| Explainable discovery of disease biomarkers: The case of ovarian cancer to illustrate the best practice in machine learning and Shapley analysis W Huang, H Suominen, T Liu, G Rice, C Salomon, AS Barnard Journal of Biomedical Informatics 141, 104365, 2023 | 14 | 2023 |
| Fast derivation of Shapley based feature importances through feature extraction methods for nanoinformatics T Liu, AS Barnard Machine Learning: Science and Technology 2 (3), 035034, 2021 | 13 | 2021 |
| Using restart heuristics to improve agent performance in angry birds T Liu, J Renz, P Zhang, M Stephenson 2019 IEEE Conference on Games (CoG), 1-8, 2019 | 4 | 2019 |
| The emergent role of explainable artificial intelligence in the materials sciences T Liu, AS Barnard Perspective, 2023 | 2 | 2023 |
| Shapley based residual decomposition for instance analysis T Liu, AS Barnard International Conference on Machine Learning, 21375-21387, 2023 | 2 | 2023 |
| Understanding the importance of individual samples and their effects on materials data using explainable artificial intelligence T Liu, ZY Tho, AS Barnard Digital Discovery 3 (2), 422-435, 2024 | 1 | 2024 |
| Dimension Reduction and Data Augmentation Methods for the Physical Sciences T Liu The Australian National University, 2021 | 1 | 2021 |
| Using Evolutionary Algorithms for Hyperparameter Tuning and Network Reduction Techniques to Classify Core Porosity Classes Based on Petrographical Descriptions T Liu, J Plested International Conference on Neural Information Processing, 750-757, 2019 | 1 | 2019 |
