no code implementations • 31 Aug 2022 • 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 neural networks of that time.
1 code implementation • 10 Sep 2021 • Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, Eric Mjolsness
We present a machine learning method for model reduction which incorporates domain-specific physics through candidate functions.
no code implementations • 29 Jun 2021 • Cory Braker Scott, Eric Mjolsness, Diane Oyen, Chie Kodera, David Bouchez, Magalie Uyttewaal
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.
1 code implementation • 3 Aug 2020 • James Brunner, Jacob Kim, Timothy Downing, Eric Mjolsness, Kord M. Kober
Despite active research, studies to date have focused on using statistical models to predict gene expression from methylation data.
no code implementations • 14 Feb 2020 • C. B. Scott, Eric Mjolsness
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.
no code implementations • 10 Sep 2019 • C. B. Scott, Eric Mjolsness
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.
1 code implementation • 28 May 2019 • Oliver K. Ernst, Tom Bartol, Terrence Sejnowski, Eric Mjolsness
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.
1 code implementation • 14 Jun 2018 • C. B. Scott, Eric Mjolsness
Multigrid modeling algorithms are a technique used to accelerate relaxation models running on a hierarchy of similar graphlike structures.
no code implementations • 30 Apr 2018 • Eric Mjolsness
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.
1 code implementation • 2 Mar 2018 • Oliver K. Ernst, Thomas Bartol, Terrence Sejnowski, Eric Mjolsness
Finding reduced models of spatially-distributed chemical reaction networks requires an estimation of which effective dynamics are relevant.
Biological Physics Statistical Mechanics