1.3k citations · 1.5k across the 6 of their papers we have counts for
6 papers
-VAE: Curvature regularized variational autoencoders for uncovering emergent low dimensional geometric structure in high dimensional data
Jason Z. Kim, Nicolas Perrin-Gilbert, Erkan Narmanli +5
Natural systems with emergent behaviors often organize along low-dimensional subsets of high-dimensional spaces. For example, despite the tens of thousands of genes in the human ge…
Extracting falsifiable predictions from sloppy models
Ryan N. Gutenkunst, Fergal P. Casey, Joshua J. Waterfall +2
Successful predictions are among the most compelling validations of any model. Extracting falsifiable predictions from nonlinear multiparameter models is complicated by the fact th…
Universally Sloppy Parameter Sensitivities in Systems Biology
Ryan N. Gutenkunst, Joshua J. Waterfall, Fergal P. Casey +3
Quantitative computational models play an increasingly important role in modern biology. Such models typically involve many free parameters, and assigning their values is often a s…
Optimal experimental design in an EGFR signaling and down-regulation model
Fergal P. Casey, Dan Baird, Qiyu Feng +6
We apply the methods of optimal experimental design to a differential equation model for epidermal growth factor receptor (EGFR) signaling, trafficking, and down-regulation. The mo…
Variational method for estimating the rate of convergence of Markov Chain Monte Carlo algorithms
Fergal P. Casey, Joshua J. Waterfall, Ryan N. Gutenkunst +2
We demonstrate the use of a variational method to determine a quantitative lower bound on the rate of convergence of Markov Chain Monte Carlo (MCMC) algorithms as a function of the…
The sloppy model universality class and the Vandermonde matrix
Joshua J. Waterfall, Fergal P. Casey, Ryan N. Gutenkunst +5
In a variety of contexts, physicists study complex, nonlinear models with many unknown or tunable parameters to explain experimental data. We explain why such systems so often are…