How might one "reduce" a graph?

First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. This paper develops a model for multifurcating trees with edge lengths and observed data at the tree leaves; the model is based on the beta coalescent from the probability literature. The authors develop an MCMC inference scheme for their model, in which they draw on existing work that uses belief propagation to perform inference for the Kingman coalescent (an edge case of the beta coalescent in which all trees are binary). The particular challenge for inference here is that there are many more possible parent-child node relationships when parents can have multiple children (not just two). The authors seem to use a Dirichlet Process mixture model (DPMM) at each node to narrow down the space of possible children subsets to consider. As the authors note, even inference with the Kingman coalescent is a hard problem. In experiments, they compare to the Kingman coalescent and hierarchical agglomerative clustering. The Kingman coalescent is a popular modeling tool, so it is great to see a practical extension of the Kingman coalescent to the multifurcating case being explored for inference.

Our goal is to provide efficient algorithms to discover bushy hierarchies.

Neural Information Processing Systems http://nips.cc/

Dapeng Chen is the corresponding author. The initial learning rate is set to 0.00035 and is decreased to 1/10 of its previous value every 20 epochs in the total 50 epochs. Table 7, significant 4.8% mAP improvements can be observed when applying the self-paced learning What is interesting is that the final performance is even better than that on DBSCAN. Experiments are conducted on the tasks of unsupervised person re-ID. Market-1501, and the chosen hyper-parameters are directly applied to all the other tasks.

Neural Information Processing Systems http://nips.cc/

Work done as an intern and student researcher at Google Brain.