Ryan P. Adams

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David DuvenaudAssociate Professor, University of TorontoPatvirtintas el. paštas cs.toronto.edu
Hugo LarochelleGoogle DeepMind & MilaPatvirtintas el. paštas google.com
Matthew J. JohnsonGoogle BrainPatvirtintas el. paštas csail.mit.edu
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GooglePatvirtintas el. paštas eng.cam.ac.uk
Iain MurraySchool of Informatics, University of EdinburghPatvirtintas el. paštas ed.ac.uk
Richard ZemelProfessor of Computer Science, University of TorontoPatvirtintas el. paštas cs.toronto.edu
David MacKayProfessor of Natural Philosophy, Cavendish Laboratory, University of CambridgePatvirtintas el. paštas mrao.cam.ac.uk
Nando de FreitasCIFAR & DeepMindPatvirtintas el. paštas google.com
George E. DahlGoogle Inc.Patvirtintas el. paštas google.com
Abigail G. DoyleUCLAPatvirtintas el. paštas chem.ucla.edu
Peter OrbanzColumbia UniversityPatvirtintas el. paštas stat.columbia.edu
Jamie KirosOpenAIPatvirtintas el. paštas openai.com
Elaine AngelinoMiller Fellow, UC BerkeleyPatvirtintas el. paštas eecs.berkeley.edu
Emily B. FoxStanford UniversityPatvirtintas el. paštas stanford.edu
Margo SeltzerUniversity of British ColumbiaPatvirtintas el. paštas cs.ubc.ca
Diana CaiCenter for Computational Mathematics, Flatiron InstitutePatvirtintas el. paštas flatironinstitute.org
Sigrid AdriaenssensPrinceton UniversityPatvirtintas el. paštas princeton.edu
Michael I. JordanProfessor of Electrical Engineering and Computer Sciences and Professor of Statistics, UC BerkeleyPatvirtintas el. paštas cs.berkeley.edu
Hanna WallachPartner Research Manager, Microsoft ResearchPatvirtintas el. paštas dirichlet.net
Joshua B. TenenbaumMITPatvirtintas el. paštas mit.edu

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Ryan P. Adams

Princeton University

Patvirtintas el. paštas princeton.edu - Pagrindinis puslapis

Machine Learning Artificial Intelligence Statistics


Pavadinimas Rūšiuoti pagal šaltinius Rūšiuoti pagal metus Rūšiuoti pagal pavadinimą	Cituota Cituota	Metai
Practical bayesian optimization of machine learning algorithms J Snoek, H Larochelle, RP Adams Advances in neural information processing systems 25, 2012	10238	2012
Taking the human out of the loop: A review of Bayesian optimization B Shahriari, K Swersky, Z Wang, RP Adams, N De Freitas Proceedings of the IEEE 104 (1), 148-175, 2015	5459	2015
Convolutional networks on graphs for learning molecular fingerprints DK Duvenaud, D Maclaurin, J Iparraguirre, R Bombarell, T Hirzel, ... Advances in neural information processing systems 28, 2015	4345	2015
Automatic chemical design using a data-driven continuous representation of molecules R Gómez-Bombarelli, JN Wei, D Duvenaud, JM Hernández-Lobato, ... ACS central science 4 (2), 268-276, 2018	3380	2018
Scalable bayesian optimization using deep neural networks J Snoek, O Rippel, K Swersky, R Kiros, N Satish, N Sundaram, M Patwary, ... International conference on machine learning, 2171-2180, 2015	1299	2015
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	1135	2015
Bayesian online changepoint detection RP Adams, DJC MacKay arXiv preprint arXiv:0710.3742, 2007	1072	2007
Gradient-based hyperparameter optimization through reversible learning D Maclaurin, D Duvenaud, R Adams International conference on machine learning, 2113-2122, 2015	1060	2015
Design of efficient molecular organic light-emitting diodes by a high-throughput virtual screening and experimental approach R Gómez-Bombarelli, J Aguilera-Iparraguirre, TD Hirzel, D Duvenaud, ... Nature materials 15 (10), 1120-1127, 2016	972	2016
Multi-task bayesian optimization K Swersky, J Snoek, RP Adams Advances in neural information processing systems 26, 2013	915	2013
Gaussian process kernels for pattern discovery and extrapolation A Wilson, R Adams International conference on machine learning, 1067-1075, 2013	807	2013
Mapping sub-second structure in mouse behavior AB Wiltschko, MJ Johnson, G Iurilli, RE Peterson, JM Katon, ... Neuron 88 (6), 1121-1135, 2015	711	2015
Bayesian optimization with unknown constraints MA Gelbart, J Snoek, RP Adams arXiv preprint arXiv:1403.5607, 2014	651	2014
Bayesian reaction optimization as a tool for chemical synthesis BJ Shields, J Stevens, J Li, M Parasram, F Damani, JIM Alvarado, ... Nature 590 (7844), 89-96, 2021	607	2021
Elliptical slice sampling I Murray, R Adams, D MacKay Proceedings of the thirteenth international conference on artificial …, 2010	559	2010
Composing graphical models with neural networks for structured representations and fast inference MJ Johnson, DK Duvenaud, A Wiltschko, RP Adams, SR Datta Advances in neural information processing systems 29, 2016	545	2016
Spectral representations for convolutional neural networks O Rippel, J Snoek, RP Adams Advances in neural information processing systems 28, 2015	407	2015
Discovering latent network structure in point process data S Linderman, R Adams International conference on machine learning, 1413-1421, 2014	342	2014
Autograd: Effortless gradients in numpy D Maclaurin, D Duvenaud, RP Adams ICML 2015 AutoML workshop 238 (5), 2015	331	2015
Freeze-thaw Bayesian optimization K Swersky, J Snoek, RP Adams arXiv preprint arXiv:1406.3896, 2014	316	2014

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