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
11142017
Towards a rigorous science of interpretable machine learning
F Doshi-Velez, B Kim
10752017
Smoothgrad: removing noise by adding noise
D Smilkov, N Thorat, B Kim, F Viégas, M Wattenberg
arXiv preprint arXiv:1706.03825, 2017
5012017
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
4022018
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
331*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
2692016
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
1932010
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
1842014
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
1772017
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
1772017
The (un) reliability of saliency methods
PJ Kindermans, S Hooker, J Adebayo, M Alber, KT Schütt, S Dähne, ...
arXiv preprint arXiv:1711.00867, 2017
1482017
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
1282018
A Roadmap for a Rigorous Science of Interpretability
F Doshi-Velez, B Kim
arXiv preprint arXiv:1702.08608, 2017
952017
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
942018
Mind the Gap: A Generative Approach to Interpretable Feature Selection and Extraction
B Kim, DV Finale, J Shah
Neural Information Processing Systems, 2015
782015
A benchmark for interpretability methods in deep neural networks
S Hooker, D Erhan, PJ Kindermans, B Kim
Advances in Neural Information Processing Systems, 9737-9748, 2019
772019
Interactive and interpretable machine learning models for human machine collaboration
B Kim
Massachusetts Institute of Technology, 2015
742015
The (Un) reliability of Saliency Methods
M Alber, KT Schütt, S Dähne, D Erhan, B Kim
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning 11700 …, 2019
63*2019
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
632019
Towards automatic concept-based explanations
A Ghorbani, J Wexler, JY Zou, B Kim
Advances in Neural Information Processing Systems, 9277-9286, 2019
62*2019
The system can't perform the operation now. Try again later.
Articles 1–20