| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 6995 | 2023 |
| Nats-bench: Benchmarking nas algorithms for architecture topology and size X Dong, L Liu, K Musial, B Gabrys IEEE transactions on pattern analysis and machine intelligence 44 (7), 3634-3646, 2021 | 1213* | 2021 |
| Fedproto: Federated prototype learning across heterogeneous clients Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang, C Zhang AAAI Conference on Artificial Intelligence 2022 1, 3, 2022 | 891 | 2022 |
| Free Lunch for Few-shot Learning: Distribution Calibration S Yang, L Liu, M Xu ICLR 2021, 2021 | 499 | 2021 |
| Omni-Scale CNNs: a simple and effective kernel size configuration for time series classification W Tang, G Long, L Liu, T Zhou, M Blumenstein, J Jiang International Conference on Learning Representations 2022, 2021 | 447* | 2021 |
| Federated learning from pre-trained models: A contrastive learning approach Y Tan, G Long, J Ma, L Liu, T Zhou, J Jiang NeurIPS 2022, 2022 | 308 | 2022 |
| A universal representation transformer layer for few-shot image classification L Liu, W Hamilton, G Long, J Jiang, H Larochelle ICLR 2021, 2020 | 184 | 2020 |
| Learning to Propagate for Graph Meta-Learning L Liu, T Zhou, G Long, J Jiang, C Zhang NeurIPS 2019, 2019 | 114 | 2019 |
| Attribute Propagation Network for Graph Zero-shot Learning L Liu, T Zhou, G Long, J Jiang, C Zhang AAAI 2020, 2020 | 107 | 2020 |
| Prototype Propagation Networks (PPN) for Weakly-supervised Few-shot Learning on Category Graph L Liu, T Zhou, G Long, J Jiang, L Yao, C Zhang IJCAI 2019, 2019 | 92 | 2019 |
| Fedproto: Federated prototype learning over heterogeneous devices Y Tan, G Long, L Liu, T Zhou, Q Lu, J Jiang, C Zhang arXiv preprint arXiv:2105.00243 3, 2021 | 57 | 2021 |
| Isometric Propagation Network for Generalized Zero-shot Learning L Liu, T Zhou, G Long, J Jiang, X Dong, C Zhang ICLR 2021, 2021 | 54 | 2021 |
| Interpretable time-series classification on few-shot samples W Tang, L Liu, G Long 2020 International joint conference on neural networks (IJCNN), 1-8, 2020 | 53 | 2020 |
| Many-Class Few-Shot Learning on Multi-Granularity Class Hierarchy L Liu, T Zhou, G LONG, J Jiang, C Zhang IEEE Transactions on Knowledge and Data Engineering, 1-1, 2020 | 44 | 2020 |
| Episodic memory governs choices: An RNN-based reinforcement learning model for decision-making task X Zhang, L Liu, G Long, J Jiang, S Liu Neural Networks 134, 1-10, 2021 | 30 | 2021 |
| Recognizing vector graphics without rasterization X Jiang, L Liu, C Shan, Y Shen, X Dong, D Li Advances in Neural Information Processing Systems 34, 24569-24580, 2021 | 27 | 2021 |
| Full-cycle energy consumption benchmark for low-carbon computer vision B Li, X Jiang, D Bai, Y Zhang, N Zheng, X Dong, L Liu, Y Yang, D Li arXiv preprint arXiv:2108.13465, 2021 | 14 | 2021 |
| Few-shot Time-series Classification with Dual Interpretability W Tang, L Liu, G Long ICML 2019 Time Series Workshop, 2019 | 9 | 2019 |
| Addendum to GPT-4o System Card: 4o image generation OpenAI | | 2025 |
Guodong LongAssociate Professor, Faculty of Engineering and IT, University of Technology SydneyVerified email at uts.edu.au
