XAITK: The explainable AI toolkit (English)
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- New search for: Hu, Brian
- New search for: Tunison, Paul
- New search for: Vasu, Bhavan
- New search for: Menon, Nitesh
- New search for: Collins, Roddy
- New search for: Hoogs, Anthony
- New search for: Hu, Brian
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- New search for: Vasu, Bhavan
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In:
Applied AI Letters
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2
, 4
;
2021
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ISSN:
- Article (Journal) / Electronic Resource
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Title:XAITK: The explainable AI toolkit
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Contributors:Hu, Brian ( author ) / Tunison, Paul ( author ) / Vasu, Bhavan ( author ) / Menon, Nitesh ( author ) / Collins, Roddy ( author ) / Hoogs, Anthony ( author )
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Published in:Applied AI Letters ; 2, 4
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Publisher:
- New search for: Blackwell Publishing Ltd
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Publication date:2021-12-01
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Size:10 pages
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ISSN:
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DOI:
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Type of media:Article (Journal)
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Type of material:Electronic Resource
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Language:English
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Keywords:
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Source:
Table of contents – Volume 2, Issue 4
The tables of contents are generated automatically and are based on the data records of the individual contributions available in the index of the TIB portal. The display of the Tables of Contents may therefore be incomplete.
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Patching interpretable And‐Or‐Graph knowledge representation using augmented realityLiu, Hangxin / Zhu, Yixin / Zhu, Song‐Chun et al. | 2021
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User‐guided global explanations for deep image recognition: A user studyHamidi‐Haines, Mandana / Qi, Zhongang / Fern, Alan / Li, Fuxin / Tadepalli, Prasad et al. | 2021
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How level of explanation detail affects human performance in interpretable intelligent systems: A study on explainable fact checkingLinder, Rhema / Mohseni, Sina / Yang, Fan / Pentyala, Shiva K. / Ragan, Eric D. / Hu, Xia Ben et al. | 2021
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Explaining autonomous drones: An XAI journeyStefik, Mark / Youngblood, Michael / Pirolli, Peter / Lebiere, Christian / Thomson, Robert / Price, Robert / Nelson, Lester D. / Krivacic, Robert / Le, Jacob / Mitsopoulos, Konstantinos et al. | 2021
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Issue Information| 2021
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Explainable, interactive content‐based image retrievalVasu, Bhavan / Hu, Brian / Dong, Bo / Collins, Roddy / Hoogs, Anthony et al. | 2021
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Methods and standards for research on explainable artificial intelligence: Lessons from intelligent tutoring systemsClancey, William J. / Hoffman, Robert R. et al. | 2021
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XAITK: The explainable AI toolkitHu, Brian / Tunison, Paul / Vasu, Bhavan / Menon, Nitesh / Collins, Roddy / Hoogs, Anthony et al. | 2021
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From heatmaps to structured explanations of image classifiersFuxin, Li / Qi, Zhongang / Khorram, Saeed / Shitole, Vivswan / Tadepalli, Prasad / Kahng, Minsuk / Fern, Alan et al. | 2021
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Neural response time analysis: Explainable artificial intelligence using only a stopwatchTaylor, J. Eric T. / Shekhar, Shashank / Taylor, Graham W. et al. | 2021
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Improving users' mental model with attention‐directed counterfactual editsAlipour, Kamran / Ray, Arijit / Lin, Xiao / Cogswell, Michael / Schulze, Jurgen P. / Yao, Yi / Burachas, Giedrius T. et al. | 2021
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Towards structured NLP interpretation via graph explainersYuan, Hao / Yang, Fan / Du, Mengnan / Ji, Shuiwang / Hu, Xia et al. | 2021
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Explainable activity recognition in videos: Lessons learnedRoy, Chiradeep / Nourani, Mahsan / Honeycutt, Donald R. / Block, Jeremy E. / Rahman, Tahrima / Ragan, Eric D. / Ruozzi, Nicholas / Gogate, Vibhav et al. | 2021
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From “no clear winner” to an effective Explainable Artificial Intelligence process: An empirical journeyDodge, Jonathan / Anderson, Andrew / Khanna, Roli / Irvine, Jed / Dikkala, Rupika / Lam, Kin‐Ho / Tabatabai, Delyar / Ruangrotsakun, Anita / Shureih, Zeyad / Kahng, Minsuk et al. | 2021
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Abstraction, validation, and generalization for explainable artificial intelligenceYang, Scott Cheng‐Hsin / Folke, Tomas / Shafto, Patrick et al. | 2021
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Explainable neural computation via stack neural module networksHu, Ronghang / Andreas, Jacob / Darrell, Trevor / Saenko, Kate et al. | 2021
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Generating and evaluating explanations of attended and error‐inducing input regions for VQA modelsRay, Arijit / Cogswell, Michael / Lin, Xiao / Alipour, Kamran / Divakaran, Ajay / Yao, Yi / Burachas, Giedrius et al. | 2021
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Objective criteria for explanations of machine learning modelsYeh, Chih‐Kuan / Ravikumar, Pradeep et al. | 2021
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Reframing explanation as an interactive medium: The EQUAS (Explainable QUestion Answering System) projectFerguson, William / Batra, Dhruv / Mooney, Raymond / Parikh, Devi / Torralba, Antonio / Bau, David / Diller, David / Fasching, Josh / Fiotto‐Kaufman, Jaden / Goyal, Yash et al. | 2021
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Remembering for the right reasons: Explanations reduce catastrophic forgettingEbrahimi, Sayna / Petryk, Suzanne / Gokul, Akash / Gan, William / Gonzalez, Joseph E. / Rohrbach, Marcus / Darrell, Trevor et al. | 2021
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Generating visual explanations with natural languageHendricks, Lisa Anne / Rohrbach, Anna / Schiele, Bernt / Darrell, Trevor / Akata, Zeynep et al. | 2021
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DARPA's explainable AI (XAI) program: A retrospectiveGunning, David / Vorm, Eric / Wang, Jennifer Yunyan / Turek, Matt et al. | 2021
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Toward explainable and advisable model for self‐driving carsKim, Jinkyu / Rohrbach, Anna / Akata, Zeynep / Moon, Suhong / Misu, Teruhisa / Chen, Yi‐Ting / Darrell, Trevor / Canny, John et al. | 2021
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Measuring and characterizing generalization in deep reinforcement learningWitty, Sam / Lee, Jun K. / Tosch, Emma / Atrey, Akanksha / Clary, Kaleigh / Littman, Michael L. / Jensen, David et al. | 2021
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Explaining robot policiesWatkins, Olivia / Huang, Sandy / Frost, Julius / Bhatia, Kush / Weiner, Eric / Abbeel, Pieter / Darrell, Trevor / Plummer, Bryan / Saenko, Kate / Dragan, Anca et al. | 2021