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Visual correspondence-based explanations improve AI robustness and human-AI team accuracy
Giang Nguyen, Mohammad Reza Taesiri, Anh Nguyen
Explaining artificial intelligence (AI) predictions is increasingly important and even imperative in many high-stakes applications where humans are the ultimate decision-makers. In…
PiC: A Phrase-in-Context Dataset for Phrase Understanding and Semantic Search
Thang M. Pham, Seunghyun Yoon, Trung Bui +1
While contextualized word embeddings have been a de-facto standard, learning contextualized phrase embeddings is less explored and being hindered by the lack of a human-annotated b…
How explainable are adversarially-robust CNNs?
Mehdi Nourelahi, Lars Kotthoff, Peijie Chen +1
Three important criteria of existing convolutional neural networks (CNNs) are (1) test-set accuracy; (2) out-of-distribution accuracy; and (3) explainability. While these criteria…