20 citations · 25 across the 6 of their papers we have counts for
6 papers
Knots in random neural networks
Kevin K. Chen, Anthony C. Gamst, Alden K. Walker
The weights of a neural network are typically initialized at random, and one can think of the functions produced by such a network as having been generated by a prior over some fun…
Estimating the Operating Characteristics of Ensemble Methods
Anthony Gamst, Jay-Calvin Reyes, Alden Walker
In this paper we present a technique for using the bootstrap to estimate the operating characteristics and their variability for certain types of ensemble methods. Bootstrapping a…
The energy landscape of a simple neural network
Anthony Collins Gamst, Alden Walker
We explore the energy landscape of a simple neural network. In particular, we expand upon previous work demonstrating that the empirical complexity of fitted neural networks is vas…
The empirical size of trained neural networks
Kevin K. Chen, Anthony Gamst, Alden Walker
ReLU neural networks define piecewise linear functions of their inputs. However, initializing and training a neural network is very different from fitting a linear spline. In this…
Regression of ranked responses when raw responses are censored
Michael C. Donohue, Anthony C. Gamst, Robert A. Rissman +1
We discuss semiparametric regression when only the ranks of responses are observed. The model is , where is the unobs…
The Bayesian Analysis of Complex, High-Dimensional Models: Can It Be CODA?
Y. Ritov, P. J. Bickel, A. C. Gamst +1
We consider the Bayesian analysis of a few complex, high-dimensional models and show that intuitive priors, which are not tailored to the fine details of the model and the estimate…