Stephan Mandt
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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
Stochastic Gradient Descent as Approximate Bayesian Inference
S Mandt, MD Hoffman, DM Blei
Journal of Machine Learning Research 18, 1-35, 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
Dynamic Word Embeddings
R Bamler, S Mandt
International Conference on Machine Learning 70, 380-389, 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
Disentangled Sequential Autoencoder
Y Li, S Mandt
International Conference on Machine Learning 80, 5670-5679, 2018
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
GP-VAE: Deep Probabilistic Time Series Imputation
V Fortuin, D Baranchuk, G Rätsch, S Mandt
Artificial Intelligence and Statistics (AISTATS), 2020, 2020
Exponential Family Embeddings
MR Rudolph, FJR Ruiz, S Mandt, DM Blei
Neural Information Processing Systems, 2016
A Variational Analysis of Stochastic Gradient Algorithms
S Mandt, MD Hoffman, DM Blei
International Conference on Machine Learning 48, 354--363, 2016
Iterative Amortized Inference
J Marino, Y Yue, S Mandt
International Conference on Machine Learning 80, 3403--3412, 2018
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
Determinantal Point Processes for Mini-Batch Diversification
C Zhang, H Kjellström, S Mandt
Uncertainty in Artificial Intelligence, 2017
Deep Generative Video Compression
J Han, S Lombardo, C Schroers, S Mandt
Neural Information Processing Systems, 2019
Quasi-Monte Carlo Variational Inference
A Buchholz, F Wenzel, S Mandt
International Conference on Machine Learning 80, 668-677, 2018
Improving inference for neural image compression
Y Yang, R Bamler, S Mandt
Neural Information Processing Systems, 2020
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
Variational tempering
S Mandt, J McInerney, F Abrol, R Ranganath, D Blei
Artificial intelligence and statistics, 704-712, 2016
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
Active Mini-Batch Sampling using Repulsive Point Processes
C Zhang, C Öztireli, S Mandt, G Salvi
Thirty-Third AAAI Conference on Artificial Intelligence (AAAI), 2019
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