Guardado en:
| Autor principal: | Zhang, Yizhou |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | https://arxiv.org/abs/2511.07892 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws
por: Zhang, Yizhou
Publicado: (2025)
por: Zhang, Yizhou
Publicado: (2025)
When Does Learning Renormalize? Sufficient Conditions for Power Law Spectral Dynamics
por: Zhang, Yizhou
Publicado: (2025)
por: Zhang, Yizhou
Publicado: (2025)
Data Curation Through the Lens of Spectral Dynamics: Static Limits, Dynamic Acceleration, and Practical Oracles
por: Zhang, Yizhou, et al.
Publicado: (2025)
por: Zhang, Yizhou, et al.
Publicado: (2025)
Superposition Yields Robust Neural Scaling
por: Liu, Yizhou, et al.
Publicado: (2025)
por: Liu, Yizhou, et al.
Publicado: (2025)
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
por: Nikolaou, Konstantin, et al.
Publicado: (2026)
por: Nikolaou, Konstantin, et al.
Publicado: (2026)
A General Benchmark Framework is Dynamic Graph Neural Network Need
por: Zhang, Yusen
Publicado: (2024)
por: Zhang, Yusen
Publicado: (2024)
Optimal Control Operator Perspective and a Neural Adaptive Spectral Method
por: Feng, Mingquan, et al.
Publicado: (2024)
por: Feng, Mingquan, et al.
Publicado: (2024)
SlepNet: Spectral Subgraph Representation Learning for Neural Dynamics
por: Viswanath, Siddharth, et al.
Publicado: (2025)
por: Viswanath, Siddharth, et al.
Publicado: (2025)
Big2Small: A Unifying Neural Network Framework for Model Compression
por: Liao, Jing-Xiao, et al.
Publicado: (2026)
por: Liao, Jing-Xiao, et al.
Publicado: (2026)
Do Contemporary Causal Inference Models Capture Real-World Heterogeneity? Findings from a Large-Scale Benchmark
por: Yu, Haining, et al.
Publicado: (2024)
por: Yu, Haining, et al.
Publicado: (2024)
Spectral-Aware Text-to-Time Series Generation with Billion-Scale Multimodal Meteorological Data
por: Zhang, Shijie
Publicado: (2026)
por: Zhang, Shijie
Publicado: (2026)
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence Guarantee
por: Li, Chenyang, et al.
Publicado: (2023)
por: Li, Chenyang, et al.
Publicado: (2023)
Robust Spectral Watermark for Synthetic Tabular Data
por: Zhao, Yizhou, et al.
Publicado: (2025)
por: Zhao, Yizhou, et al.
Publicado: (2025)
A Spectral Framework for Multi-Scale Nonlinear Dimensionality Reduction
por: Huang, Zeyang, et al.
Publicado: (2026)
por: Huang, Zeyang, et al.
Publicado: (2026)
Incremental Spatial and Spectral Learning of Neural Operators for Solving Large-Scale PDEs
por: George, Robert Joseph, et al.
Publicado: (2022)
por: George, Robert Joseph, et al.
Publicado: (2022)
Learning Robust Spectral Dynamics for Temporal Domain Generalization
por: Yu, En, et al.
Publicado: (2025)
por: Yu, En, et al.
Publicado: (2025)
A Compression Based Classification Framework Using Symbolic Dynamics of Chaotic Maps
por: Naik, Parth, et al.
Publicado: (2025)
por: Naik, Parth, et al.
Publicado: (2025)
Dynamic Spectral Backpropagation for Efficient Neural Network Training
por: Muthuraman, Mannmohan
Publicado: (2025)
por: Muthuraman, Mannmohan
Publicado: (2025)
A Spectral Framework for Graph Neural Operators: Convergence Guarantees and Tradeoffs
por: Holden, Roxanne, et al.
Publicado: (2025)
por: Holden, Roxanne, et al.
Publicado: (2025)
Predicting LLM Compression Degradation from Spectral Statistics
por: Xu, Mingxue
Publicado: (2026)
por: Xu, Mingxue
Publicado: (2026)
Large-Scale Spectral Graph Neural Networks via Laplacian Sparsification: Technical Report
por: Ding, Haipeng, et al.
Publicado: (2025)
por: Ding, Haipeng, et al.
Publicado: (2025)
A General Error-Theoretical Analysis Framework for Constructing Compression Strategies
por: Zhang, Boyang, et al.
Publicado: (2025)
por: Zhang, Boyang, et al.
Publicado: (2025)
On the Invariance and Generality of Neural Scaling Laws
por: Han, Xing, et al.
Publicado: (2026)
por: Han, Xing, et al.
Publicado: (2026)
Questioning the Coverage-Length Metric in Conformal Prediction: When Shorter Intervals Are Not Better
por: Min, Yizhou, et al.
Publicado: (2026)
por: Min, Yizhou, et al.
Publicado: (2026)
Schrodinger AI: A Unified Spectral-Dynamical Framework for Classification, Reasoning, and Operator-Based Generalization
por: Nguyen, Truong Son
Publicado: (2025)
por: Nguyen, Truong Son
Publicado: (2025)
Conditional Local Independence Testing for Itô processes with Applications to Dynamic Causal Discovery
por: Liu, Mingzhou, et al.
Publicado: (2025)
por: Liu, Mingzhou, et al.
Publicado: (2025)
A method of supervised learning from conflicting data with hidden contexts
por: Zhang, Tianren, et al.
Publicado: (2021)
por: Zhang, Tianren, et al.
Publicado: (2021)
Neural Normalized Compression Distance and the Disconnect Between Compression and Classification
por: Hurwitz, John, et al.
Publicado: (2024)
por: Hurwitz, John, et al.
Publicado: (2024)
Spectral Neural Graph Sparsification
por: Liguori, Angelica, et al.
Publicado: (2025)
por: Liguori, Angelica, et al.
Publicado: (2025)
Universal One-third Time Scaling in Learning Peaked Distributions
por: Liu, Yizhou, et al.
Publicado: (2026)
por: Liu, Yizhou, et al.
Publicado: (2026)
Feature contamination: Neural networks learn uncorrelated features and fail to generalize
por: Zhang, Tianren, et al.
Publicado: (2024)
por: Zhang, Tianren, et al.
Publicado: (2024)
SpectralNet: Spectral Clustering using Deep Neural Networks
por: Shaham, Uri, et al.
Publicado: (2018)
por: Shaham, Uri, et al.
Publicado: (2018)
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
por: Subramonian, Arjun, et al.
Publicado: (2024)
por: Subramonian, Arjun, et al.
Publicado: (2024)
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
por: Thielmann, Anton, et al.
Publicado: (2023)
por: Thielmann, Anton, et al.
Publicado: (2023)
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
por: Gu, Lingyu, et al.
Publicado: (2024)
por: Gu, Lingyu, et al.
Publicado: (2024)
A Dynamical Model of Neural Scaling Laws
por: Bordelon, Blake, et al.
Publicado: (2024)
por: Bordelon, Blake, et al.
Publicado: (2024)
Spectral Condition for $μ$P under Width-Depth Scaling
por: Zheng, Chenyu, et al.
Publicado: (2026)
por: Zheng, Chenyu, et al.
Publicado: (2026)
Homomorphism Expressivity of Spectral Invariant Graph Neural Networks
por: Gai, Jingchu, et al.
Publicado: (2025)
por: Gai, Jingchu, et al.
Publicado: (2025)
SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts
por: Li, Jiayi, et al.
Publicado: (2026)
por: Li, Jiayi, et al.
Publicado: (2026)
The Lifecycle of the Spectral Edge: From Gradient Learning to Weight-Decay Compression
por: Xu, Yongzhong
Publicado: (2026)
por: Xu, Yongzhong
Publicado: (2026)
Ejemplares similares
-
Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws
por: Zhang, Yizhou
Publicado: (2025) -
When Does Learning Renormalize? Sufficient Conditions for Power Law Spectral Dynamics
por: Zhang, Yizhou
Publicado: (2025) -
Data Curation Through the Lens of Spectral Dynamics: Static Limits, Dynamic Acceleration, and Practical Oracles
por: Zhang, Yizhou, et al.
Publicado: (2025) -
Superposition Yields Robust Neural Scaling
por: Liu, Yizhou, et al.
Publicado: (2025) -
Spectral Reach: Understanding Neural Scaling as Progress into the Spectral Tail
por: Nikolaou, Konstantin, et al.
Publicado: (2026)