97 citations · 196 across the 22 of their papers we have counts for
9 papers · 1 filter
A Survey of TinyML Applications in Beekeeping for Hive Monitoring and Management
Willy Sucipto, Jianlong Zhou, Ray Seung Min Kwon +1
Honey bee colonies are essential for global food security and ecosystem stability, yet they face escalating threats from pests, diseases, and environmental stressors. Traditional h…
ABE: A Unified Framework for Robust and Faithful Attribution-Based Explainability
Zhiyu Zhu, Jiayu Zhang, Zhibo Jin +2
Attribution algorithms are essential for enhancing the interpretability and trustworthiness of deep learning models by identifying key features driving model decisions. Existing fr…
Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
Zhibo Jin, Jiayu Zhang, Zhiyu Zhu +4
In recent times, the swift evolution of adversarial attacks has captured widespread attention, particularly concerning their transferability and other performance attributes. These…
ACGAN-GNNExplainer: Auxiliary Conditional Generative Explainer for Graph Neural Networks
Yiqiao Li, Jianlong Zhou, Yifei Dong +2
Graph neural networks (GNNs) have proven their efficacy in a variety of real-world applications, but their underlying mechanisms remain a mystery. To address this challenge and ena…
Explaining Imitation Learning through Frames
Boyuan Zheng, Jianlong Zhou, Chunjie Liu +2
As one of the prevalent methods to achieve automation systems, Imitation Learning (IL) presents a promising performance in a wide range of domains. However, despite the considerabl…
GANExplainer: GAN-based Graph Neural Networks Explainer
Yiqiao Li, Jianlong Zhou, Boyuan Zheng +1
With the rapid deployment of graph neural networks (GNNs) based techniques into a wide range of applications such as link prediction, node classification, and graph classification…
A Survey of Explainable Graph Neural Networks: Taxonomy and Evaluation Metrics
Yiqiao Li, Jianlong Zhou, Sunny Verma +1
Graph neural networks (GNNs) have demonstrated a significant boost in prediction performance on graph data. At the same time, the predictions made by these models are often hard to…
Imitation Learning: Progress, Taxonomies and Challenges
Boyuan Zheng, Sunny Verma, Jianlong Zhou +2
Imitation learning aims to extract knowledge from human experts' demonstrations or artificially created agents in order to replicate their behaviors. Its success has been demonstra…
Facilitating Machine Learning Model Comparison and Explanation Through A Radial Visualisation
Jianlong Zhou, Weidong Huang, Fang Chen
Building an effective Machine Learning (ML) model for a data set is a difficult task involving various steps. One of the most important steps is to compare generated substantial am…