13 citations · 54 across the 17 of their papers we have counts for
17 papers
Feynman on Artificial Intelligence and Machine Learning, with Updates
Eric Mjolsness
I present my recollections of Richard Feynman's mid-1980s interest in artificial intelligence and neural networks, set in the technical context of the physics-related approaches to…
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…
A dynamical system model for predicting gene expression from the epigenome
James Brunner, Jacob Kim, Timothy Downing +2
Gene regulation is an important fundamental biological process. The regulation of gene expression is managed through a variety of methods including epigenetic processes (e.g., DNA…
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…