Geoff Pleiss

Citirano

	Sve	Od 2019.
Citati	13099	12652
H-indeks	23	23
i10-indeks	30	30

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2017201820192020202120222023202469 295 659 1293 2072 2513 3039 3061

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Kilian WeinbergerCornell UniversityPotvrđena adresa e-pošte na cornell.edu
Jacob GardnerUniversity of PennsylvaniaPotvrđena adresa e-pošte na seas.upenn.edu
Andrew Gordon WilsonNew York UniversityPotvrđena adresa e-pošte na cims.nyu.edu
John CunninghamProfessor, Columbia UniversityPotvrđena adresa e-pošte na columbia.edu
Gao Huang （黄高）Associate Professor, Tsinghua UniversityPotvrđena adresa e-pošte na tsinghua.edu.cn
Yu SunPostdoc, Stanford UniversityPotvrđena adresa e-pošte na cs.stanford.edu
Chuan GuoResearch Scientist, FAIR, MetaPotvrđena adresa e-pošte na fb.com
John HopcroftCornell UniversityPotvrđena adresa e-pošte na cs.cornell.edu
Laurens van der MaatenDistinguished Research Scientist, Llama Team, Meta AIPotvrđena adresa e-pošte na meta.com
Martin JankowiakGenerate BiomedicinesPotvrđena adresa e-pošte na generatebiomedicines.com
David BindelProfessor of Computer Science, Cornell UniversityPotvrđena adresa e-pošte na cs.cornell.edu
Sharon LiUniversity of Wisconsin-MadisonPotvrđena adresa e-pošte na cs.wisc.edu
Felix WuCharacter AIPotvrđena adresa e-pošte na character.ai
Jon KleinbergProfessor of Computer Science, Cornell UniversityPotvrđena adresa e-pošte na cs.cornell.edu
Manish RaghavanMITPotvrđena adresa e-pošte na mit.edu
Zhuang LiuResearch Scientist, FAIR, MetaPotvrđena adresa e-pošte na berkeley.edu
Wei-Lun (Harry) Chao 趙偉崙Assistant Professor, The Ohio State UniversityPotvrđena adresa e-pošte na osu.edu
Yan WangSenior Research Scientist, NVIDIA ResearchPotvrđena adresa e-pošte na nvidia.com
bharath hariharanCornell UniversityPotvrđena adresa e-pošte na cs.cornell.edu
Gabriel Loaiza-GanemLayer 6 AIPotvrđena adresa e-pošte na layer6.ai

Prati

Geoff Pleiss

Assistant Professor, University of British Columbia

Potvrđena adresa e-pošte na stat.ubc.ca - Početna stranica

Machine Learning


Naslov Poredaj po navodima Poredaj po godini Poredaj po naslovu	Citirano Citirano	Godina
On calibration of modern neural networks C Guo, G Pleiss, Y Sun, KQ Weinberger International Conference on Machine Learning, 1321-1330, 2017	6478	2017
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	1356	2018
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	1147	2017
On fairness and calibration G Pleiss, M Raghavan, F Wu, J Kleinberg, KQ Weinberger Advances in Neural Information Processing Systems, 2017	1078	2017
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	550	2019
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	470	2020
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	369	2017
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	283	2019
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	269	2020
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	205	2017
Constant-time predictive distributions for Gaussian processes G Pleiss, JR Gardner, KQ Weinberger, AG Wilson International Conference on Machine Learning, 2018	129	2018
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	89	2018
Parametric Gaussian Process Regressors M Jankowiak, G Pleiss, JR Gardner International Conference on Machine Learning, 2020	88	2020
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	87	2020
Deep Ensembles Work, But Are They Necessary? T Abe, EK Buchanan, G Pleiss, R Zemel, JP Cunningham Advances in Neural Information Processing Systems, 2022	60	2022
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	54	2020
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	54	2017
Rectangular flows for manifold learning AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham Advances in Neural Information Processing Systems, 2021	42	2021
Variational Nearest Neighbor Gaussian Processes L Wu, G Pleiss, JP Cunningham International Conference on Machine Learning, 2022	38	2022
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization J Wenger, G Pleiss, P Hennig, JP Cunningham, JR Gardner International Conference on Machine Learning, 2022	32*	2022

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