8,280 research outputs found
Efficient Bayesian Nonparametric Modelling of Structured Point Processes
This paper presents a Bayesian generative model for dependent Cox point
processes, alongside an efficient inference scheme which scales as if the point
processes were modelled independently. We can handle missing data naturally,
infer latent structure, and cope with large numbers of observed processes. A
further novel contribution enables the model to work effectively in higher
dimensional spaces. Using this method, we achieve vastly improved predictive
performance on both 2D and 1D real data, validating our structured approach.Comment: Presented at UAI 2014. Bibtex: @inproceedings{structcoxpp14_UAI,
Author = {Tom Gunter and Chris Lloyd and Michael A. Osborne and Stephen J.
Roberts}, Title = {Efficient Bayesian Nonparametric Modelling of Structured
Point Processes}, Booktitle = {Uncertainty in Artificial Intelligence (UAI)},
Year = {2014}
Becoming the Expert - Interactive Multi-Class Machine Teaching
Compared to machines, humans are extremely good at classifying images into
categories, especially when they possess prior knowledge of the categories at
hand. If this prior information is not available, supervision in the form of
teaching images is required. To learn categories more quickly, people should
see important and representative images first, followed by less important
images later - or not at all. However, image-importance is individual-specific,
i.e. a teaching image is important to a student if it changes their overall
ability to discriminate between classes. Further, students keep learning, so
while image-importance depends on their current knowledge, it also varies with
time.
In this work we propose an Interactive Machine Teaching algorithm that
enables a computer to teach challenging visual concepts to a human. Our
adaptive algorithm chooses, online, which labeled images from a teaching set
should be shown to the student as they learn. We show that a teaching strategy
that probabilistically models the student's ability and progress, based on
their correct and incorrect answers, produces better 'experts'. We present
results using real human participants across several varied and challenging
real-world datasets.Comment: CVPR 201
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