Ziming Liu - RankMe

61 papers A* 10Journal 51

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Inverse Depth Scaling From Most Layers Being Similar. CoRR Yizhou Liu, Sara Kangaslahti, Ziming Liu, Jeff Gore
2026	J	`jnl`	Universal One-third Time Scaling in Learning Peaked Distributions. CoRR Yizhou Liu, Ziming Liu, Cengiz Pehlevan, Jeff Gore
2025	J	`jnl`	Do Two AI Scientists Agree? CoRR Xinghong Fu, Ziming Liu, Max Tegmark
2025	J	`jnl`	FOCUS: First Order Concentrated Updating Scheme. CoRR Yizhou Liu, Ziming Liu, Jeff Gore
2025	J	`jnl`	Fokker-Planck to Callan-Symanzik: evolution of weight matrices under training. CoRR Wei Bu, Uri Kol, Ziming Liu
2025	J	`jnl`	Harmonic Loss Trains Interpretable AI Models. CoRR David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark
2025	J	`jnl`	Harmonic Loss Trains Interpretable AI Models. Trans. Mach. Learn. Res. David D. Baek, Ziming Liu, Riya Tyagi, Max Tegmark
2025	J	`jnl`	High precision PINNs in unbounded domains: application to singularity formulation in PDEs. CoRR Yixuan Wang, Ziming Liu, Zongyi Li, Anima Anandkumar, Thomas Y. Hou
2025	J	`jnl`	Interpretable Machine Learning in Physics: A Review. CoRR Sebastian Johann Wetzel, Seungwoong Ha, Raban Iten, Miriam Klopotek, Ziming Liu
2025	A*	`conf`	KAN: Kolmogorov-Arnold Networks. ICLR Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark
2025	J	`jnl`	KANO: Kolmogorov-Arnold Neural Operator. CoRR Jin Lee, Ziming Liu, Xinling Yu, Yixuan Wang, Haewon Jeong, Murphy Yuezhen Niu, Zheng Zhang
2025	J	`jnl`	Neural Thermodynamic Laws for Large Language Model Training. CoRR Ziming Liu, Yizhou Liu, Jeff Gore, Max Tegmark
2025	A*	`conf`	On the expressiveness and spectral bias of KANs. ICLR Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou
2025	J	`jnl`	Physics of Skill Learning. CoRR Ziming Liu, Yizhou Liu, Eric J. Michaud, Jeff Gore, Max Tegmark
2025	J	`jnl`	Superposition Yields Robust Neural Scaling. CoRR Yizhou Liu, Ziming Liu, Jeff Gore
2024	J	`jnl`	A Resource Model For Neural Scaling Law. CoRR Jinyeop Song, Ziming Liu, Max Tegmark, Jeff Gore
2024	J	`jnl`	Do Diffusion Models Learn Semantically Meaningful and Efficient Representations? CoRR Qiyao Liang, Ziming Liu, Ila Fiete
2024	J	`jnl`	GenEFT: Understanding Statics and Dynamics of Model Generalization via Effective Theory. CoRR David D. Baek, Ziming Liu, Max Tegmark
2024	A*	`conf`	How Diffusion Models Learn to Factorize and Compose. NeurIPS Qiyao Liang, Ziming Liu, Mitchell Ostrow, Ila Fiete
2024	J	`jnl`	How Diffusion Models Learn to Factorize and Compose. CoRR Qiyao Liang, Ziming Liu, Mitchell Ostrow, Ila Fiete
2024	J	`jnl`	How Do Transformers "Do" Physics? Investigating the Simple Harmonic Oscillator. CoRR Subhash Kantamneni, Ziming Liu, Max Tegmark
2024	J	`jnl`	How Do Transformers Model Physics? Investigating the Simple Harmonic Oscillator. Entropy Subhash Kantamneni, Ziming Liu, Max Tegmark
2024	J	`jnl`	KAN 2.0: Kolmogorov-Arnold Networks Meet Science. CoRR Ziming Liu, Pingchuan Ma, Yixuan Wang, Wojciech Matusik, Max Tegmark
2024	J	`jnl`	KAN: Kolmogorov-Arnold Networks. CoRR Ziming Liu, Yixuan Wang, Sachin Vaidya, Fabian Ruehle, James Halverson, Marin Soljacic, Thomas Y. Hou, Max Tegmark
2024	J	`jnl`	On the expressiveness and spectral bias of KANs. CoRR Yixuan Wang, Jonathan W. Siegel, Ziming Liu, Thomas Y. Hou
2024	J	`jnl`	Opening the AI Black Box: Distilling Machine-Learned Algorithms into Code. Entropy Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark
2024	J	`jnl`	Opening the AI black box: program synthesis via mechanistic interpretability. CoRR Eric J. Michaud, Isaac Liao, Vedang Lad, Ziming Liu, Anish Mudide, Chloe Loughridge, Zifan Carl Guo, Tara Rezaei Kheirkhah, Mateja Vukelic, Max Tegmark
2024	J	`jnl`	OptPDE: Discovering Novel Integrable Systems via AI-Human Collaboration. CoRR Subhash Kantamneni, Ziming Liu, Max Tegmark
2024	J	`jnl`	Seeing Is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability. Entropy Ziming Liu, Eric Gan, Max Tegmark
2024	J	`jnl`	Survival of the Fittest Representation: A Case Study with Modular Addition. CoRR Xiaoman Delores Ding, Zifan Carl Guo, Eric J. Michaud, Ziming Liu, Max Tegmark
2023	J	`jnl`	A Neural Scaling Law from Lottery Ticket Ensembling. CoRR Ziming Liu, Max Tegmark
2023	J	`jnl`	Discovering New Interpretable Conservation Laws as Sparse Invariants. CoRR Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, Max Tegmark
2023	J	`jnl`	GenPhys: From Physical Processes to Generative Models. CoRR Ziming Liu, Di Luo, Yilun Xu, Tommi S. Jaakkola, Max Tegmark
2023	J	`jnl`	Generating Interpretable Networks using Hypernetworks. CoRR Isaac Liao, Ziming Liu, Max Tegmark
2023	J	`jnl`	Grokking as Compression: A Nonlinear Complexity Perspective. CoRR Ziming Liu, Ziqian Zhong, Max Tegmark
2023	J	`jnl`	Growing Brains: Co-emergence of Anatomical and Functional Modularity in Recurrent Neural Networks. CoRR Ziming Liu, Mikail Khona, Ila R. Fiete, Max Tegmark
2023	A*	`conf`	Omnigrok: Grokking Beyond Algorithmic Data. ICLR Ziming Liu, Eric J. Michaud, Max Tegmark
2023	A*	`conf`	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. ICML Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
2023	J	`jnl`	PFGM++: Unlocking the Potential of Physics-Inspired Generative Models. CoRR Yilun Xu, Ziming Liu, Yonglong Tian, Shangyuan Tong, Max Tegmark, Tommi S. Jaakkola
2023	J	`jnl`	Precision Machine Learning. Entropy Eric J. Michaud, Ziming Liu, Max Tegmark
2023	A*	`conf`	Restart Sampling for Improving Generative Processes. NeurIPS Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
2023	J	`jnl`	Restart Sampling for Improving Generative Processes. CoRR Yilun Xu, Mingyang Deng, Xiang Cheng, Yonglong Tian, Ziming Liu, Tommi S. Jaakkola
2023	J	`jnl`	Scientific discovery in the age of artificial intelligence. Nat. 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
2023	J	`jnl`	Seeing is Believing: Brain-Inspired Modular Training for Mechanistic Interpretability. CoRR Ziming Liu, Eric Gan, Max Tegmark
2023	A*	`conf`	The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks. NeurIPS Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas
2023	J	`jnl`	The Clock and the Pizza: Two Stories in Mechanistic Explanation of Neural Networks. CoRR Ziqian Zhong, Ziming Liu, Max Tegmark, Jacob Andreas
2023	A*	`conf`	The Quantization Model of Neural Scaling. NeurIPS Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
2023	J	`jnl`	The Quantization Model of Neural Scaling. CoRR Eric J. Michaud, Ziming Liu, Uzay Girit, Max Tegmark
2022	J	`jnl`	AI Poincaré 2.0: Machine Learning Conservation Laws from Differential Equations. CoRR Ziming Liu, Varun Madhavan, Max Tegmark
2022	J	`jnl`	Omnigrok: Grokking Beyond Algorithmic Data. CoRR Ziming Liu, Eric J. Michaud, Max Tegmark
2022	A*	`conf`	Poisson Flow Generative Models. NeurIPS Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola
2022	J	`jnl`	Poisson Flow Generative Models. CoRR Yilun Xu, Ziming Liu, Max Tegmark, Tommi S. Jaakkola
2022	J	`jnl`	Precision Machine Learning. CoRR Eric J. Michaud, Ziming Liu, Max Tegmark
2022	J	`jnl`	Second Order Ensemble Langevin Method for Sampling and Inverse Problems. CoRR Ziming Liu, Andrew M. Stuart, Yixuan Wang
2022	A*	`conf`	Towards Understanding Grokking: An Effective Theory of Representation Learning. NeurIPS Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
2022	J	`jnl`	Towards Understanding Grokking: An Effective Theory of Representation Learning. CoRR Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud, Max Tegmark, Mike Williams
2021	J	`jnl`	Machine-Learning Non-Conservative Dynamics for New-Physics Detection. CoRR Ziming Liu, Bohan Wang, Qi Meng, Wei Chen, Max Tegmark, Tie-Yan Liu
2021	J	`jnl`	Machine-learning hidden symmetries. CoRR Ziming Liu, Max Tegmark
2021	J	`jnl`	Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning. CoRR Ziming Liu, Yunyue Chen, Yuanqi Du, Max Tegmark
2020	J	`jnl`	AI Poincaré: Machine Learning Conservation Laws from Trajectories. CoRR Ziming Liu, Max Tegmark
2019	J	`jnl`	Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling. CoRR Ziming Liu, Zheng Zhang