| Model-based deep reinforcement learning for dynamic portfolio optimization P Yu, JS Lee, I Kulyatin, Z Shi, S Dasgupta arXiv preprint arXiv:1901.08740, 2019 | 101 | 2019 |
| Hutchinson trace estimation for high-dimensional and high-order physics-informed neural networks Z Hu, Z Shi, GE Karniadakis, K Kawaguchi Computer Methods in Applied Mechanics and Engineering 424, 116883, 2024 | 16 | 2024 |
| Model-based deep reinforcement learning for financial portfolio optimization P Yu, JS Lee, I Kulyatin, Z Shi, S Dasgupta RWSDM Workshop, ICML 1, 2019, 2019 | 12 | 2019 |
| Neural Integral Functionals Z Hu, T Li, Z Shi, K Zheng, G Vignale, K Kawaguchi, YAN Shuicheng, ... ICLR 2023 Workshop on Physics for Machine Learning, 2023 | 1 | 2023 |
| Diagonalization without Diagonalization: A Direct Optimization Approach for Solid-State Density Functional Theory T Li, M Lin, S Dale, Z Shi, AH Neto, KS Novoselov, G Vignale arXiv preprint arXiv:2411.05033, 2024 | | 2024 |
| Jrystal: A JAX-based Differentiable Density Functional Theory Framework for Materials T Li, Z Shi, SG Dale, G Vignale, M Lin | | |
| Amortized Eigendecomposition for Neural Networks T Li, Z Shi, J Zhao, M Lin The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | | |
| Stochastic Taylor Derivative Estimator: Efficient amortization for arbitrary differential operators Z Shi, Z Hu, M Lin, K Kawaguchi The Thirty-eighth Annual Conference on Neural Information Processing Systems, 0 | | |
| Differentiable Optimization in Plane-Wave Density Functional Theory for Solid States T Li, SG Dale, Z Shi, J Li, G Vignale, AHC Neto, KS Novoselov, M Lin | | |
| Generalization in Neural Operator: Irregular Domains, Orthogonal Basis, and Super-Resolution Z Hu, Z Hao, T Li, Z Shi, K Kawaguchi, M Lin | | |
Pengqian YuIBM Research在 ibm.com 的电子邮件经过验证
