Statistical Learning
Model-Based Relative Entropy Stochastic Search
Abbas Abdolmaleki, Rudolf Lioutikov, Jan R. Peters, Nuno Lau, Luis Pualo Reis, Gerhard Neumann
Neural Information Processing Systems http://nips.cc/
Automatic Variational Inference in Stan
Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David Blei
Variational inference is a scalable technique for approximate Bayesian inference. Deriving variational inference algorithms requires tedious model-specific calculations; this makes it difficult for non-experts to use.
PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph
Yikang LI, Tao Ma, Yeqi Bai, Nan Duan, Sining Wei, Xiaogang Wang
Neural Information Processing Systems http://nips.cc/
Learning metrics for persistence-based summaries and applications for graph classification
A series of methods have been developed to map a persistence diagram to a vector representation so as to facilitate the downstream use of machine learning tools.
Planar Ultrametrics for Image Segmentation
Julian E. Yarkony, Charless Fowlkes
We study the problem of hierarchical clustering on planar graphs. We formulate this in terms of finding the closest ultrametric to a specified set of distances and solve it using an LP relaxation that leverages minimum cost perfect matching as a subroutine to efficiently explore the space of planar partitions. We apply our algorithm to the problem of hierarchical image segmentation.