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Found 7 papers, 0 papers with code
Learning to fail: Predicting fracture evolution in brittle material models using recurrent graph convolutional neural networks
Our methods use deep learning and train on simulation data from high-fidelity models, emulating the results of these models while avoiding the overwhelming computational demands associated with running a statistically significant sample of simulations.
Unsupervised vehicle recognition using incremental reseeding of acoustic signatures
Vehicle recognition and classification have broad applications, ranging from traffic flow management to military target identification.
Dimensionality reduction for acoustic vehicle classification with spectral embedding
We propose a method for recognizing moving vehicles, using data from roadside audio sensors.
Machine learning for graph-based representations of three-dimensional discrete fracture networks
Restricting the flowing fracture network to this backbone provides a significant reduction in the network's effective size.
Improving Image Clustering using Sparse Text and the Wisdom of the Crowds
We propose a method to improve image clustering using sparse text and the wisdom of the crowds.
Multiclass Semi-Supervised Learning on Graphs using Ginzburg-Landau Functional Minimization
We present a graph-based variational algorithm for classification of high-dimensional data, generalizing the binary diffuse interface model to the case of multiple classes.
Spectral Clustering with Epidemic Diffusion
Existing methods for spectral clustering use the eigenvalues and eigenvectors of the graph Laplacian, an operator that is closely associated with random walks on graphs.
