
Better call Surrogates: A hybrid Evolutionary Algorithm for Hyperparameter optimization
In this paper, we propose a surrogateassisted evolutionary algorithm (E...
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Scaling Hamiltonian Monte Carlo Inference for Bayesian Neural Networks with Symmetric Splitting
Hamiltonian Monte Carlo (HMC) is a Markov chain Monte Carlo (MCMC) appro...
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URSABench: Comprehensive Benchmarking of Approximate Bayesian Inference Methods for Deep Neural Networks
While deep learning methods continue to improve in predictive accuracy o...
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HumBug Zooniverse: a crowdsourced acoustic mosquito dataset
Mosquitoes are the only known vector of malaria, which leads to hundreds...
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Introducing an Explicit Symplectic Integration Scheme for Riemannian Manifold Hamiltonian Monte Carlo
We introduce a recent symplectic integration scheme derived for solving ...
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An Ensemble of Bayesian Neural Networks for Exoplanetary Atmospheric Retrieval
Machine learning is now used in many areas of astrophysics, from detecti...
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Bayesian deep neural networks for lowcost neurophysiological markers of Alzheimer's disease severity
As societies around the world are ageing, the number of Alzheimer's dise...
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Bayesian Deep Learning for Exoplanet Atmospheric Retrieval
Over the past decade, the study of exoplanets has shifted from their det...
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LossCalibrated Approximate Inference in Bayesian Neural Networks
Current approaches in approximate inference for Bayesian neural networks...
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Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent d...
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Learning from lions: inferring the utility of agents from their trajectories
We build a model using Gaussian processes to infer a spatiotemporal vec...
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Adam D. Cobb
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