David Salinas
Title
Cited by
Cited by
Year
DeepAR: Probabilistic forecasting with autoregressive recurrent networks
D Salinas, V Flunkert, J Gasthaus, T Januschowski
International Journal of Forecasting 36 (3), 1181-1191, 2020
3972020
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
842012
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
842011
GluonTS: Probabilistic and Neural Time Series Modeling in Python.
A Alexandrov, K Benidis, M Bohlke-Schneider, V Flunkert, J Gasthaus, ...
J. Mach. Learn. Res. 21 (116), 1-6, 2020
80*2020
Vietoris–Rips complexes also provide topologically correct reconstructions of sampled shapes
D Attali, A Lieutier, D Salinas
Computational Geometry 46 (4), 448-465, 2013
782013
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
782011
On challenges in machine learning model management
S Schelter, F Biessmann, T Januschowski, D Salinas, S Seufert, ...
722018
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
692017
Bayesian Intermittent Demand Forecasting for Large Inventories
M Seeger, D Salinas, V Flunkert
Advances in Neural Information Processing Systems, 2016
612016
Criteria for classifying forecasting methods
T Januschowski, J Gasthaus, Y Wang, D Salinas, V Flunkert, ...
International Journal of Forecasting 36 (1), 167-177, 2020
532020
Structure‐aware mesh decimation
D Salinas, F Lafarge, P Alliez
Computer Graphics Forum 34 (6), 211-227, 2015
472015
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
452019
Neural forecasting: Introduction and literature overview
K Benidis, SS Rangapuram, V Flunkert, B Wang, D Maddix, C Turkmen, ...
arXiv preprint arXiv:2004.10240, 2020
442020
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
392018
High-dimensional multivariate forecasting with low-rank gaussian copula processes
D Salinas, M Bohlke-Schneider, L Callot, R Medico, J Gasthaus
arXiv preprint arXiv:1910.03002, 2019
362019
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
342020
Image computation for polynomial dynamical systems using the Bernstein expansion
T Dang, D Salinas
International Conference on Computer Aided Verification, 219-232, 2009
322009
DataWig: Missing Value Imputation for Tables.
F Biessmann, T Rukat, P Schmidt, P Naidu, S Schelter, A Taptunov, ...
J. Mach. Learn. Res. 20, 175:1-175:6, 2019
262019
A Quantile-based Approach for Hyperparameter Transfer Learning
D Salinas, H Shen, V Perrone
International Conference on Machine Learning 2020 37, 7706--7716, 2020
142020
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
112017
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