Stephan Mandt
Title
Cited by
Cited by
Year
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
3992012
Stochastic Gradient Descent as Approximate Bayesian Inference
S Mandt, MD Hoffman, DM Blei
Journal of Machine Learning Research 18, 1-35, 2017
2972017
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
2032018
Dynamic Word Embeddings
R Bamler, S Mandt
International Conference on Machine Learning 70, 380-389, 2017
1552017
Exponential family embeddings
M Rudolph, F Ruiz, S Mandt, D Blei
Advances in Neural Information Processing Systems 29, 478-486, 2016
1002016
A Variational Analysis of Stochastic Gradient Algorithms
S Mandt, MD Hoffman, DM Blei
International Conference on Machine Learning 48, 354--363, 2016
962016
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
852010
Disentangled Sequential Autoencoder
Y Li, S Mandt
International Conference on Machine Learning 80, 5670-5679, 2018
70*2018
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
652018
Iterative Amortized Inference
J Marino, Y Yue, S Mandt
International Conference on Machine Learning 80, 3403--3412, 2018
652018
Determinantal Point Processes for Mini-Batch Diversification
C Zhang, H Kjellström, S Mandt
Uncertainty in Artificial Intelligence, 2017
562017
Disentangled sequential autoencoder
L Yingzhen, S Mandt
International Conference on Machine Learning, 5670-5679, 2018
332018
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
322020
Quasi-Monte Carlo Variational Inference
A Buchholz, F Wenzel, S Mandt
International Conference on Machine Learning 80, 668-677, 2018
322018
Variational tempering
S Mandt, J McInerney, F Abrol, R Ranganath, D Blei
Artificial Intelligence and Statistics, 704-712, 2016
322016
Smoothed gradients for stochastic variational inference
S Mandt, D Blei
Advances in Neural Information Processing Systems 27, 2438-2446, 2014
322014
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
312017
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
292011
GP-VAE: Deep Probabilistic Time Series Imputation
V Fortuin, D Baranchuk, G Rätsch, S Mandt
Artificial Intelligence and Statistics (AISTATS), 2020, 2020
28*2020
Perturbative Black Box Variational Inference
R Bamler, C Zhang, M Opper, S Mandt
Neural Information Processing Systems (NIPS 2017), 2017
282017
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