| 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 | 973* | 2023 |
| KAN: Kolmogorov-Arnold Networks Z Liu, Y Wang, S Vaidya, F Ruehle, J Halverson, M Soljačić, TY Hou, ... arXiv:2404.19756, 2024 | 571* | 2024 |
| Machine learning conservation laws from trajectories Z Liu, M Tegmark Physical Review Letters 126 (18), 180604, 2021 | 161 | 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 | 138 | 2022 |
| Omnigrok: Grokking Beyond Algorithmic Data Z Liu, E Michaud, M Tegmark Eleventh International Conference on Learning Representations, 2023 | 95 | 2023 |
| Poisson flow generative models Y Xu, Z Liu, M Tegmark, T Jaakkola Advances in Neural Information Processing Systems 35, 16782-16795, 2022 | 91 | 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 | 69 | 2023 |
| Machine learning hidden symmetries Z Liu, M Tegmark Physical Review Letters 128 (18), 180201, 2022 | 68 | 2022 |
| The quantization model of neural scaling E Michaud, Z Liu, G Uzay, M Tegmark Advances in Neural Information Processing Systems, 2023 | 63 | 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 | 56 | 2023 |
| Restart Sampling for Improving Generative Processes Y Xu, M Deng, X Cheng, Y Tian, Z Liu, T Jaakkola arXiv: 2306.14878, 2023 | 46 | 2023 |
| KAN 2.0: Kolmogorov-Arnold Networks Meet Science Z Liu, P Ma, Y Wang, W Matusik, M Tegmark arXiv: 2408.10205, 2024 | 40 | 2024 |
| Seeing is believing: Brain-inspired modular training for mechanistic interpretability Z Liu, E Gan, M Tegmark Entropy 26 (1), 41, 2023 | 40 | 2023 |
| Machine learning conservation laws from differential equations Z Liu, V Madhavan, M Tegmark Physical Review E 106 (4), 045307, 2022 | 39 | 2022 |
| Precision machine learning EJ Michaud, Z Liu, M Tegmark Entropy 25 (1), 175, 2023 | 37 | 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 | 32 | 2021 |
| 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 | 30 | 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 | 29 | 2019 |
| Physics-augmented learning: A new paradigm beyond physics-informed learning Z Liu, Y Chen, Y Du, M Tegmark arXiv preprint arXiv:2109.13901, 2021 | 26 | 2021 |
| Growing Brains in Recurrent Neural Networks for Multiple Cognitive Tasks Z Liu, M Khan, IR Fiete, M Tegmark NeurIPS 2023 Workshop NeurReps, 2023 | 12* | 2023 |
Max TegmarkProfessor of Physics, MITVerified email at mit.edu
