Syama Sundar Rangapuram

Author

Saarland University ORCID

Also Known As

Rangapuram, Syama, Rangapuram, Syama Sundar, Syama Rangapuram, Syama Sundar Rangapuram

Publications

1.7K

Citations

H-Index

Concepts

All Affiliations

Saarland University Amazon (United States)Max Planck Society Amazon (Germany)Technical University of Munich

About

Syama Sundar Rangapuram is an author at Saarland University specializing in engineering, computer science, and data science.

Top Concepts

#	Concept	H-Index	Publications	Citations
1	Engineering	12	16	1.2K
2	Business	4	4	179
3	Social Sciences	1	1	71
4	Biostatistics	1	1	10
5	Communication	1	1	84

Syama Sundar Rangapuram

Publications

1.4K

Citations

H-Index

Page 1

	Year	Citations
Deep State Space Models for Time Series Forecasting Syama Sundar Rangapuram, Matthias Seeger, Jan Gasthaus, Neural Information Processing Systems Forecasting MethodologyDeep Neural NetworksEngineeringMachine LearningData Science	2018	441
GluonTS: Probabilistic and Neural Time Series Modeling in Python A. Alexandrov, Konstantinos Benidis, Michael Bohlke‐Schneider, Journal of Machine Learning Research EngineeringMachine LearningTemporal Pattern RecognitionComputer ScienceRecurrent Neural Network	2020	117
Elastic Machine Learning Algorithms in Amazon SageMaker Edo Liberty, Zohar Karnin, Bing Xiang, Artificial IntelligenceCluster ComputingEngineeringMachine LearningMachine Learning Tool	2020	100
Towards realistic team formation in social networks based on densest subgraphs Syama Sundar Rangapuram, Thomas Bühler, Matthias Hein Mathematical ProgrammingEngineeringDensest SubgraphsNetwork AnalysisCommunication	2013	84
Probabilistic Forecasting with Spline Quantile Function RNNs Jan Gasthaus, Konstantinos Benidis, Yuyang Wang, Intelligent ForecastingMonotonic Regression SplinesForecasting MethodologyProbabilistic ForecastingEngineering	2019	83
GluonTS: Probabilistic Time Series Models in Python A. Alexandrov, Konstantinos Benidis, Michael Bohlke‐Schneider, arXiv (Cornell University) Data AugmentationSpatiotemporal DiagnosticsMachine LearningData ScienceEngineering	2019	76
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited Matthias Hein, Simon Setzer, Leonardo Jost, arXiv (Cornell University) Graph SparsityEngineeringMachine LearningTotal VariationGraph Processing	2013	73
Neural forecasting: Introduction and literature overview. Konstantinos Benidis, Syama Sundar Rangapuram, Valentín Flunkert, arXiv (Cornell University) Forecasting MethodsForecasting MethodologyEngineeringMachine LearningData Science	2020	71
Normalizing Kalman Filters for Multivariate Time Series Analysis Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Neural Information Processing Systems State EstimationEngineeringFiltering TechniqueProcess ControlForecasting	2020	65
Constrained 1-Spectral Clustering Syama Sundar Rangapuram, Matthias Hein	2012	62

Page 1