Graph clustering, a fundamental and challenging task in graph mining, aims to classify
nodes in a graph into several disjoint clusters. In recent years, graph contrastive learning …

Masked Image Modeling (MIM) methods, like Masked Autoencoders (MAE), efficiently learn
a rich representation of the input. However, for adapting to downstream tasks, they require a …

Self-supervised learning (SSL) has recently received significant attention due to its ability to
train high-performance encoders purely on unlabeled data-often scraped from the internet …

Despite the empirical success and practical significance of (relational) knowledge distillation
that matches (the relations of) features between teacher and student models, the …

Spatially resolved transcriptomics data are being used in a revolutionary way to decipher the
spatial pattern of gene expression and the spatial architecture of cell types. Much work has …

Most deep learning-based time series clustering models concentrate on data representation
in a separate process from clustering. This leads to that clustering loss cannot guide feature …

* Boundaries* are the timestamps at which a class in a time series changes. Recently,
representation-based boundary detection has gained popularity, but its emphasis on …

Contrastive representation learning is a modern paradigm for learning representations of
unlabeled data via augmentations--precisely, contrastive models learn to embed …

Contrastive learning is an efficient approach to self-supervised representation learning.
Although recent studies have made progress in the theoretical understanding of contrastive …

Positive Unlabeled (PU) learning refers to the task of learning a binary classifier given a few
labeled positive samples, and a set of unlabeled samples (which could be positive or …