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ORCIDZiming Liu 0001
Person information
- affiliation (PhD): Massachusetts Institute of Technology, Department of Physics, Institute for Artificial Intelligence and Fundamental Interactions, Cambridge, MA, USA
Other persons with the same name
- Ziming Liu — disambiguation page
- Ziming Liu 0002
![[0000-0003-4763-8397]](https://dblp.org/img/orcid-mark.12x12.png "0000-0003-4763-8397")
— University of Oklahoma, Department of Computer Science, Norman, OK, USA (and 1 more)
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2020 – today
- 2025
[c9]Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark:
KAN: Kolmogorov-Arnold Networks. ICLR 2025- [c8]Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou:
On the expressiveness and spectral bias of KANs. ICLR 2025 - [i42]Wei Bu, Uri Kol, Ziming Liu:
Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training. CoRR abs/2501.09659 (2025) - [i41]Yizhou Liu, Ziming Liu, Jeff Gore:
FOCUS: First Order Concentrated Updating Scheme. CoRR abs/2501.12243 (2025) - [i40]Ziming Liu, Yizhou Liu, Eric J. Michaud, Jeff Gore, Max Tegmark:
Physics of Skill Learning. CoRR abs/2501.12391 (2025) - [i39]David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark:
Harmonic Loss Trains Interpretable AI Models. CoRR abs/2502.01628 (2025) - [i38]Sebastian Johann Wetzel, Seungwoong Ha, Raban Iten, Miriam Klopotek, Ziming Liu:
Interpretable Machine Learning in Physics: A Review. CoRR abs/2503.23616 (2025) - [i37]Xinghong Fu, Ziming Liu, Max Tegmark:
Do Two AI Scientists Agree? CoRR abs/2504.02822 (2025) - [i36]Yizhou Liu, Ziming Liu, Jeff Gore:
Superposition Yields Robust Neural Scaling. CoRR abs/2505.10465 (2025) - [i35]Ziming Liu, Yizhou Liu, Jeff Gore, Max Tegmark:
Neural Thermodynamic Laws for Large Language Model Training. CoRR abs/2505.10559 (2025) - [i34]Yixuan Wang, Ziming Liu, Zongyi Li, Anima Anandkumar, Thomas Y. Hou:
High precision PINNs in unbounded domains: application to singularity formulation in PDEs. CoRR abs/2506.19243 (2025) - [i33]Jin Lee, Ziming Liu, Xinling Yu, Yixuan Wang, Haewon Jeong, Murphy Yuezhen Niu, Zheng Zhang:
KANO: Kolmogorov-Arnold Neural Operator. CoRR abs/2509.16825 (2025) - 2024
- [j5]Ziming Liu, Eric Gan, Max Tegmark:
Seeing Is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability. Entropy 26(1): 41 (2024) - [j4]Subhash Kantamneni, Ziming Liu, Max Tegmark:
How Do Transformers Model Physics? Investigating the Simple Harmonic Oscillator. Entropy 26(11): 997 (2024) - [j3]Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark:
Opening the AI Black Box: Distilling Machine-Learned Algorithms into Code. Entropy 26(12): 1046 (2024) - [i32]Qiyao Liang, Ziming Liu, Ila Fiete:
Do Diffusion Models Learn Semantically Meaningful and Efficient Representations? CoRR abs/2402.03305 (2024) - [i31]Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark:
Opening the AI black box: program synthesis via mechanistic interpretability. CoRR abs/2402.05110 (2024) - [i30]Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore:
A Resource Model For Neural Scaling Law. CoRR abs/2402.05164 (2024) - [i29]David D. Baek, Ziming Liu, Max Tegmark:
GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory. CoRR abs/2402.05916 (2024) - [i28]Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark:
KAN: Kolmogorov-Arnold Networks. CoRR abs/2404.19756 (2024) - [i27]Subhash Kantamneni, Ziming Liu, Max Tegmark:
OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration. CoRR abs/2405.04484 (2024) - [i26]Subhash Kantamneni, Ziming Liu, Max Tegmark:
How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator. CoRR abs/2405.17209 (2024) - [i25]Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark:
Survival of the Fittest Representation: A Case Study with Modular Addition. CoRR abs/2405.17420 (2024) - [i24]Ziming Liu, Pingchuan Ma, Yixuan Wang, Wojciech Matusik, Max Tegmark:
KAN 2.0: Kolmogorov-Arnold Networks Meet Science. CoRR abs/2408.10205 (2024) - [i23]Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou:
On the expressiveness and spectral bias of KANs. CoRR abs/2410.01803 (2024) - 2023
- [j2]Eric J. Michaud, Ziming Liu, Max Tegmark:
Precision Machine Learning. Entropy 25(1): 175 (2023) - [j1]Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun K. Manrai, Debora S. Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Velickovic, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, Marinka Zitnik:
Scientific discovery in the age of artificial intelligence. Nat. 620(7972): 47-60 (2023) - [c7]Ziming Liu, Eric J. Michaud, Max Tegmark:
Omnigrok: Grokking Beyond Algorithmic Data. ICLR 2023 - [c6]Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola:
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. ICML 2023: 38566-38591 - [c5]Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark:
The Quantization Model of Neural Scaling. NeurIPS 2023 - [c4]Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola:
Restart Sampling for Improving Generative Processes. NeurIPS 2023 - [c3]Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas:
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks. NeurIPS 2023 - [i22]Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola:
PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. CoRR abs/2302.04265 (2023) - [i21]Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark:
The Quantization Model of Neural Scaling. CoRR abs/2303.13506 (2023) - [i20]Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark:
GenPhys: From Physical Processes to Generative Models. CoRR abs/2304.02637 (2023) - [i19]Ziming Liu, Eric Gan, Max Tegmark:
Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability. CoRR abs/2305.08746 (2023) - [i18]Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark:
Discovering New Interpretable Conservation Laws as Sparse Invariants. CoRR abs/2305.19525 (2023) - [i17]Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola:
Restart Sampling for Improving Generative Processes. CoRR abs/2306.14878 (2023) - [i16]Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas:
The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks. CoRR abs/2306.17844 (2023) - [i15]Ziming Liu, Max Tegmark:
A Neural Scaling Law from Lottery Ticket Ensembling. CoRR abs/2310.02258 (2023) - [i14]Ziming Liu, Ziqian Zhong, Max Tegmark:
Grokking as Compression: A Nonlinear Complexity Perspective. CoRR abs/2310.05918 (2023) - [i13]Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark:
Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks. CoRR abs/2310.07711 (2023) - [i12]Isaac Liao, Ziming Liu, Max Tegmark:
Generating Interpretable Networks using Hypernetworks. CoRR abs/2312.03051 (2023) - 2022
- [c2]Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams:
Towards Understanding Grokking: An Effective Theory of Representation Learning. NeurIPS 2022 - [c1]Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola:
Poisson Flow Generative Models. NeurIPS 2022 - [i11]Ziming Liu, Varun Madhavan, Max Tegmark:
AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations. CoRR abs/2203.12610 (2022) - [i10]Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams:
Towards Understanding Grokking: An Effective Theory of Representation Learning. CoRR abs/2205.10343 (2022) - [i9]Ziming Liu, Andrew M. Stuart, Yixuan Wang:
Second Order Ensemble Langevin Method for Sampling and Inverse Problems. CoRR abs/2208.04506 (2022) - [i8]Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola:
Poisson Flow Generative Models. CoRR abs/2209.11178 (2022) - [i7]Ziming Liu, Eric J. Michaud, Max Tegmark:
Omnigrok: Grokking Beyond Algorithmic Data. CoRR abs/2210.01117 (2022) - [i6]Eric J. Michaud, Ziming Liu, Max Tegmark:
Precision Machine Learning. CoRR abs/2210.13447 (2022) - 2021
- [i5]Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu:
Machine-Learning Non-Conservative Dynamics for New-Physics Detection. CoRR abs/2106.00026 (2021) - [i4]Ziming Liu, Max Tegmark:
Machine-learning hidden symmetries. CoRR abs/2109.09721 (2021) - [i3]Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark:
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning. CoRR abs/2109.13901 (2021) - 2020
- [i2]Ziming Liu, Max Tegmark:
AI Poincaré: Machine Learning Conservation Laws from Trajectories. CoRR abs/2011.04698 (2020)
2010 – 2019
- 2019
- [i1]Ziming Liu, Zheng Zhang:
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling. CoRR abs/1912.01937 (2019)
Coauthor Index
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