Been Kim
Been Kim
Google Brain
Verified email at csail.mit.edu - Homepage
Title
Cited by
Cited by
Year
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
6272017
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
5992017
Smoothgrad: removing noise by adding noise
D Smilkov, N Thorat, B Kim, F Viégas, M Wattenberg
arXiv preprint arXiv:1706.03825, 2017
2582017
Multiple relative pose graphs for robust cooperative mapping
B Kim, M Kaess, L Fletcher, J Leonard, A Bachrach, N Roy, S Teller
Robotics and Automation (ICRA), 2010 IEEE International Conference on, 3185-3192, 2010
1772010
Sanity checks for saliency maps
J Adebayo, J Gilmer, M Muelly, I Goodfellow, M Hardt, B Kim
Advances in Neural Information Processing Systems, 9505-9515, 2018
1602018
Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
RS Been Kim, Martin Wattenberg, Justin Gilmer, Carrie Cai, James Wexler ...
arXiv preprint arXiv:1711.11279, 2018
158*2018
Examples are not enough, learn to criticize! criticism for interpretability
B Kim, R Khanna, OO Koyejo
Advances in Neural Information Processing Systems, 2280-2288, 2016
1552016
Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
B Kim, C Rudin, J Shah
Neural Information Processing Systems (NIPS), 2014
1352014
The (un) reliability of saliency methods
PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ...
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, 267-280, 2019
1132019
Learning how to explain neural networks: PatternNet and PatternAttribution
PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne
arXiv preprint arXiv:1705.05598, 2017
1082017
Learning how to explain neural networks: PatternNet and PatternAttribution
PJ Kindermans, KT Schütt, M Alber, KR Müller, D Erhan, B Kim, S Dähne
arXiv preprint arXiv:1705.05598, 2017
1082017
A Roadmap for a Rigorous Science of Interpretability
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
662017
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
B Kim, DV Finale, J Shah
Neural Information Processing Systems, 2015
662015
How do Humans Understand Explanations from Machine Learning Systems? An Evaluation of the Human-Interpretability of Explanation
M Narayanan, E Chen, J He, B Kim, S Gershman, F Doshi-Velez
arXiv preprint arXiv:1802.00682, 2018
522018
To trust or not to trust a classifier
H Jiang, B Kim, M Guan, M Gupta
Advances in neural information processing systems, 5541-5552, 2018
502018
Interactive and interpretable machine learning models for human machine collaboration
B Kim
Massachusetts Institute of Technology, 2015
492015
Local explanation methods for deep neural networks lack sensitivity to parameter values
J Adebayo, J Gilmer, I Goodfellow, B Kim
arXiv preprint arXiv:1810.03307, 2018
352018
Evaluating Feature Importance Estimates
S Hooker, D Erhan, PJ Kindermans, B Kim
arXiv preprint arXiv:1806.10758, 2018
252018
Human-in-the-loop interpretability prior
I Lage, A Ross, SJ Gershman, B Kim, F Doshi-Velez
Advances in Neural Information Processing Systems, 10159-10168, 2018
252018
Human-centered tools for coping with imperfect algorithms during medical decision-making
CJ Cai, E Reif, N Hegde, J Hipp, B Kim, D Smilkov, M Wattenberg, ...
Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems …, 2019
182019
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