| 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 | 614 | 2018 |
| Stochastic Gradient Descent as Approximate Bayesian Inference S Mandt, MD Hoffman, DM Blei Journal of Machine Learning Research 18, 1-35, 2017 | 535 | 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 | 495 | 2012 |
| Dynamic Word Embeddings R Bamler, S Mandt International Conference on Machine Learning 70, 380-389, 2017 | 357 | 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 | 269 | 2020 |
| Disentangled Sequential Autoencoder Y Li, S Mandt International Conference on Machine Learning 80, 5670-5679, 2018 | 251 | 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 | 210 | 2019 |
| GP-VAE: Deep Probabilistic Time Series Imputation V Fortuin, D Baranchuk, G Rätsch, S Mandt Artificial Intelligence and Statistics (AISTATS), 2020, 2020 | 163* | 2020 |
| Exponential Family Embeddings MR Rudolph, FJR Ruiz, S Mandt, DM Blei Neural Information Processing Systems, 2016 | 143 | 2016 |
| A Variational Analysis of Stochastic Gradient Algorithms S Mandt, MD Hoffman, DM Blei International Conference on Machine Learning 48, 354--363, 2016 | 142 | 2016 |
| Iterative Amortized Inference J Marino, Y Yue, S Mandt International Conference on Machine Learning 80, 3403--3412, 2018 | 139 | 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 | 101 | 2010 |
| Determinantal Point Processes for Mini-Batch Diversification C Zhang, H Kjellström, S Mandt Uncertainty in Artificial Intelligence, 2017 | 79 | 2017 |
| Deep Generative Video Compression J Han, S Lombardo, C Schroers, S Mandt Neural Information Processing Systems, 2019 | 74* | 2019 |
| Improving inference for neural image compression Y Yang, R Bamler, S Mandt Neural Information Processing Systems, 2020 | 63 | 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 | 57 | 2020 |
| Quasi-Monte Carlo Variational Inference A Buchholz, F Wenzel, S Mandt International Conference on Machine Learning 80, 668-677, 2018 | 57 | 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 | 52 | 2017 |
| Variational tempering S Mandt, J McInerney, F Abrol, R Ranganath, D Blei Artificial intelligence and statistics, 704-712, 2016 | 51 | 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 | 48 | 2021 |
Robert BamlerUniveristy of Tübingen, Germany在 uni-tuebingen.de 的电子邮件经过验证
