblem coefficients with a combinatorial optimization prob-lem that uses the predicted coefficients. While this problemcan…
Reflection Backdoor: A Natural Backdoor Attack on Deep Neural Networks
Yunfei Liu, Xingjun Ma, James Bailey +1
Recent studies have shown that DNNs can be compromised by backdoor attacks crafted at training time. A backdoor attack installs a backdoor into the victim model by injecting a back…
Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma, Hanxun Huang, Yisen Wang +3
Robust loss functions are essential for training accurate deep neural networks (DNNs) in the presence of noisy (incorrect) labels. It has been shown that the commonly used Cross En…
Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles
Ranjie Duan, Xingjun Ma, Yisen Wang +3
Deep neural networks (DNNs) are known to be vulnerable to adversarial examples. Existing works have mostly focused on either digital adversarial examples created via small and impe…
Clean-Label Backdoor Attacks on Video Recognition Models
Shihao Zhao, Xingjun Ma, Xiang Zheng +3
Deep neural networks (DNNs) are vulnerable to backdoor attacks which can hide backdoor triggers in DNNs by poisoning training data. A backdoored model behaves normally on clean tes…