5 citations · 5 across the 3 of their papers we have counts for
3 papers
cs.AI2025
Towards Provably Unlearnable Examples via Bayes Error Optimisation
Ruihan Zhang, Jun Sun, Ee-Peng Lim +1
The recent success of machine learning models, especially large-scale classifiers and language models, relies heavily on training with massive data. These data are often collected…
cs.LG2024
Generative Semi-supervised Graph Anomaly Detection
Hezhe Qiao, Qingsong Wen, Xiaoli Li +2
This work considers a practical semi-supervised graph anomaly detection (GAD) scenario, where part of the nodes in a graph are known to be normal, contrasting to the extensively ex…
cs.CL2023★ 5 cited
On Exploring the Reasoning Capability of Large Language Models with Knowledge Graphs
Pei-Chi Lo, Yi-Hang Tsai, Ee-Peng Lim +1
This paper examines the capacity of LLMs to reason with knowledge graphs using their internal knowledge graph, i.e., the knowledge graph they learned during pre-training. Two resea…