Stephan Mandt

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Robert BamlerUniveristy of Tübingen, GermanyVerified email at uni-tuebingen.de
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Cheng ZhangMicrosoft Research, CambridgeVerified email at kth.se
Marius KloftTU KaiserslauternVerified email at cs.uni-kl.de
Achim RoschInstitute for Theoretical Physics, University of CologneVerified email at thp.uni-koeln.de
Florian WenzelGoogle Brain BerlinVerified email at google.com
Immanuel BlochMax-Planck Institute of Quantum Optics; Ludwig-Maximilians UniversityVerified email at mpq.mpg.de
Matthew D. HoffmanResearch Scientist, Google ResearchVerified email at google.com
Joseph MarinoCalifornia Institute of TechnologyVerified email at caltech.edu
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Yisong YueCalifornia Institute of TechnologyVerified email at caltech.edu
Hedvig Kjellström (Sidenbladh)Professor at KTH Royal Institute of Technology, Lead AI Scientist at Silo AI, SwedenVerified email at kth.se
Salvator LombardoVerified email at cornell.edu
Sebastian NowozinPartner Research Manager, Machine Intelligence, Microsoft Research (Cambridge, UK)Verified email at microsoft.com
Tim SalimansGoogle Brain AmsterdamVerified email at google.com
Bastiaan S. VeelingUniversity of AmsterdamVerified email at uva.nl
Rodolphe JENATTONSenior machine learning scientist at Google Brain, BerlinVerified email at google.com
Kevin RothETH ZurichVerified email at inf.ethz.ch
Jasper SnoekGoogle BrainVerified email at google.com
Jakub ŚwiątkowskiPhD Student, University of WarsawVerified email at mimuw.edu.pl

Stephan Mandt

Assistant Professor, University of California, Irvine

Verified email at uci.edu - Homepage

Machine Learning Variational Inference Neural Compression Deep Learning Generative Models


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Fermionic transport and out-of-equilibrium dynamics in a homogeneous Hubbard model with ultracold atoms U Schneider, L Hackermüller, JP Ronzheimer, S Will, S Braun, T Best, ... Nature Physics 8 (3), 213-218, 2012	409	2012
Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei Journal of Machine Learning Research 18, 1-35, 2017	325	2017
Advances in variational inference C Zhang, J Bütepage, H Kjellström, S Mandt IEEE transactions on pattern analysis and machine intelligence 41 (8), 2008-2026, 2018	242	2018
Dynamic Word Embeddings R Bamler, S Mandt International Conference on Machine Learning 70, 380-389, 2017	172	2017
A Variational Analysis of Stochastic Gradient Algorithms S Mandt, MD Hoffman, DM Blei International Conference on Machine Learning 48, 354--363, 2016	101	2016
Exponential Family Embeddings MR Rudolph, FJR Ruiz, S Mandt, DM Blei Neural Information Processing Systems, 2016	100	2016
Equilibration rates and negative absolute temperatures for ultracold atoms in optical lattices A Rapp, S Mandt, A Rosch Physical review letters 105 (22), 220405, 2010	87	2010
Image anomaly detection with generative adversarial networks L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft Joint european conference on machine learning and knowledge discovery in …, 2018	80	2018
Disentangled Sequential Autoencoder Y Li, S Mandt International Conference on Machine Learning 80, 5670-5679, 2018	76*	2018
Iterative Amortized Inference J Marino, Y Yue, S Mandt International Conference on Machine Learning 80, 3403--3412, 2018	67	2018
Determinantal Point Processes for Mini-Batch Diversification C Zhang, H Kjellström, S Mandt Uncertainty in Artificial Intelligence, 2017	59	2017
How good is the bayes posterior in deep neural networks really? F Wenzel, K Roth, BS Veeling, J Świątkowski, L Tran, S Mandt, J Snoek, ... International Conference on Machine Learning, 2020, 2020	47	2020
GP-VAE: Deep Probabilistic Time Series Imputation V Fortuin, D Baranchuk, G Rätsch, S Mandt Artificial Intelligence and Statistics (AISTATS), 2020, 2020	43*	2020
Disentangled sequential autoencoder L Yingzhen, S Mandt International Conference on Machine Learning, 5670-5679, 2018	39	2018
Variational tempering S Mandt, J McInerney, F Abrol, R Ranganath, D Blei Artificial Intelligence and Statistics, 704-712, 2016	36	2016
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt International Conference on Machine Learning 80, 668-677, 2018	34	2018
Factorized Variational Autoencoders for Modeling Audience Reactions to Movies Z Deng, R Navarathna, P Carr, S Mandt, Y Yue, I Matthews, G Mori Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017	34	2017
Smoothed Gradients for Stochastic Variational Inference S Mandt, D Blei Neural Information Processing Systems, 2014	33	2014
Deep Generative Video Compression J Han, S Lombardo, C Schroers, S Mandt Neural Information Processing Systems, 2019	32*	2019
Interacting fermionic atoms in optical lattices diffuse symmetrically upwards and downwards in a gravitational potential S Mandt, A Rapp, A Rosch Physical review letters 106 (25), 250602, 2011	29	2011

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