219 citations · 1.8k across the 99 of their papers we have counts for
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Robust Explanation Constraints for Neural Networks
Matthew Wicker, Juyeon Heo, Luca Costabello +1
Post-hoc explanation methods are used with the intent of providing insights about neural networks and are sometimes said to help engender trust in their outputs. However, popular e…
Towards More Robust Interpretation via Local Gradient Alignment
Sunghwan Joo, Seokhyeon Jeong, Juyeon Heo +2
Neural network interpretation methods, particularly feature attribution methods, are known to be fragile with respect to adversarial input perturbations. To address this, several m…
Human-in-the-Loop Mixup
Katherine M. Collins, Umang Bhatt, Weiyang Liu +4
Aligning model representations to humans has been found to improve robustness and generalization. However, such methods often focus on standard observational data. Synthetic data i…
Iterative Teaching by Data Hallucination
Zeju Qiu, Weiyang Liu, Tim Z. Xiao +5
We consider the problem of iterative machine teaching, where a teacher sequentially provides examples based on the status of a learner under a discrete input space (i.e., a pool of…
Continual Learning by Modeling Intra-Class Variation
Longhui Yu, Tianyang Hu, Lanqing Hong +3
It has been observed that neural networks perform poorly when the data or tasks are presented sequentially. Unlike humans, neural networks suffer greatly from catastrophic forgetti…
Can We Automate the Analysis of Online Child Sexual Exploitation Discourse?
Darren Cook, Miri Zilka, Heidi DeSandre +3
Social media's growing popularity raises concerns around children's online safety. Interactions between minors and adults with predatory intentions is a particularly grave concern.…