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Geoff Pleiss
Geoff Pleiss
Assistant Professor, University of British Columbia
E-mailová adresa ověřena na: stat.ubc.ca - Domovská stránka
Název
Citace
Citace
Rok
On calibration of modern neural networks
C Guo, G Pleiss, Y Sun, KQ Weinberger
International Conference on Machine Learning, 1321-1330, 2017
68012017
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
14292018
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
11882017
On fairness and calibration
G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger
Advances in Neural Information Processing Systems, 2017
11272017
Convolutional Networks with Dense Connectivity
G Huang, Z Liu, G Pleiss, L Van Der Maaten, KQ Weinberger
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019
5792019
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, 2020
4832020
Deep feature interpolation for image content changes
P Upchurch, JR Gardner, G Pleiss, K Bala, R Pless, N Snavely, ...
Computer Vision and Pattern Recognition, 2017
3802017
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
2942020
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
2912019
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
2082017
Constant-time predictive distributions for Gaussian processes
G Pleiss, JR Gardner, KQ Weinberger, AG Wilson
International Conference on Machine Learning, 2018
1342018
Parametric Gaussian Process Regressors
M Jankowiak, G Pleiss, JR Gardner
International Conference on Machine Learning, 2020
1002020
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, 1-10, 2020
972020
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
902018
Deep Ensembles Work, But Are They Necessary?
T Abe, EK Buchanan, G Pleiss, R Zemel, JP Cunningham
Advances in Neural Information Processing Systems, 2022
642022
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
602020
Snapshot ensembles: Train 1, get m for free,” 2017
G Huang, Y Li, G Pleiss, Z Liu, JE Hopcroft, KQ Weinberger
arXiv preprint arXiv:1704.00109, 1-14, 2017
552017
Variational Nearest Neighbor Gaussian Processes
L Wu, G Pleiss, JP Cunningham
International Conference on Machine Learning, 2022
442022
Rectangular flows for manifold learning
AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham
Advances in Neural Information Processing Systems, 2021
442021
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
J Wenger, G Pleiss, P Hennig, JP Cunningham, JR Gardner
International Conference on Machine Learning, 2022
35*2022
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Články 1–20