| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International Journal of Forecasting 36 (3), 1181-1191, 2020 | 1163 | 2020 |
| Gluonts: Probabilistic and neural time series modeling in python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... The Journal of Machine Learning Research 21 (1), 4629-4634, 2020 | 209* | 2020 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 150 | 2015 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 138 | 2020 |
| High-dimensional multivariate forecasting with low-rank gaussian copula processes D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus Advances in neural information processing systems 32, 2019 | 128 | 2019 |
| Probabilistic forecasting with spline quantile function RNNs J Gasthaus, K Benidis, Y Wang, SS Rangapuram, D Salinas, V Flunkert, ... The 22nd international conference on artificial intelligence and statistics …, 2019 | 121 | 2019 |
| Probabilistic demand forecasting at scale JH Böse, V Flunkert, J Gasthaus, T Januschowski, D Lange, D Salinas, ... Proceedings of the VLDB Endowment 10 (12), 1694-1705, 2017 | 116 | 2017 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240 6, 2020 | 107 | 2020 |
| Vietoris–Rips complexes also provide topologically correct reconstructions of sampled shapes D Attali, A Lieutier, D Salinas Proceedings of the twenty-seventh annual symposium on Computational geometry …, 2011 | 107 | 2011 |
| Bayesian Intermittent Demand Forecasting for Large Inventories M Seeger, D Salinas, V Flunkert Advances in Neural Information Processing Systems, 2016 | 101 | 2016 |
| Elastic machine learning algorithms in amazon sagemaker E Liberty, Z Karnin, B Xiang, L Rouesnel, B Coskun, R Nallapati, ... Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 93 | 2020 |
| Efficient data structure for representing and simplifying simplicial complexes in high dimensions D Attali, A Lieutier, D Salinas Proceedings of the Twenty-seventh Annual Symposium on Computational Geometry …, 2011 | 89 | 2011 |
| Deep Learning for Missing Value Imputation in Tables with Non-Numerical Data F Biessmann, D Salinas, S Schelter, P Schmidt, D Lange Proceedings of the 27th ACM International Conference on Information and …, 2018 | 74 | 2018 |
| DataWig: Missing Value Imputation for Tables. F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ... J. Mach. Learn. Res. 20 (175), 1-6, 2019 | 71 | 2019 |
| Structure‐aware mesh decimation D Salinas, F Lafarge, P Alliez Computer Graphics Forum 34 (6), 211-227, 2015 | 70 | 2015 |
| A Quantile-based Approach for Hyperparameter Transfer Learning D Salinas, H Shen, V Perrone International Conference on Machine Learning 2020 37, 7706--7716, 2020 | 39* | 2020 |
| Image computation for polynomial dynamical systems using the Bernstein expansion T Dang, D Salinas Computer Aided Verification: 21st International Conference, CAV 2009 …, 2009 | 37 | 2009 |
| Deep learning for time series forecasting: Tutorial and literature survey K Benidis, SS Rangapuram, V Flunkert, Y Wang, D Maddix, C Turkmen, ... ACM Computing Surveys 55 (6), 1-36, 2022 | 24 | 2022 |
| Approximate bayesian inference in linear state space models for intermittent demand forecasting at scale M Seeger, S Rangapuram, Y Wang, D Salinas, J Gasthaus, ... arXiv preprint arXiv:1709.07638, 2017 | 20 | 2017 |
| The effectiveness of discretization in forecasting: An empirical study on neural time series models S Rabanser, T Januschowski, V Flunkert, D Salinas, J Gasthaus arXiv preprint arXiv:2005.10111, 2020 | 14 | 2020 |
