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Found 5 papers, 2 papers with code
Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unlabeled, unannotated pathology slides
Definitive cancer diagnosis and management depend upon the extraction of information from microscopy images by pathologists.
Meta-Learning MCMC Proposals
The learned neural proposals generalize to occurrences of common structural motifs across different models, allowing for the construction of a library of learned inference primitives that can accelerate inference on unseen models with no model-specific training required.
Signal-based Bayesian Seismic Monitoring
Detecting weak seismic events from noisy sensors is a difficult perceptual task.
Parallel Chromatic MCMC with Spatial Partitioning
We introduce a novel approach for parallelizing MCMC inference in models with spatially determined conditional independence relationships, for which existing techniques exploiting graphical model structure are not applicable.
Gaussian Process Random Fields
Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications.
