11 citations · 22 across the 6 of their papers we have counts for
11 papers
SPARROW: Scalable Taxonomy Induction via Structure-Preserving Partitioning and Constraint-Guided Merging
Yirui Zhang, Yixuan Tang, Yandong Sun +2
Taxonomy induction aims to organize concept sets into coherent hierarchical structures. Recent LLM-based methods can induce taxonomies directly from flat term lists, avoiding the n…
Leveraging Old Knowledge to Continually Learn New Classes in Medical Images
Evelyn Chee, Mong Li Lee, Wynne Hsu
Class-incremental continual learning is a core step towards developing artificial intelligence systems that can continuously adapt to changes in the environment by learning new con…
Distributional Shifts in Automated Diabetic Retinopathy Screening
Jay Nandy, Wynne Hsu, Mong Li Lee
Deep learning-based models are developed to automatically detect if a retina image is `referable' in diabetic retinopathy (DR) screening. However, their classification accuracy deg…
Towards Fully Interpretable Deep Neural Networks: Are We There Yet?
Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee
Despite the remarkable performance, Deep Neural Networks (DNNs) behave as black-boxes hindering user trust in Artificial Intelligence (AI) systems. Research on opening black-box DN…
Towards Bridging the gap between Empirical and Certified Robustness against Adversarial Examples
Jay Nandy, Sudipan Saha, Wynne Hsu +2
The current state-of-the-art defense methods against adversarial examples typically focus on improving either empirical or certified robustness. Among them, adversarially trained (…
Comprehensible Convolutional Neural Networks via Guided Concept Learning
Sandareka Wickramanayake, Wynne Hsu, Mong Li Lee
Learning concepts that are consistent with human perception is important for Deep Neural Networks to win end-user trust. Post-hoc interpretation methods lack transparency in the fe…