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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...
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05/15/2023
Neural Boltzmann Machines
Conditional generative models are capable of using contextual informatio...
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11/03/2022
Can RBMs be trained with zero step contrastive divergence?
Restricted Boltzmann Machines (RBMs) are probabilistic generative models...
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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...
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02/04/2020
Generating Digital Twins with Multiple Sclerosis Using Probabilistic Neural Networks
Multiple Sclerosis (MS) is a neurodegenerative disorder characterized by...
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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...
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04/23/2018
Boltzmann Encoded Adversarial Machines
Restricted Boltzmann Machines (RBMs) are a class of generative neural ne...
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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...
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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...
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09/24/2014
Variational Pseudolikelihood for Regularized Ising Inference
I propose a variational approach to maximum pseudolikelihood inference o...
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07/30/2014