Charles K. Fisher | DeepAI

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Featured Co-authors

David J. Schwab
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∙ 08/09/2023

A Rule of Thumb for the Power Gain due to Covariate Adjustment in Randomized Controlled Trials with Continuous Outcomes

Randomized Controlled Trials (RCTs) often adjust for baseline covariates...

0 Charles K. Fisher, et al. ∙

∙ 05/15/2023

Neural Boltzmann Machines

Conditional generative models are capable of using contextual informatio...

0 Alex H. Lang, et al. ∙

∙ 11/03/2022

Can RBMs be trained with zero step contrastive divergence?

Restricted Boltzmann Machines (RBMs) are probabilistic generative models...

0 Charles K. Fisher, et al. ∙

∙ 12/24/2020

Modeling Disease Progression in Mild Cognitive Impairment and Alzheimer's Disease with Digital Twins

Alzheimer's Disease (AD) is a neurodegenerative disease that affects sub...

0 Daniele Bertolini, et al. ∙

∙ 02/04/2020

Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks

Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by...

0 Jonathan R. Walsh, et al. ∙

∙ 07/10/2018

Using deep learning for comprehensive, personalized forecasting of Alzheimer's Disease progression

A patient is more than one number, yet most approaches to machine learni...

4 Charles K. Fisher, et al. ∙

∙ 04/23/2018

Boltzmann Encoded Adversarial Machines

Restricted Boltzmann Machines (RBMs) are a class of generative neural ne...

0 Charles K. Fisher, et al. ∙

∙ 03/23/2018

A high-bias, low-variance introduction to Machine Learning for physicists

Machine Learning (ML) is one of the most exciting and dynamic areas of m...

2 Pankaj Mehta, et al. ∙

∙ 11/03/2014

Bayesian feature selection with strongly-regularizing priors maps to the Ising Model

Identifying small subsets of features that are relevant for prediction a...

0 Charles K. Fisher, et al. ∙

∙ 09/24/2014

Variational Pseudolikelihood for Regularized Ising Inference

I propose a variational approach to maximum pseudolikelihood inference o...

0 Charles K. Fisher, et al. ∙

∙ 07/30/2014

Fast Bayesian Feature Selection for High Dimensional Linear Regression in Genomics via the Ising Approximation

Feature selection, identifying a subset of variables that are relevant f...

0 Charles K. Fisher, et al. ∙

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