David Salinas
David Salinas
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Cited by
Cited by
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
D Salinas, V Flunkert, J Gasthaus, T Januschowski
International journal of forecasting 36 (3), 1181-1191, 2020
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
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
On challenges in machine learning model management
S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ...
Criteria for classifying forecasting methods
T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ...
International Journal of Forecasting 36 (1), 167-177, 2020
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
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
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
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
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
Bayesian Intermittent Demand Forecasting for Large Inventories
M Seeger, D Salinas, V Flunkert
Advances in Neural Information Processing Systems, 2016
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
DataWig: Missing value imputation for tables
F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ...
Journal of Machine Learning Research 20 (175), 1-6, 2019
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
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
Structure‐aware mesh decimation
D Salinas, F Lafarge, P Alliez
Computer Graphics Forum 34 (6), 211-227, 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
Image computation for polynomial dynamical systems using the Bernstein expansion
T Dang, D Salinas
Computer Aided Verification: 21st International Conference, CAV 2009 …, 2009
Syne tune: A library for large scale hyperparameter tuning and reproducible research
D Salinas, M Seeger, A Klein, V Perrone, M Wistuba, C Archambeau
International Conference on Automated Machine Learning, 16/1-23, 2022
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
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