23 citations · 34 across the 8 of their papers we have counts for
8 papers
Learning the shape of protein micro-environments with a holographic convolutional neural network
Michael N. Pun, Andrew Ivanov, Quinn Bellamy +5
Proteins play a central role in biology from immune recognition to brain activity. While major advances in machine learning have improved our ability to predict protein structure f…
Design of an optimal combination therapy with broadly neutralizing antibodies to suppress HIV-1
Colin LaMont, Jakub Otwinowski, Kanika Vanshylla +3
Broadly neutralizing antibodies (bNAbs) are promising targets for vaccination and therapy against HIV. Passive infusions of bNAbs have shown promise in clinical trials as a potenti…
Information-geometric optimization with natural selection
Jakub Otwinowski, Colin LaMont
Evolutionary algorithms, inspired by natural evolution, aim to optimize difficult objective functions without computing derivatives. Here we detail the relationship between populat…
On the correspondence between thermodynamics and inference
Colin H. LaMont, Paul A. Wiggins
We expand upon a natural analogy between Bayesian statistics and statistical physics in which sample size corresponds to inverse temperature. This analogy motivates the definition…
The Lindley paradox: The loss of resolution in Bayesian inference
Colin H. LaMont, Paul A. Wiggins
There are three principle paradigms of statistics: Bayesian, frequentist and information-based inference. Although these paradigms are in agreement in some contexts, the Lindley pa…
An objective prior that unifies objective Bayes and information-based inference
Colin H. LaMont, Paul A. Wiggins
There are three principle paradigms of statistical inference: (i) Bayesian, (ii) information-based and (iii) frequentist inference. We describe an objective prior (the weighting or…
Information-based inference for singular models and finite sample sizes: A frequentist information criterion
Colin H. LaMont, Paul A. Wiggins
In the information-based paradigm of inference, model selection is performed by selecting the candidate model with the best estimated predictive performance. The success of this ap…
The development of an information criterion for Change-Point Analysis
Paul A. Wiggins, Colin H. LaMont
Change-point analysis is a flexible and computationally tractable tool for the analysis of times series data from systems that transition between discrete states and whose observab…