| DeepAR: Probabilistic forecasting with autoregressive recurrent networks D Salinas, V Flunkert, J Gasthaus, T Januschowski International Journal of Forecasting 36 (3), 1181-1191, 2020 | 200 | 2020 |
| Efficient data structure for representing and simplifying simplicial complexes in high dimensions D Attali, A Lieutier, D Salinas International Journal of Computational Geometry & Applications 22 (04), 279-303, 2012 | 74 | 2012 |
| 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 | 74 | 2011 |
| Vietoris–Rips complexes also provide topologically correct reconstructions of sampled shapes D Attali, A Lieutier, D Salinas Computational Geometry 46 (4), 448-465, 2013 | 70 | 2013 |
| 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 | 70 | 2011 |
| 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 | 50 | 2017 |
| Bayesian Intermittent Demand Forecasting for Large Inventories M Seeger, D Salinas, V Flunkert Advances in Neural Information Processing Systems, 2016 | 50 | 2016 |
| Structure‐aware mesh decimation D Salinas, F Lafarge, P Alliez Computer Graphics Forum 34 (6), 211-227, 2015 | 37 | 2015 |
| On challenges in machine learning model management S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ... | 35 | 2018 |
| GluonTS: Probabilistic and Neural Time Series Modeling in Python A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ... Journal of Machine Learning Research 21 (116), 1-6, 2020 | 33* | 2020 |
| Image computation for polynomial dynamical systems using the Bernstein expansion T Dang, D Salinas International Conference on Computer Aided Verification, 219-232, 2009 | 31 | 2009 |
| 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 | 30 | 2019 |
| 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 | 29 | 2018 |
| Criteria for classifying forecasting methods T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ... International Journal of Forecasting 36 (1), 167-177, 2020 | 28 | 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, 6827-6837, 2019 | 17 | 2019 |
| 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 | 14 | 2020 |
| Neural forecasting: Introduction and literature overview K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ... arXiv preprint arXiv:2004.10240, 2020 | 13 | 2020 |
| Now available in amazon sagemaker: Deepar algorithm for more accurate time series forecasting T Januschowski, D Arpin, D Salinas, V Flunkert, J Gasthaus, L Stella, ... | 9 | 2018 |
| 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 | 9 | 2017 |
| A Quantile-based Approach for Hyperparameter Transfer Learning D Salinas, H Shen, V Perrone International Conference on Machine Learning 2020 37, 7706--7716, 2020 | 7* | 2020 |
