21 research outputs found
Raiders of the Lost Architecture: Kernels for Bayesian Optimization in Conditional Parameter Spaces
In practical Bayesian optimization, we must often search over structures with
differing numbers of parameters. For instance, we may wish to search over
neural network architectures with an unknown number of layers. To relate
performance data gathered for different architectures, we define a new kernel
for conditional parameter spaces that explicitly includes information about
which parameters are relevant in a given structure. We show that this kernel
improves model quality and Bayesian optimization results over several simpler
baseline kernels.Comment: 6 pages, 3 figures. Appeared in the NIPS 2013 workshop on Bayesian
optimizatio
Prediction and Fault Detection of Environmental Signals with Uncharacterised Faults
Many signals of interest are corrupted by faults of an unknown type. We propose an approach that uses Gaussian processes and a general “fault bucket ” to capture a priori uncharacterised faults, along with an approximate method for marginalising the potential faultiness of all observations. This gives rise to an efficient, flexible algorithm for the detection and automatic correction of faults. Our method is deployed in the domain of water monitoring and management, where it is able to solve several fault detection, correction, and prediction problems. The method works well despite the fact that the data is plagued with numerous difficulties, including missing observations, multiple discontinuities, nonlinearity and many unanticipated types of fault
Abstract No. 480 FOAMed in IR: designing an original podcast for future interventional radiologists
03:27 PM Abstract No. 182 Microwave ablation (MWA) in cirrhotic patients: prediction of underablation or overablation based on the energy and volume of ablation
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03:27 PM Abstract No. 182 Microwave ablation (MWA) in cirrhotic patients: prediction of underablation or overablation based on the energy and volume of ablation
Recommended from our members