221 citations · 645 across the 25 of their papers we have counts for
8 papers · 1 filter
Fair yet Asymptotically Equal Collaborative Learning
Xiaoqiang Lin, Xinyi Xu, See-Kiong Ng +2
In collaborative learning with streaming data, nodes (e.g., organizations) jointly and continuously learn a machine learning (ML) model by sharing the latest model updates computed…
Training-Free Neural Active Learning with Initialization-Robustness Guarantees
Apivich Hemachandra, Zhongxiang Dai, Jasraj Singh +2
Existing neural active learning algorithms have aimed to optimize the predictive performance of neural networks (NNs) by selecting data for labelling. However, other than a good pr…
Data-Free Diversity-Based Ensemble Selection For One-Shot Federated Learning in Machine Learning Model Market
Naibo Wang, Wenjie Feng, Fusheng Liu +2
The emerging availability of trained machine learning models has put forward the novel concept of Machine Learning Model Market in which one can harness the collective intelligence…
ARES: Locally Adaptive Reconstruction-based Anomaly Scoring
Adam Goodge, Bryan Hooi, See Kiong Ng +1
How can we detect anomalies: that is, samples that significantly differ from a given set of high-dimensional data, such as images or sensor data? This is a practical problem with n…
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural Networks
Adam Goodge, Bryan Hooi, See Kiong Ng +1
Many well-established anomaly detection methods use the distance of a sample to those in its local neighbourhood: so-called `local outlier methods', such as LOF and DBSCAN. They ar…
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Baihong Jin, Dan Li, Seshadhri Srinivasan +3
Early detection of incipient faults is of vital importance to reducing maintenance costs, saving energy, and enhancing occupant comfort in buildings. Popular supervised learning mo…
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Dan Li, Dacheng Chen, Lei Shi +3
The prevalence of networked sensors and actuators in many real-world systems such as smart buildings, factories, power plants, and data centers generate substantial amounts of mult…
Anomaly Detection with Generative Adversarial Networks for Multivariate Time Series
Dan Li, Dacheng Chen, Jonathan Goh +1
Today's Cyber-Physical Systems (CPSs) are large, complex, and affixed with networked sensors and actuators that are targets for cyber-attacks. Conventional detection techniques are…