Ziming Liu
Cited by
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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
Machine learning conservation laws from trajectories
Z Liu, M Tegmark
Physical Review Letters 126 (18), 180604, 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
Poisson flow generative models
Y Xu, Z Liu, M Tegmark, T Jaakkola
Advances in Neural Information Processing Systems 35, 16782-16795, 2022
Omnigrok: Grokking Beyond Algorithmic Data
Z Liu, E Michaud, M Tegmark
Eleventh International Conference on Learning Representations, 2023
Machine learning hidden symmetries
Z Liu, M Tegmark
Physical Review Letters 128 (18), 180201, 2022
Machine learning conservation laws from differential equations
Z Liu, V Madhavan, M Tegmark
Physical Review E 106 (4), 045307, 2022
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
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
The quantization model of neural scaling
E Michaud, Z Liu, G Uzay, M Tegmark
Advances in Neural Information Processing Systems, 2023
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
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
Physics-augmented learning: A new paradigm beyond physics-informed learning
Z Liu, Y Chen, Y Du, M Tegmark
arXiv preprint arXiv:2109.13901, 2021
Seeing is believing: Brain-inspired modular training for mechanistic interpretability
Z Liu, E Gan, M Tegmark
Entropy 26 (1), 41, 2023
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
Restart Sampling for Improving Generative Processes
Y Xu, M Deng, X Cheng, Y Tian, Z Liu, T Jaakkola
arXiv: 2306.14878, 2023
Precision machine learning
EJ Michaud, Z Liu, M Tegmark
Entropy 25 (1), 175, 2023
Quantum-inspired hamiltonian monte carlo for bayesian sampling
Z Liu, Z Zhang
arXiv preprint arXiv:1912.01937, 2019
Genphys: From physical processes to generative models
Z Liu, D Luo, Y Xu, T Jaakkola, M Tegmark
arXiv preprint arXiv:2304.02637, 2023
Second order ensemble Langevin method for sampling and inverse problems
Z Liu, AM Stuart, Y Wang
arXiv preprint arXiv:2208.04506, 2022
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