's likelihood kernel that bounds the loss of information based on information-geometric

The stochastic block model (SBM) is a fundamental model for studying graph clustering or community detection in networks. It has received great attention in the last decade and the balanced case, i.e., assuming all clusters have large

Nevertheless, the use of these techniques at a large scale can be challenging and expensive. Satellite data have been widely used as a valuable tool in estimating biomass on a global scale.

We provide our source codes to ensure the reproducibility of our experimental results. Below we summarize several critical aspects w.r .tthe The datasets we used are all publicly accessible, which is introduced in Appendix E.1. For long-tailed subsets, we strictly follows previous work [29] on CIFAR-100-L T to avoid the bias attribute to the sampling randomness. On ImageNet-L T and Places-L T, we employ the widely-used data split first introduced in [44]. All the experiments are conducted on NVIDIA GeForce RTX 3090 with Python 3.7 and Pytorch 1.7.