| Deep graph library: A graph-centric, highly-performant package for graph neural networks M Wang, D Zheng, Z Ye, Q Gan, M Li, X Song, J Zhou, C Ma, L Yu, Y Gai, ... arXiv preprint arXiv:1909.01315, 2019 | 1156 | 2019 |
| Deep graph library: Towards efficient and scalable deep learning on graphs MY Wang ICLR workshop on representation learning on graphs and manifolds, 2019 | 707 | 2019 |
| A statistical framework for low-bitwidth training of deep neural networks J Chen, Y Gai, Z Yao, MW Mahoney, JE Gonzalez Advances in neural information processing systems 33, 883-894, 2020 | 27 | 2020 |
| Loss functions for multiset prediction S Welleck, Z Yao, Y Gai, J Mao, Z Zhang, K Cho Advances in Neural Information Processing Systems 31, 2018 | 19 | 2018 |
| Blockchain large language models Y Gai, L Zhou, K Qin, D Song, A Gervais arXiv preprint arXiv:2304.12749, 2023 | 17 | 2023 |
| Grounded graph decoding improves compositional generalization in question answering Y Gai, P Jain, W Zhang, JE Gonzalez, D Song, I Stoica arXiv preprint arXiv:2111.03642, 2021 | 8 | 2021 |
| Practical convex formulation of robust one-hidden-layer neural network training Y Bai, T Gautam, Y Gai, S Sojoudi arXiv preprint arXiv:2105.12237, 2021 | 8 | 2021 |
| Deep graph library, 2018 M Wang, L Yu, Q Gan, D Zheng, Y Gai, Z Ye, M Li, J Zhou, Q Huang, ... URL http://dgl. ai, 0 | 7 | |
| KnowHalu: Hallucination Detection via Multi-Form Knowledge Based Factual Checking J Zhang, C Xu, Y Gai, F Lecue, D Song, B Li arXiv preprint arXiv:2404.02935, 2024 | | 2024 |
| Practical convex formulations of one-hidden-layer neural network adversarial training Y Bai, T Gautam, Y Gai, S Sojoudi 2022 American Control Conference (ACC), 1535-1542, 2022 | | 2022 |
| Gradient-based learning for F-measure and other performance metrics Y Gai, Z Zhang, K Cho | | 2018 |
| Convex Formulation of Robust Two-layer Neural Network Training Y Bai, T Gautam, Y Gai, S Sojoudi | | |
