Search Results for author:
Found 10 papers, 5 papers with code
Feynman on Artificial Intelligence and Machine Learning, with Updates
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 neural networks of that time.
Physics-based machine learning for modeling stochastic IP3-dependent calcium dynamics
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions.
Diff2Dist: Learning Spectrally Distinct Edge Functions, with Applications to Cell Morphology Analysis
Because all steps involved in calculating this modified GDD are differentiable, we demonstrate that it is possible for a small neural network model to learn edge weights which minimize loss.
A dynamical system model for predicting gene expression from the epigenome
Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data.
Graph Prolongation Convolutional Networks: Explicitly Multiscale Machine Learning on Graphs with Applications to Modeling of Cytoskeleton
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 prediction.
Novel diffusion-derived distance measures for graphs
Variants of the distance metric are introduced to consider such optimized maps under sparsity constraints as well as fixed time-scaling between the two Laplacians.
Deep Learning Moment Closure Approximations using Dynamic Boltzmann Distributions
Moment closure methods are used to approximate a subset of low order moments by terminating the hierarchy at some order and replacing higher order terms with functions of lower order ones.
Multilevel Artificial Neural Network Training for Spatially Correlated Learning
Multigrid modeling algorithms are a technique used to accelerate relaxation models running on a hierarchy of similar graphlike structures.
Prospects for Declarative Mathematical Modeling of Complex Biological Systems
Based on previous work, here we define declarative modeling of complex biological systems by defining the operator algebra semantics of an increasingly powerful series of declarative modeling languages including reaction-like dynamics of parameterized and extended objects; we define semantics-preserving implementation and semantics-approximating model reduction transformations; and we outline a "meta-hierarchy" for organizing declarative models and the mathematical methods that can fruitfully manipulate them.
Learning Dynamic Boltzmann Distributions as Reduced Models of Spatial Chemical Kinetics
Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant.
Biological Physics Statistical Mechanics
