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Found 57 dataset(s) matching "deep neural networks".
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<p>This dataset contains the spatiotemporal data used to train the spatiotemporal deep neural networks described in "Modeling the Spread of a Livestock Disease With Semi-Supervised Spatiotemporal...
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Air quality modeling for China. This dataset is not publicly accessible because: Data was generated and owned by Tsinghua University. It can be accessed through the following means: Data can be...
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This is a technical report for the Probabilistic Estimation of Seismic Response Using Physics Informed Recurrent Neural Networks project. The report describes the process of designing a recurrent...
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This is a presentation on the Probabilistic Estimation of Seismic Response Using Physics-Informed Recurrent Neural Networks by GTC Analytics, presented by Jesse Williams. This video slide...
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The INTEGRATE (Inverse Network Transformations for Efficient Generation of Robust Airfoil and Turbine Enhancements) project is developing a new inverse-design capability for the aerodynamic design...
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Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over...
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Machine learning can be used to predict fault properties such as shear stress, friction, and time to failure using continuous records of fault zone acoustic emissions. The files are extracted...
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Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over...
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Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over...
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Information on water depth in river channels is important for a number of applications in water resource management but can be difficult to obtain via conventional field methods, particularly over...
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The datasets contain the retrieved aerosol and ocean color properties from AirHARP measurements using the FastMAPOL algorithm. Three scenes (5Mb each) are included in the zip...
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This report describes the development of a preliminary 3D seismic velocity model at the Utah FORGE site and first results from estimating seismic resolution in the generated fracture volume during...
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The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes,...
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The overarching goal of the project is to create a highly efficient framework of machine learning (ML) methods that provide consistent and accurate real-time knowledge of system states from...
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This is data from and a final report on the development of a 3D velocity model for the larger FORGE area and on the seismic resolution in the stimulated fracture volume at the bottom of well...
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Climate change over the past century has altered vegetation community composition and species distributions across rangelands in the western United States. The scale and magnitude of climatic...
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We provide annotated fish imagery data for use in deep learning models (e.g., convolutional neural networks) for individual and species recognition. For individual recognition models, the dataset...
