2 citations · 2 across the 6 of their papers we have counts for
4 papers · 1 filter
Gaussian Process Assisted Meta-learning for Image Classification and Object Detection Models
Anna R. Flowers, Christopher T. Franck, Robert B. Gramacy +1
Collecting operationally realistic data to inform machine learning models can be costly. Before collecting new data, it is helpful to understand where a model is deficient. For exa…
Monitoring the calibration of probability forecasts with an application to concept drift detection involving image classification
Christopher T. Franck, Anne R. Driscoll, Zoe Szajnfarber +1
Machine learning approaches for image classification have led to impressive advances in that field. For example, convolutional neural networks are able to achieve remarkable image…
Detecting Urban PM Hotspots with Mobile Sensing and Gaussian Process Regression
Niál Perry, Peter P. Pedersen, Charles N. Christensen +6
Low-cost mobile sensors can be used to collect PM concentration data throughout an entire city. However, identifying air pollution hotspots from the data is challenging due…
Modular Jump Gaussian Processes
Anna R. Flowers, Christopher T. Franck, Mickaël Binois +2
Gaussian processes (GPs) furnish accurate nonlinear predictions with well-calibrated uncertainty. However, the typical GP setup has a built-in stationarity assumption, making it il…