V
Vipin Kumar
Researcher at University of Minnesota
Publications - 678
Citations - 67181
Vipin Kumar is an academic researcher from University of Minnesota. The author has contributed to research in topics: Parallel algorithm & Computer science. The author has an hindex of 95, co-authored 614 publications receiving 59034 citations. Previous affiliations of Vipin Kumar include University of Maryland, College Park & United States Department of the Army.
Papers
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Journal ArticleDOI
Plantation Mapping in Southeast Asia.
TL;DR: This paper studies an ensemble learning based framework for automatically mapping plantations in southern Kalimantan on a yearly scale using remote sensing data and examines the effectiveness of several components in this framework, including class aggregation, data sampling, learning model selection and post-processing.
A Data-dependency Based Intelligent Backtracking Scheme for Prolog
Vipin Kumar,Yow Lin +1 more
TL;DR: A scheme for intelligent backtracking in PROLOG programs that chooses backtrack points by doing the analysis of data dependency between literals, and is just as effective as intelligent back tracking schemes based upon (more accurate) analysis of unification failure, and yet incurs small space and time over- head.
Journal ArticleDOI
KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments
Licheng Liu,Shaoming Xu,Jinyun Tang,Kaiyu Guan,Timothy J. Griffis,M. Erickson,Alexander L. Frie,Xiaowei Jia,Tae-Gon Kim,Lee T. Miller,Bin Peng,Shaowei Wu,Yufeng Yang,Wang Zhou,Vipin Kumar,Zhenong Jin +15 more
TL;DR: A first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and which always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks.
Journal ArticleDOI
Bayesian Federated Learning: A Survey
TL;DR: Bayesian federated learning (BFL) has emerged as a promising approach to address the issues of limited and dynamic data and conditions, complexities including heterogeneities and uncertainties, and analytical explainability as discussed by the authors .
Journal ArticleDOI
Near‐term forecasts of stream temperature using deep learning and data assimilation in support of management decisions
Jacob A. Zwart,Samantha K. Oliver,William David Watkins,Jeffrey M. Sadler,Alison P. Appling,Hayley Corson-Dosch,Xiaowei Jia,Vipin Kumar,J. F. Read +8 more
TL;DR: In this paper , a process-guided DL and data assimilation (DA) approach was used to forecast the daily maximum water temperature in the Delaware River Basin in support of water management decisions.