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David Bau
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Artificial intelligence should create transparency in a complex world. As our sophisticated systems exhibit more unexpected and emergent behavior, I believe the focus of machine learning should shift to machine explanation: the development of methods that reveal the patterns and strategies our algorithms find, so that programmers and users can retain agency.
Papers39 papers
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CVPR, pp.14274-14283, (2020)
Proceedings of the National Academy of Sciences of the United States of America, no. 48 (2020): 30071-30078
european conference on computer vision, pp.351-369, (2020)
CVPR, pp.2029-2038, (2019)
ICLR Workshop, no. 3 (2019): 4
Explainable AI: Interpreting, Explaining and Visualizing Deep Learning, pp.243-252, (2019)
2985060393, pp.4502-4511, (2019)
Cited by28Bibtex
David Bau, Jun-Yan Zhu, Hendrik Strobelt, Bolei Zhou, Joshua B Tenenbaum, William T Freeman, Antonio Torralba
arXiv preprint arXiv:1901.09887, (2019)
David Bau, Jun-Yan Zhu,Hendrik Strobelt,Bolei Zhou,Joshua B. Tenenbaum,William T. Freeman, Antonio Torralba
DGS@ICLR, (2019)
ACM Transactions on Graphics (TOG), no. 4 (2019): 59-11
International Journal of Child-Computer Interaction, (2019): 1-8
EIBibtex
Journal of Vision, no. 10 (2018): 1244-1244
Cited by1Bibtex
DSAA, pp.80-89, (2018)
ECCV, pp.122-138, (2018)
arXiv: Computer Vision and Pattern Recognition, (2018)
IEEE Transactions on Pattern Analysis and Machine Intelligence, no. 9 (2018): 2131-2145
CVPR, (2017): 3319-3327
Commun. ACM, no. 6 (2017)
(2015)
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