Stephan Mandt

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Marius KloftProfessor, RPTU Kaiserslautern-LandauVerified email at cs.uni-kl.de
Robert BamlerUniveristy of Tübingen, GermanyVerified email at uni-tuebingen.de
Maja RudolphUW MadisonVerified email at wisc.edu
Cheng ZhangGenAI, MetaVerified email at meta.com
Florian WenzelCTO/Co-founder at Mirelo AIVerified email at mirelo.ai
Yibo YangUniversity of California, IrvineVerified email at uci.edu
David BleiProfessor of Statistics and Computer Science, Columbia UniversityVerified email at columbia.edu
Ruihan YangUniversity of California, IrvineVerified email at uci.edu
Vincent FortuinPrincipal Investigator, Helmholtz AI & TU MunichVerified email at tum.de
Chen QiuResearch Scientist, Bosch Center for AIVerified email at us.bosch.com
Achim RoschInstitute for Theoretical Physics, University of CologneVerified email at thp.uni-koeln.de
Joseph MarinoSenior Research Scientist, Google DeepMindVerified email at deepmind.com
Yingzhen LiImperial College LondonVerified email at imperial.ac.uk
Matthew D. HoffmanResearch Scientist, Google ResearchVerified email at google.com
Fabian JirasekLaboratory of Engineering Themodynamics (LTD), RPTU KaiserslauternVerified email at rptu.de
Sebastian NowozinGoogle DeepMindVerified email at deepmind.com
Tim SalimansGoogle DeepMind AmsterdamVerified email at google.com
Bastiaan S. VeelingMicrosoft Research AmsterdamVerified email at uva.nl
Rodolphe JENATTONCTO at BioptimusVerified email at bioptimus.com
Jasper SnoekGoogle BrainVerified email at google.com

Stephan Mandt

Associate Professor, University of California, Irvine

Verified email at uci.edu - Homepage

Machine Learning Variational Inference Neural Data Compression ML for Science Generative Models


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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	864	2018
Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei Journal of Machine Learning Research 18, 1-35, 2017	698	2017
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	522	2012
Dynamic Word Embeddings R Bamler, S Mandt International Conference on Machine Learning 70, 380-389, 2017	466	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	386	2020
Disentangled Sequential Autoencoder Y Li, S Mandt International Conference on Machine Learning 80, 5670-5679, 2018	341	2018
GP-VAE: Deep Probabilistic Time Series Imputation V Fortuin, D Baranchuk, G Rätsch, S Mandt Artificial Intelligence and Statistics (AISTATS), 2020, 2020	306	2020
Image anomaly detection with generative adversarial networks L Deecke, R Vandermeulen, L Ruff, S Mandt, M Kloft Machine Learning and Knowledge Discovery in Databases: European Conference …, 2019	280	2019
Diffusion probabilistic modeling for video generation R Yang, P Srivastava, S Mandt Entropy 25 (10), 1469, 2023	206	2023
Iterative Amortized Inference J Marino, Y Yue, S Mandt International Conference on Machine Learning 80, 3403--3412, 2018	191	2018
A Variational Analysis of Stochastic Gradient Algorithms S Mandt, MD Hoffman, DM Blei International Conference on Machine Learning 48, 354--363, 2016	172	2016
Exponential Family Embeddings MR Rudolph, FJR Ruiz, S Mandt, DM Blei Neural Information Processing Systems, 2016	158	2016
Neural Transformation Learning for Deep Anomaly Detection Beyond Images C Qiu, T Pfrommer, M Kloft, S Mandt, M Rudolph International Conference on Machine Learning, 2021	132	2021
Improving inference for neural image compression Y Yang, R Bamler, S Mandt Neural Information Processing Systems, 2020	112	2020
Equilibration rates and negative absolute temperatures for ultracold atoms in optical lattices A Rapp, S Mandt, A Rosch Phys. Rev. Lett. 105 (220405), 2010	108	2010
Deep Generative Video Compression J Han, S Lombardo, C Schroers, S Mandt Neural Information Processing Systems, 2019	99*	2019
An introduction to neural data compression Y Yang, S Mandt, L Theis Foundations and Trends® in Computer Graphics and Vision 15 (2), 113-200, 2023	93	2023
Determinantal Point Processes for Mini-Batch Diversification C Zhang, H Kjellström, S Mandt Uncertainty in Artificial Intelligence, 2017	92	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	85	2020
Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt International Conference on Machine Learning 80, 668-677, 2018	71	2018

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