13 citations · 54 across the 17 of their papers we have counts for
6 papers · 1 filter
Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions. Our method estimates an effective probability distr…
Diff2Dist: Learning Spectrally Distinct Edge Functions, with Applications to Cell Morphology Analysis
Cory Braker Scott, Eric Mjolsness, Diane Oyen +3
We present a method for learning "spectrally descriptive" edge weights for graphs. We generalize a previously known distance measure on graphs (Graph Diffusion Distance), thereby a…
Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs with Applications to Modeling of Cytoskeleton
C. B. Scott, Eric Mjolsness
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…
Novel diffusion-derived distance measures for graphs
C. B. Scott, Eric Mjolsness
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 de…
Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Oliver K. Ernst, Tom Bartol, Terrence Sejnowski +1
The moments of spatial probabilistic systems are often given by an infinite hierarchy of coupled differential equations. Moment closure methods are used to approximate a subset of…
Multilevel Artificial Neural Network Training for Spatially Correlated Learning
C. B. Scott, Eric Mjolsness
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…