Stephan Mandt

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

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	461	2012
Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei Journal of Machine Learning Research 18, 1-35, 2017	441	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	440	2018
Dynamic Word Embeddings R Bamler, S Mandt International Conference on Machine Learning 70, 380-389, 2017	225	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	163	2020
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	148	2018
Exponential Family Embeddings MR Rudolph, FJR Ruiz, S Mandt, DM Blei Neural Information Processing Systems, 2016	129	2016
A Variational Analysis of Stochastic Gradient Algorithms S Mandt, MD Hoffman, DM Blei International Conference on Machine Learning 48, 354--363, 2016	124	2016
Iterative Amortized Inference J Marino, Y Yue, S Mandt International Conference on Machine Learning 80, 3403--3412, 2018	115	2018
Disentangled Sequential Autoencoder Y Li, S Mandt International Conference on Machine Learning 80, 5670-5679, 2018	113*	2018
GP-VAE: Deep Probabilistic Time Series Imputation V Fortuin, D Baranchuk, G Rätsch, S Mandt Artificial Intelligence and Statistics (AISTATS), 2020, 2020	106*	2020
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	91	2010
Disentangled sequential autoencoder L Yingzhen, S Mandt International Conference on Machine Learning, 5670-5679, 2018	75	2018
Determinantal Point Processes for Mini-Batch Diversification C Zhang, H Kjellström, S Mandt Uncertainty in Artificial Intelligence, 2017	74	2017
Deep Generative Video Compression J Han, S Lombardo, C Schroers, S Mandt Neural Information Processing Systems, 2019	58*	2019
Variational tempering S Mandt, J McInerney, F Abrol, R Ranganath, D Blei Artificial intelligence and statistics, 704-712, 2016	48	2016
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt International Conference on Machine Learning 80, 668-677, 2018	47	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	44	2017
Machine learning in thermodynamics: Prediction of activity coefficients by matrix completion F Jirasek, RAS Alves, J Damay, RA Vandermeulen, R Bamler, M Bortz, ... The journal of physical chemistry letters 11 (3), 981-985, 2020	35	2020
Smoothed Gradients for Stochastic Variational Inference S Mandt, D Blei Neural Information Processing Systems, 2014	35	2014

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