Výsledky vyhledávání - "Scott, C. B."

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Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs with Applications to Modeling of Cytoskeleton

Autor: Scott, C. B., Mjolsness, Eric

We define a novel type of ensemble Graph Convolutional Network (GCN) model. Using optimized linear projection operators to map between spatial scales of graph, this ensemble model learns to aggregate information from each scale for its final predicti

Externí odkaz: http://arxiv.org/abs/2002.05842

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Novel diffusion-derived distance measures for graphs

Autor: Scott, C. B., Mjolsness, Eric

We define a new family of similarity and distance measures on graphs, and explore their theoretical properties in comparison to conventional distance metrics. These measures are defined by the solution(s) to an optimization problem which attempts fin

Externí odkaz: http://arxiv.org/abs/1909.04203

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Multilevel Artificial Neural Network Training for Spatially Correlated Learning

Autor: Scott, C. B., Mjolsness, Eric

Multigrid modeling algorithms are a technique used to accelerate relaxation models running on a hierarchy of similar graphlike structures. We introduce and demonstrate a new method for training neural networks which uses multilevel methods. Using an

Externí odkaz: http://arxiv.org/abs/1806.05703

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Algebraic properties of generalized Rijndael-like ciphers

Autor: Babinkostova, L., Bombardier, K. W., Cole, M. M., Morrell, T. A., Scott, C. B.

We provide conditions under which the set of Rijndael functions considered as permutations of the state space and based on operations of the finite field $\GF (p^k)$ ($p\geq 2$ a prime number) is not closed under functional composition. These conditi

Externí odkaz: http://arxiv.org/abs/1210.7942

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