S
See-Kiong Ng
Researcher at National University of Singapore
Publications - 186
Citations - 5649
See-Kiong Ng is an academic researcher from National University of Singapore. The author has contributed to research in topics: Computer science & Support vector machine. The author has an hindex of 36, co-authored 150 publications receiving 4639 citations. Previous affiliations of See-Kiong Ng include Singapore University of Technology and Design & Carnegie Mellon University.
Papers
More filters
Posted Content
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
TL;DR: The proposed MAD-GAN framework considers the entire variable set concurrently to capture the latent interactions amongst the variables and is effective in reporting anomalies caused by various cyber-intrusions compared in these complex real-world systems.
Journal ArticleDOI
Computational approaches for detecting protein complexes from protein interaction networks: a survey
TL;DR: The state-of-the-art techniques for computational detection of protein complexes are reviewed, some promising research directions in this field are discussed, and experimental results with yeast protein interaction data show that the interaction subgraphs discovered by various computational methods matched well with actual protein complexes.
Journal ArticleDOI
A core-attachment based method to detect protein complexes in PPI networks
TL;DR: A novel core-attachment based method (COACH) which detects protein complexes in two stages and includes attachments into these cores to form biologically meaningful structures, which shows that COACH performs significantly better than the state-of-the-art techniques.
Book ChapterDOI
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
TL;DR: In this article, an unsupervised multivariate anomaly detection method based on Generative Adversarial Networks (GANs), using the Long Short-Term-Memory Recurrent Neural Networks (LSTM-RNN) as the base models (namely, the generator and discriminator) in the GAN framework, was proposed.
Posted Content
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
TL;DR: This work proposed a novel Generative Adversarial Networks-based Anomaly Detection (GAN-AD) method that was used to distinguish abnormal attacked situations from normal working conditions for a complex six-stage Secure Water Treatment (SWaT) system.