Various contrastive learning approaches have been proposed in recent yea...
In this paper, we propose REASON, a novel framework that enables the
aut...
Personalized Federated Learning (PFL) which collaboratively trains a
fed...
During the past several years, a surge of multi-lingual Pre-trained Lang...
We target the task of cross-lingual Machine Reading Comprehension (MRC) ...
Detecting abnormal activities in real-world surveillance videos is an
im...
We present FACESEC, a framework for fine-grained robustness evaluation o...
We present a contrasting learning approach with data augmentation techni...
Forecasting on sparse multivariate time series (MTS) aims to model the
p...
Graph Neural Networks (GNNs) have shown to be powerful tools for graph
a...
Accurate air turbulence forecasting can help airlines avoid hazardous
tu...
Discrete event sequences are ubiquitous, such as an ordered event series...
Detecting anomalies in dynamic graphs is a vital task, with numerous
pra...
Recently, recommender systems have been able to emit substantially impro...
Information systems have widely been the target of malware attacks.
Trad...
Graph neural network (GNN), as a powerful representation learning model ...
Program or process is an integral part of almost every IT/OT system. Can...
Nowadays, multivariate time series data are increasingly collected in va...