Ensemble clustering, also called consensus clustering, has
been attracting much attention in recent years, aiming to combine multiple base clustering algorithms into a better and more consensus clustering. Due to its good performance, ensemble clustering plays a vital role in many research areas, such as community detection and bioinformatics.

Automated graph learning is a method that aims at discovering the best hyper-parameter and neural architecture configuration for different graph tasks/data without manual design.

AutoGL

An adaptive graph learning method for automated molecular interactions and properties predictions

Ensemble Clustering

Ensemble Learning for Spectral Clustering

**Noise2Fast** is a model for single image blind denoising. It is similar to masking based methods -- filling in the pixel gaps -- in that the network is blind to many of the input pixels during training. The method is inspired by Neighbor2Neighbor, where the neural network learns a mapping between adjacent pixels. Noise2Fast is tuned to speed by using a discrete four image training set obtained by a form of downsampling called “checkerboard downsampling.

Source	Ensemble Learning for Spectral Clustering
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com