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- The University of SydneyAU198 papers
- Centre for Quantum Computation and Communication TechnologyAU120 papers
- UNSW SydneyAU97 papers
- Australian National UniversityAU88 papers
- Tsinghua UniversityCN80 papers
- Commonwealth Scientific and Industrial Research OrganisationAU75 papers
- Chinese Academy of SciencesCN74 papers
- Macquarie UniversityAU68 papers
- The University of MelbourneAU50 papers
- Data61AU43 papers
- ARC Centre of Excellence for Transformative Meta-Optical SystemsAU41 papers
- Nanyang Technological UniversitySG41 papers
214 papers · 1 filter
iFuzz-Meta: An Interpretable Fuzzy Learning Framework Bridging Top-Down and Bottom-Up Knowledge Integration
Xiaowei Jiang, Daniel Leong, Beining Cao +5
Interpretable representation learning remains a key challenge in modern neural computation, particularly when models are expected not only to perform but also to explain their reas…
Federated Weather Modeling on Sensor Data
Shengchao Chen, Guodong Long
Federated weather modeling on sensor data is a distributed system underpinned by federated learning, enabling multiple sensor data sources, including ground weather stations, satel…
Efficient Learning-based Graph Simulation for Temporal Graphs
Sheng Xiang, Chenhao Xu, Dawei Cheng +2
Graph simulation has recently received a surge of attention in graph processing and analytics. In real-life applications, e.g. social science, biology, and chemistry, many graphs a…
Generative Dynamic Graph Representation Learning for Conspiracy Spoofing Detection
Sheng Xiang, Yidong Jiang, Yunting Chen +3
Spoofing detection in financial trading is crucial, especially for identifying complex behaviors such as conspiracy spoofing. Traditional machine-learning approaches primarily focu…
Crafting Imperceptible On-Manifold Adversarial Attacks for Tabular Data
Zhipeng He, Alexander Stevens, Chun Ouyang +3
Adversarial attacks on tabular data present unique challenges due to the heterogeneous nature of mixed categorical and numerical features. Unlike images where pixel perturbations m…
h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective
Wenjian Huang, Guiping Cao, Jiahao Xia +3
Deep neural networks have demonstrated remarkable performance across numerous learning tasks but often suffer from miscalibration, resulting in unreliable probability outputs. This…