Ziming Liu

According to our database, Ziming Liu authored at least 46 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2025

KANO: Kolmogorov-Arnold Neural Operator.

Jin Lee, Ziming Liu, Xinling Yu, Yixuan Wang, Haewon Jeong, Murphy Yuezhen Niu, Zheng Zhang

CoRR, September, 2025

High precision PINNs in unbounded domains: application to singularity formulation in PDEs.

Yixuan Wang, Ziming Liu, Zongyi Li, Anima Anandkumar, Thomas Y. Hou

CoRR, June, 2025

Neural Thermodynamic Laws for Large Language Model Training.

Ziming Liu, Yizhou Liu, Jeff Gore, Max Tegmark

CoRR, May, 2025

Superposition Yields Robust Neural Scaling.

Yizhou Liu, Ziming Liu, Jeff Gore

CoRR, May, 2025

Do Two AI Scientists Agree?

Xinghong Fu, Ziming Liu, Max Tegmark

CoRR, April, 2025

Interpretable Machine Learning in Physics: A Review.

Sebastian Johann Wetzel, Seungwoong Ha, Raban Iten, Miriam Klopotek, Ziming Liu

CoRR, March, 2025

Harmonic Loss Trains Interpretable AI Models.

David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark

CoRR, February, 2025

Physics of Skill Learning.

Ziming Liu, Yizhou Liu, Eric J. Michaud, Jeff Gore, Max Tegmark

CoRR, January, 2025

FOCUS: First Order Concentrated Updating Scheme.

Yizhou Liu, Ziming Liu, Jeff Gore

CoRR, January, 2025

Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training.

Wei Bu, Uri Kol, Ziming Liu

CoRR, January, 2025

On the expressiveness and spectral bias of KANs.

Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

KAN: Kolmogorov-Arnold Networks.

Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark

Proceedings of the Thirteenth International Conference on Learning Representations, 2025

2024

Seeing Is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability.

Ziming Liu, Eric Gan, Max Tegmark

Entropy, January, 2024

Opening the AI Black Box: Distilling Machine-Learned Algorithms into Code.

Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark

Entropy, 2024

How Do Transformers Model Physics? Investigating the Simple Harmonic Oscillator.

Subhash Kantamneni, Ziming Liu, Max Tegmark

Entropy, 2024

KAN 2.0: Kolmogorov-Arnold Networks Meet Science.

Ziming Liu, Pingchuan Ma, Yixuan Wang, Wojciech Matusik, Max Tegmark

CoRR, 2024

Survival of the Fittest Representation: A Case Study with Modular Addition.

Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark

CoRR, 2024

How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator.

Subhash Kantamneni, Ziming Liu, Max Tegmark

CoRR, 2024

OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration.

Subhash Kantamneni, Ziming Liu, Max Tegmark

CoRR, 2024

GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory.

David D. Baek, Ziming Liu, Max Tegmark

CoRR, 2024

A Resource Model For Neural Scaling Law.

Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore

CoRR, 2024

Opening the AI black box: program synthesis via mechanistic interpretability.

Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark

CoRR, 2024

Do Diffusion Models Learn Semantically Meaningful and Efficient Representations?

Qiyao Liang, Ziming Liu, Ila Fiete

CoRR, 2024

How Diffusion Models Learn to Factorize and Compose.

Qiyao Liang, Ziming Liu, Mitchell Ostrow, Ila Fiete

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

2023

Precision Machine Learning.

Eric J. Michaud, Ziming Liu, Max Tegmark

Entropy, January, 2023

Scientific discovery in the age of artificial intelligence.

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

Nat., 2023

Generating Interpretable Networks using Hypernetworks.

Isaac Liao, Ziming Liu, Max Tegmark

CoRR, 2023

Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks.

Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark

CoRR, 2023

Grokking as Compression: A Nonlinear Complexity Perspective.

Ziming Liu, Ziqian Zhong, Max Tegmark

CoRR, 2023

A Neural Scaling Law from Lottery Ticket Ensembling.

Ziming Liu, Max Tegmark

CoRR, 2023

Discovering New Interpretable Conservation Laws as Sparse Invariants.

Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark

CoRR, 2023

GenPhys: From Physical Processes to Generative Models.

Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark

CoRR, 2023

The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks.

Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Restart Sampling for Improving Generative Processes.

Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

The Quantization Model of Neural Scaling.

Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

PFGM++: Unlocking the Potential of Physics-Inspired Generative Models.

Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola

Proceedings of the International Conference on Machine Learning, 2023

Omnigrok: Grokking Beyond Algorithmic Data.

Ziming Liu, Eric J. Michaud, Max Tegmark

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Second Order Ensemble Langevin Method for Sampling and Inverse Problems.

Ziming Liu, Andrew M. Stuart, Yixuan Wang

CoRR, 2022

AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations.

Ziming Liu, Varun Madhavan, Max Tegmark

CoRR, 2022

Poisson Flow Generative Models.

Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Towards Understanding Grokking: An Effective Theory of Representation Learning.

Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning.

Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark

CoRR, 2021

Machine-learning hidden symmetries.

Ziming Liu, Max Tegmark

CoRR, 2021

Machine-Learning Non-Conservative Dynamics for New-Physics Detection.

Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu

CoRR, 2021

2020

AI Poincaré: Machine Learning Conservation Laws from Trajectories.

Ziming Liu, Max Tegmark

CoRR, 2020

2019

Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling.

Ziming Liu, Zheng Zhang

CoRR, 2019