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Gradient-based active learning for intelligent discovery of colloidal phase diagrams

Accurately mapping colloidal phase diagrams using molecular dynamics (MD) is computationally expensive, particularly at sharp phase boundaries, so we propose an active learning …

Sumeet Vadhavkar
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On the interdependence between data selection and architecture optimization in deep active learning featured image

On the interdependence between data selection and architecture optimization in deep active learning

Deep active learning (DAL) studies the optimal selection of labeled data for training deep neural networks (DNNs). While data selection in traditional active learning is mostly …

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Pradeep Bajracharya
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Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning featured image

Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning

We present a Bayesian active learning method to directly approximate the posterior pdf function of cardiac model parameters, in which we intelligently select training points to …

Md Shakil Zaman
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Embedding High-dimensional Bayesian Optimization via Generative Modeling: Parameter Personalization of Cardiac Electrophysiological Models featured image

Embedding High-dimensional Bayesian Optimization via Generative Modeling: Parameter Personalization of Cardiac Electrophysiological Models

We present a novel concept that uses a generative variational auto-encoder (VAE) to embed HD Bayesian optimization into a low-dimensional (LD) latent space that represents the …

Jwala Dhamala
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