José Miguel Hernández-Lobato

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Daniel Hernández-LobatoAssociate Professor, Universidad Autónoma de MadridZweryfikowany adres z uam.es
Zoubin GhahramaniProfessor, University of Cambridge, and Distinguished Researcher, GoogleZweryfikowany adres z eng.cam.ac.uk
Richard E TurnerProfessor, University of CambridgeZweryfikowany adres z cam.ac.uk
Ryan P. AdamsPrinceton UniversityZweryfikowany adres z princeton.edu
Yingzhen LiImperial College LondonZweryfikowany adres z imperial.ac.uk
Cheng ZhangPrincipal Researcher, Microsoft Research, Cambridge, UKZweryfikowany adres z kth.se
Alan Aspuru-GuzikProfessor of Chemistry and Computer Science, University of Toronto (starting July 1, 2018)Zweryfikowany adres z utoronto.ca
Matt J. KusnerAssociate Professor, University College LondonZweryfikowany adres z ucl.ac.uk
Brooks PaigeAssociate Professor, University College LondonZweryfikowany adres z ucl.ac.uk
Javier AntoranUniversity of CambridgeZweryfikowany adres z cam.ac.uk
Chao MaSenior Researcher, Microsoft Research CambridgeZweryfikowany adres z microsoft.com
Neil HoulsbyGoogleZweryfikowany adres z google.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichZweryfikowany adres z tuebingen.mpg.de
David BrooksHaley Family Professor of Computer Science, Harvard UniversityZweryfikowany adres z eecs.harvard.edu
Gu-Yeon WeiRobert and Suzanne Case Professor of EE and CS, Harvard University and Fellow, Samsung ResearchZweryfikowany adres z eecs.harvard.edu
Thang D BuiLecturer, Australian National UniversityZweryfikowany adres z anu.edu.au
Jorge Aguilera-IparraguirreRecursion PharmaceuticalsZweryfikowany adres z recursionpharma.com
David DuvenaudAssociate Professor, University of TorontoZweryfikowany adres z cs.toronto.edu
Alberto SuárezProfessor of Artificial Intelligence, Computer Science Department, Universidad Autónoma de MadridZweryfikowany adres z uam.es
Marton HavasiHarvard SEASZweryfikowany adres z g.harvard.edu

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José Miguel Hernández-Lobato

Professor of Machine Learning, University of Cambridge

Zweryfikowany adres z cam.ac.uk - Strona główna

Bayesian deep learning approximate inference deep generative modeling automatic molecular design reinforcement learning


Tytuł Sortuj wg cytatów Sortuj wg roku Sortuj wg tytułu	Cytowane przez Cytowane przez	Rok
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	2413	2018
Probabilistic backpropagation for scalable learning of bayesian neural networks JM Hernández-Lobato, R Adams International conference on machine learning, 1861-1869, 2015	937	2015
Grammar variational autoencoder MJ Kusner, B Paige, JM Hernández-Lobato International conference on machine learning, 1945-1954, 2017	821	2017
Minerva: Enabling low-power, highly-accurate deep neural network accelerators B Reagen, P Whatmough, R Adolf, S Rama, H Lee, SK Lee, ... ACM SIGARCH Computer Architecture News 44 (3), 267-278, 2016	659	2016
Predictive entropy search for efficient global optimization of black-box functions JM Hernández-Lobato, MW Hoffman, Z Ghahramani Advances in neural information processing systems 27, 2014	659	2014
Gans for sequences of discrete elements with the gumbel-softmax distribution MJ Kusner, JM Hernández-Lobato arXiv preprint arXiv:1611.04051, 2016	313	2016
Decomposition of uncertainty in Bayesian deep learning for efficient and risk-sensitive learning S Depeweg, JM Hernandez-Lobato, F Doshi-Velez, S Udluft International Conference on Machine Learning, 1184-1193, 2018	275	2018
Deep Gaussian processes for regression using approximate expectation propagation T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner International conference on machine learning, 1472-1481, 2016	237	2016
Black-box alpha divergence minimization J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ... International conference on machine learning, 1511-1520, 2016	235	2016
Constrained Bayesian optimization for automatic chemical design using variational autoencoders RR Griffiths, JM Hernández-Lobato Chemical science 11 (2), 577-586, 2020	214	2020
Predictive entropy search for multi-objective bayesian optimization D Hernández-Lobato, J Hernandez-Lobato, A Shah, R Adams International conference on machine learning, 1492-1501, 2016	209	2016
Deterministic variational inference for robust bayesian neural networks A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt arXiv preprint arXiv:1810.03958, 2018	172	2018
Learning and policy search in stochastic dynamical systems with bayesian neural networks S Depeweg, JM Hernández-Lobato, F Doshi-Velez, S Udluft arXiv preprint arXiv:1605.07127, 2016	165	2016
Probabilistic matrix factorization with non-random missing data JM Hernández-Lobato, N Houlsby, Z Ghahramani International conference on machine learning, 1512-1520, 2014	160	2014
Parallel and distributed Thompson sampling for large-scale accelerated exploration of chemical space JM Hernández-Lobato, J Requeima, EO Pyzer-Knapp, A Aspuru-Guzik International conference on machine learning, 1470-1479, 2017	151	2017
Predictive entropy search for Bayesian optimization with unknown constraints JM Hernández-Lobato, M Gelbart, M Hoffman, R Adams, Z Ghahramani International conference on machine learning, 1699-1707, 2015	144	2015
A general framework for constrained Bayesian optimization using information-based search JM Hernández-Lobato, MA Gelbart, RP Adams, MW Hoffman, ... MIT Press, 2016	143	2016
Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck International Conference on Machine Learning, 1645-1654, 2017	141	2017
Collaborative gaussian processes for preference learning N Houlsby, F Huszar, Z Ghahramani, J Hernández-lobato Advances in neural information processing systems 25, 2012	140	2012
Stochastic expectation propagation Y Li, JM Hernández-Lobato, RE Turner Advances in neural information processing systems 28, 2015	138	2015

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