Peter Hase - Concepedia

Author

Peter Hase

University of North Carolina at Chapel Hill

Also Known As

Hase, Peter, Jia, Jinghan, Peter Hase, Zhu, Liuyi

Publications

651

Citations

H-Index

Concepts

All Affiliations

University of North Carolina at Chapel Hill University of North Carolina Health Care Duke University

About

Peter Hase is an author at University of North Carolina at Chapel Hill specializing in engineering, natural language processing, and machine learning.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	8	8	276
2	Language Studies	5	5	127
3	Data Science	5	5	132
4	Computer Science	4	4	164
5	Cognitive Science	3	3	55

Peter Hase

Publications

567

Citations

H-Index

Page 1

	Year	Citations
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase, Mohit Bansal	2020	207
Interpretable Image Recognition with Hierarchical Prototypes Peter Hase, Chaofan Chen, Oscar Li, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing Image ClassificationMachine VisionImage AnalysisMachine LearningInterpretable Image Recognition	2019	101
Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language? Peter Hase, Shiyue Zhang, Harry Xie, Artificial IntelligenceEngineeringSemanticsLarge Language ModelModel Simulatability Perspective	2020	55
FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debugging Han Guo, Nazneen Fatema Rajani, Peter Hase, Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing	2021	47
Interpretable Image Recognition with Hierarchical Prototypes Peter Hase, Chaofan Chen, Oscar Li, arXiv (Cornell University) Image ClassificationMachine VisionImage AnalysisMachine LearningInterpretable Image Recognition	2019	25
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data Peter Hase, Mohit Bansal	2022	24
Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior? Peter Hase, Mohit Bansal arXiv (Cornell University) Artificial IntelligenceEngineeringMachine LearningMachine Learning ToolAi Safety	2020	23
Does Localization Inform Editing? Surprising Differences in Causality-Based Localization vs. Knowledge Editing in Language Models Peter Hase, Mohit Bansal, Been Kim, arXiv (Cornell University) Llm Fine-tuningEngineeringMachine LearningMultilingualismCausality-based Localization Vs	2023	22
Methods for Measuring, Updating, and Visualizing Factual Beliefs in Language Models Peter Hase, Mona Diab, Xian Li, EngineeringComputational SemanticsMona DiabSemanticsLanguage Processing	2023	18
When Can Models Learn From Explanations? A Formal Framework for Understanding the Roles of Explanation Data Peter Hase, Mohit Bansal arXiv (Cornell University) Artificial IntelligenceStructured PredictionLlm Fine-tuningEngineeringMachine Learning	2021	17

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