| Convergence analysis of distributed stochastic gradient descent with shuffling Q Meng, W Chen, Y Wang, ZM Ma, TY Liu Neurocomputing 337, 46-57, 2019 | 149 | 2019 |
| Target transfer Q-learning and its convergence analysis Y Wang, Y Liu, W Chen, ZM Ma, TY Liu Neurocomputing 392, 11-22, 2020 | 57 | 2020 |
| Finite sample analysis of the GTD policy evaluation algorithms in Markov setting Y Wang, W Chen, Y Liu, ZM Ma, TY Liu Advances in Neural Information Processing Systems 30, 2017 | 46 | 2017 |
| The impact of large language models on scientific discovery: a preliminary study using gpt-4 MR AI4Science, MA Quantum arXiv preprint arXiv:2311.07361, 2023 | 28 | 2023 |
| Multi-scale analysis of schizophrenia risk genes, brain structure, and clinical symptoms reveals integrative clues for subtyping schizophrenia patients L Ma, ET Rolls, X Liu, Y Liu, Z Jiao, Y Wang, W Gong, Z Ma, F Gong, ... Journal of molecular cell biology 11 (8), 678-687, 2019 | 14 | 2019 |
| Gradient information matters in policy optimization by back-propagating through model C Li, Y Wang, W Chen, Y Liu, ZM Ma, TY Liu International Conference on Learning Representations, 2022 | 11 | 2022 |
| Generalization error bounds for optimization algorithms via stability Q Meng, Y Wang, W Chen, T Wang, ZM Ma, TY Liu Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 9 | 2017 |
| NeuralStagger: accelerating physics-constrained neural PDE solver with spatial-temporal decomposition X Huang, W Shi, Q Meng, Y Wang, X Gao, J Zhang, TY Liu International Conference on Machine Learning, 13993-14006, 2023 | 8 | 2023 |
| DRVN (deep random vortex network): A new physics-informed machine learning method for simulating and inferring incompressible fluid flows R Zhang, P Hu, Q Meng, Y Wang, R Zhu, B Chen, ZM Ma, TY Liu Physics of Fluids 34 (10), 2022 | 7 | 2022 |
| Neural operator with regularity structure for modeling dynamics driven by spdes P Hu, Q Meng, B Chen, S Gong, Y Wang, W Chen, R Zhu, ZM Ma, TY Liu arXiv preprint arXiv:2204.06255, 2022 | 7 | 2022 |
| The scale-invariant space for attention layer in neural network Y Wang, Y Liu, ZM Ma Neurocomputing 392, 1-10, 2020 | 5 | 2020 |
| How to control hydrodynamic force on fluidic pinball via deep reinforcement learning H Feng, Y Wang, H Xiang, Z Jin, D Fan Physics of Fluids 35 (4), 2023 | 4 | 2023 |
| Making Better Decision by Directly Planning in Continuous Control J Zhu, Y Wang, L Wu, T Qin, W Zhou, TY Liu, H Li International Conference on Learning Representations, 2023 | 3 | 2023 |
| Deep latent regularity network for modeling stochastic partial differential equations S Gong, P Hu, Q Meng, Y Wang, R Zhu, B Chen, Z Ma, H Ni, TY Liu Proceedings of the AAAI Conference on Artificial Intelligence 37 (6), 7740-7747, 2023 | 2 | 2023 |
| Incorporating NODE with pre-trained neural differential operator for learning dynamics S Gong, Q Meng, Y Wang, L Wu, W Chen, Z Ma, TY Liu Neurocomputing 528, 48-58, 2023 | 2 | 2023 |
| Better Neural PDE Solvers Through Data-Free Mesh Movers P Hu, Y Wang, ZM Ma arXiv preprint arXiv:2312.05583, 2023 | 1 | 2023 |
| Complex-valued neural-operator-assisted soliton identification M Zhang, Q Meng, D Zhang, Y Wang, G Wang, Z Ma, L Chen, TY Liu Physical Review E 108 (2), 025305, 2023 | | 2023 |
| How to Select the Appropriate One from the Trained Models for Model-Based OPE C Li, Y Wang, ZM Ma, Y Liu CAAI International Conference on Artificial Intelligence, 285-297, 2023 | | 2023 |
| Constructing the Basis Path Set by Eliminating the Path Dependency J Zhu, Q Meng, W Chen, Y Wang, Z Ma Journal of Systems Science and Complexity 35 (5), 1944-1962, 2022 | | 2022 |
| Deep Random Vortex Method for Simulation and Inference of Navier-Stokes Equations R Zhang, P Hu, Q Meng, Y Wang, R Zhu, B Chen, ZM Ma, TY Liu arXiv preprint arXiv:2206.09571, 2022 | | 2022 |
