Scientific discovery in the age of artificial intelligence H Wang, T Fu, Y Du, W Gao, K Huang, Z Liu, P Chandak, S Liu, ... Nature 620 (7972), 47-60, 2023 | 476* | 2023 |
Machine learning conservation laws from trajectories Z Liu, M Tegmark Physical Review Letters 126 (18), 180604, 2021 | 136 | 2021 |
Towards understanding grokking: An effective theory of representation learning Z Liu, O Kitouni, NS Nolte, E Michaud, M Tegmark, M Williams Advances in Neural Information Processing Systems 35, 34651-34663, 2022 | 89 | 2022 |
Poisson flow generative models Y Xu, Z Liu, M Tegmark, T Jaakkola Advances in Neural Information Processing Systems 35, 16782-16795, 2022 | 65 | 2022 |
Omnigrok: Grokking Beyond Algorithmic Data Z Liu, E Michaud, M Tegmark Eleventh International Conference on Learning Representations, 2023 | 64 | 2023 |
KAN: Kolmogorov-Arnold Networks Z Liu, Y Wang, S Vaidya, F Ruehle, J Halverson, M Soljačić, TY Hou, ... arXiv:2404.19756, 2024 | 51 | 2024 |
Machine learning hidden symmetries Z Liu, M Tegmark Physical Review Letters 128 (18), 180201, 2022 | 51 | 2022 |
The quantization model of neural scaling E Michaud, Z Liu, G Uzay, M Tegmark Advances in Neural Information Processing Systems, 2023 | 37 | 2023 |
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models Y Xu, Z Liu, Y Tian, T Shangyuan, M Tegmark, T Jaakkola Fortieth International Conference on Machine Learning, 2023 | 35 | 2023 |
Machine learning conservation laws from differential equations Z Liu, V Madhavan, M Tegmark Physical Review E 106 (4), 045307, 2022 | 34 | 2022 |
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks Z Zhong, Z Liu, M Tegmark, J Andreas Thirty-seventh Conference on Neural Information Processing Systems, 2023 | 33 | 2023 |
Applications of deep learning to relativistic hydrodynamics H Huang, B Xiao, Z Liu, Z Wu, Y Mu, H Song Physical Review Research 3 (2), 023256, 2021 | 28 | 2021 |
Principal component analysis of collective flow in relativistic heavy-ion collisions Z Liu, W Zhao, H Song The European Physical Journal C 79 (10), 870, 2019 | 28 | 2019 |
Machine-learning nonconservative dynamics for new-physics detection Z Liu, B Wang, Q Meng, W Chen, M Tegmark, TY Liu Physical Review E 104 (5), 055302, 2021 | 26 | 2021 |
Restart Sampling for Improving Generative Processes Y Xu, M Deng, X Cheng, Y Tian, Z Liu, T Jaakkola arXiv: 2306.14878, 2023 | 25 | 2023 |
Physics-augmented learning: A new paradigm beyond physics-informed learning Z Liu, Y Chen, Y Du, M Tegmark arXiv preprint arXiv:2109.13901, 2021 | 24 | 2021 |
Seeing is believing: Brain-inspired modular training for mechanistic interpretability Z Liu, E Gan, M Tegmark Entropy 26 (1), 41, 2023 | 23 | 2023 |
Precision machine learning EJ Michaud, Z Liu, M Tegmark Entropy 25 (1), 175, 2023 | 19 | 2023 |
Quantum-inspired hamiltonian monte carlo for bayesian sampling Z Liu, Z Zhang arXiv preprint arXiv:1912.01937, 2019 | 10 | 2019 |
Genphys: From physical processes to generative models Z Liu, D Luo, Y Xu, T Jaakkola, M Tegmark arXiv preprint arXiv:2304.02637, 2023 | 9 | 2023 |