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Theory-Guided Data Science: A New Paradigm for Scientific Discovery from Data
TLDR
Data science models, although successful in a number of commercial domains, have had limited applicability in scientific problems involving complex physical phenomena. Expand
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Physics-guided Neural Networks (PGNN): An Application in Lake Temperature Modeling
TLDR
We introduce a novel framework for learning data science models by using the scientific knowledge encoded in physics-based models to generate predictions using a neural network architecture. Expand
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Introduction to Data Mining (2nd Edition)
TLDR
Introduction to Data Mining, 2nd Edition, gives a comprehensive overview of the background and general themes of data mining and is designed to be useful to students, instructors, researchers, and professionals. Expand
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Spatio-Temporal Data Mining: A Survey of Problems and Methods
Large volumes of spatio-temporal data are increasingly collected and studied in diverse domains including, climate science, social sciences, neuroscience, epidemiology, transportation, mobile health,Expand
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Machine Learning for the Geosciences: Challenges and Opportunities
TLDR
Geosciences is a field of great societal relevance that requires solutions to several urgent problems facing our humanity and the planet. Expand
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Physics Guided RNNs for Modeling Dynamical Systems: A Case Study in Simulating Lake Temperature Profiles
TLDR
This paper proposes a physics-guided recurrent neural network model (PGRNN) that combines RNNs and physics-based models to leverage their complementary strengths and improve the modeling of physical processes. Expand
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Spatio-Temporal Data Mining
TLDR
We discuss different types of spatio-temporal data and the relevant data-mining questions that arise in the context of analyzing each of these datasets. Expand
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BHPMF – a hierarchical Bayesian approach to gap-filling and trait prediction for macroecology and functional biogeography
TLDR
We introduce BHPMF, a hierarchical Bayesian extension of probabilistic matrix factorization (PMF). Expand
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Incorporating Prior Domain Knowledge into Deep Neural Networks
TLDR
In recent years, the large amount of labeled data available has also helped tend research toward using minimal domain knowledge, e.g., in deep neural network research. Expand
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Monitoring Land-Cover Changes: A Machine-Learning Perspective
TLDR
Recent advances in machine learning in the detection of land-cover change using machine-learning algorithms. Expand
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