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Geoff Pleiss
Geoff Pleiss
Verified email at columbia.edu - Homepage
Title
Cited by
Cited by
Year
On calibration of modern neural networks
C Guo, G Pleiss, Y Sun, KQ Weinberger
International Conference on Machine Learning, 1321-1330, 2017
25852017
Snapshot ensembles: Train 1, get m for free
G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger
International Conference on Learning Representations, 2017
6502017
On fairness and calibration
G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger
Advances in Neural Information Processing Systems, 2017
5302017
Gpytorch: Blackbox matrix-matrix gaussian process inference with gpu acceleration
JR Gardner, G Pleiss, KQ Weinberger, D Bindel, AG Wilson
Advances in Neural Information Processing Systems, 7576-7586, 2018
4842018
Deep feature interpolation for image content changes
P Upchurch, J Gardner, G Pleiss, K Bala, R Pless, N Snavely, ...
Computer Vision and Pattern Recognition, 2016
2312016
Convolutional Networks with Dense Connectivity
G Huang, Z Liu, G Pleiss, L Van Der Maaten, K Weinberger
Transactions on Pattern Analysis and Machine Intelligence, 2019
2012019
Pseudo-lidar++: Accurate depth for 3d object detection in autonomous driving
Y You, Y Wang, WL Chao, D Garg, G Pleiss, B Hariharan, M Campbell, ...
International Conference on Learning Representations, 2019
1972019
Exact Gaussian processes on a million data points
KA Wang, G Pleiss, JR Gardner, S Tyree, KQ Weinberger, AG Wilson
Advances in Neural Information Processing Systems, 2019
1512019
Memory-efficient implementation of densenets
G Pleiss, D Chen, G Huang, T Li, L Van Der Maaten, KQ Weinberger
arXiv preprint arXiv:1707.06990, 2017
1422017
Identifying mislabeled data using the area under the margin ranking
G Pleiss, T Zhang, ER Elenberg, KQ Weinberger
Advances in Neural Information Processing Systems, 2020
672020
Constant-time predictive distributions for Gaussian processes
G Pleiss, JR Gardner, KQ Weinberger, AG Wilson
International Conference on Machine Learning, 2018
642018
Product kernel interpolation for scalable Gaussian processes
JR Gardner, G Pleiss, R Wu, KQ Weinberger, AG Wilson
International Conference on Artificial Intelligence and Statistics, 2018
502018
Parametric Gaussian Process Regressors
M Jankowiak, G Pleiss, JR Gardner
International Conference on Machine Learning, 2019
32*2019
Uses and abuses of the cross-entropy loss: case studies in modern deep learning
E Gordon-Rodriguez, G Loaiza-Ganem, G Pleiss, JP Cunningham
NeurIPS “I Can’t Believe It’s Not Better!” Workshop, 2020
232020
Fast matrix square roots with applications to Gaussian processes and Bayesian optimization
G Pleiss, M Jankowiak, D Eriksson, A Damle, JR Gardner
Advances in Neural Information Processing Systems, 2020
182020
Potential predictability of regional precipitation and discharge extremes using synoptic-scale climate information via machine learning: An evaluation for the eastern …
J Knighton, G Pleiss, E Carter, S Lyon, MT Walter, S Steinschneider
Journal of Hydrometeorology 20 (5), 883-900, 2019
122019
Rectangular flows for manifold learning
AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham
Advances in Neural Information Processing Systems, 2021
102021
Bias-Free Scalable Gaussian Processes via Randomized Truncations
A Potapczynski, L Wu, D Biderman, G Pleiss, JP Cunningham
International Conference on Machine Learning, 2021
82021
Deep Sigma Point Processes
M Jankowiak, G Pleiss, JR Gardner
Conference on Uncertainty in Artificial Intelligence, 2020
82020
The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective
G Pleiss, JP Cunningham
Advances in Neural Information Processing Systems, 2021
62021
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