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Found 9 papers, 0 papers with code
SE-shapelets: Semi-supervised Clustering of Time Series Using Representative Shapelets
Shapelets that discriminate time series using local features (subsequences) are promising for time series clustering.
Hybrid Variational Autoencoder for Time Series Forecasting
Variational autoencoders (VAE) are powerful generative models that learn the latent representations of input data as random variables.
Transferable Graph Backdoor Attack
In this paper, we disclose the TRAP attack, a Transferable GRAPh backdoor attack.
Fake News Quick Detection on Dynamic Heterogeneous Information Networks
The spread of fake news has caused great harm to society in recent years.
Sentiment Analysis and Topic Modeling for COVID-19 Vaccine Discussions
The outbreak of the novel Coronavirus Disease 2019 (COVID-19) has lasted for nearly two years and caused unprecedented impacts on people's daily life around the world.
Representation Learning for Short Text Clustering
Effective representation learning is critical for short text clustering due to the sparse, high-dimensional and noise attributes of short text corpus.
Variational Co-embedding Learning for Attributed Network Clustering
Recent works for attributed network clustering utilize graph convolution to obtain node embeddings and simultaneously perform clustering assignments on the embedding space.
Detecting Topic and Sentiment Dynamics Due to COVID-19 Pandemic Using Social Media
The large scale social media posts (e. g., tweets) provide an ideal data source to infer the mental health for people during this pandemic period.
Examination of Community Sentiment Dynamics due to COVID-19 Pandemic: A Case Study from A State in Australia
In this paper, we exploit the massive text data posted by Twitter users to analyse the sentiment dynamics of people living in the state of New South Wales (NSW) in Australia during the pandemic period.
