Saved in:
| Main Authors: | Worschech, Roman, Rosenow, Bernd |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.09005 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
A Boundary-Layer Mechanism for One-Third Scaling in Online Softmax Classification
by: Kühn, Marcel, et al.
Published: (2026)
by: Kühn, Marcel, et al.
Published: (2026)
Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
by: Defilippis, Leonardo, et al.
Published: (2025)
by: Defilippis, Leonardo, et al.
Published: (2025)
Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning
by: Staats, Max, et al.
Published: (2023)
by: Staats, Max, et al.
Published: (2023)
Scaling Laws for Emulation of Stellar Spectra
by: Różański, Tomasz, et al.
Published: (2025)
by: Różański, Tomasz, et al.
Published: (2025)
AlphaZero Neural Scaling and Zipf's Law: a Tale of Board Games and Power Laws
by: Neumann, Oren, et al.
Published: (2024)
by: Neumann, Oren, et al.
Published: (2024)
Breaking Neural Network Scaling Laws with Modularity
by: Boopathy, Akhilan, et al.
Published: (2024)
by: Boopathy, Akhilan, et al.
Published: (2024)
Unified Neural Network Scaling Laws and Scale-time Equivalence
by: Boopathy, Akhilan, et al.
Published: (2024)
by: Boopathy, Akhilan, et al.
Published: (2024)
Neural Neural Scaling Laws
by: Hu, Michael Y., et al.
Published: (2026)
by: Hu, Michael Y., et al.
Published: (2026)
Bayesian Neural Scaling Law Extrapolation with Prior-Data Fitted Networks
by: Lee, Dongwoo, et al.
Published: (2025)
by: Lee, Dongwoo, et al.
Published: (2025)
Topological gap protocol based machine learning optimization of Majorana hybrid wires
by: Thamm, Matthias, et al.
Published: (2023)
by: Thamm, Matthias, et al.
Published: (2023)
Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances in Flat Directions
by: Kühn, Marcel, et al.
Published: (2023)
by: Kühn, Marcel, et al.
Published: (2023)
Neural Scaling Laws Rooted in the Data Distribution
by: Brill, Ari
Published: (2024)
by: Brill, Ari
Published: (2024)
On the Invariance and Generality of Neural Scaling Laws
by: Han, Xing, et al.
Published: (2026)
by: Han, Xing, et al.
Published: (2026)
Explaining Neural Scaling Laws
by: Bahri, Yasaman, et al.
Published: (2021)
by: Bahri, Yasaman, et al.
Published: (2021)
Scaling Laws are Redundancy Laws
by: Bi, Yuda, et al.
Published: (2025)
by: Bi, Yuda, et al.
Published: (2025)
Scaling Laws for Optimal Data Mixtures
by: Shukor, Mustafa, et al.
Published: (2025)
by: Shukor, Mustafa, et al.
Published: (2025)
Scaling Laws of Graph Neural Networks for Atomistic Materials Modeling
by: Li, Chaojian, et al.
Published: (2025)
by: Li, Chaojian, et al.
Published: (2025)
Towards Neural Scaling Laws on Graphs
by: Liu, Jingzhe, et al.
Published: (2024)
by: Liu, Jingzhe, et al.
Published: (2024)
On the Optimizer Dependence of Neural Scaling Laws
by: Ramani, Vansh, et al.
Published: (2026)
by: Ramani, Vansh, et al.
Published: (2026)
Spectra 1.1: Scaling Laws and Efficient Inference for Ternary Language Models
by: Vaidhya, Tejas, et al.
Published: (2025)
by: Vaidhya, Tejas, et al.
Published: (2025)
Configuration-to-Performance Scaling Law with Neural Ansatz
by: Zhang, Huaqing, et al.
Published: (2026)
by: Zhang, Huaqing, et al.
Published: (2026)
Müntz-Szász Networks: Neural Architectures with Learnable Power-Law Bases
by: N'guessan, Gnankan Landry Regis
Published: (2025)
by: N'guessan, Gnankan Landry Regis
Published: (2025)
Neural Scaling Laws for Deep Regression
by: Cadez, Tilen, et al.
Published: (2025)
by: Cadez, Tilen, et al.
Published: (2025)
Scaling Laws for Neural Material Models
by: Trikha, Akshay, et al.
Published: (2025)
by: Trikha, Akshay, et al.
Published: (2025)
Information-Theoretic Foundations for Neural Scaling Laws
by: Jeon, Hong Jun, et al.
Published: (2024)
by: Jeon, Hong Jun, et al.
Published: (2024)
Scaling Laws of Machine Learning for Optimal Power Flow
by: Liu, Xinyi, et al.
Published: (2026)
by: Liu, Xinyi, et al.
Published: (2026)
Scaling Laws and Pathologies of Single-Layer PINNs: Network Width and PDE Nonlinearity
by: Chaudhry, Faris
Published: (2026)
by: Chaudhry, Faris
Published: (2026)
Small Singular Values Matter: A Random Matrix Analysis of Transformer Models
by: Staats, Max, et al.
Published: (2024)
by: Staats, Max, et al.
Published: (2024)
Boundary between noise and information applied to filtering neural network weight matrices
by: Staats, Max, et al.
Published: (2022)
by: Staats, Max, et al.
Published: (2022)
Zero-One Laws of Graph Neural Networks
by: Adam-Day, Sam, et al.
Published: (2023)
by: Adam-Day, Sam, et al.
Published: (2023)
How to Upscale Neural Networks with Scaling Law? A Survey and Practical Guidelines
by: Sengupta, Ayan, et al.
Published: (2025)
by: Sengupta, Ayan, et al.
Published: (2025)
Towards Scaling Law Analysis For Spatiotemporal Weather Data
by: Kiefer, Alexander, et al.
Published: (2026)
by: Kiefer, Alexander, et al.
Published: (2026)
gzip Predicts Data-dependent Scaling Laws
by: Pandey, Rohan
Published: (2024)
by: Pandey, Rohan
Published: (2024)
Prescriptive Scaling Laws for Data Constrained Training
by: Lovelace, Justin, et al.
Published: (2026)
by: Lovelace, Justin, et al.
Published: (2026)
Renormalizable Spectral-Shell Dynamics as the Origin of Neural Scaling Laws
by: Zhang, Yizhou
Published: (2025)
by: Zhang, Yizhou
Published: (2025)
Complexity Scaling Laws for Neural Models using Combinatorial Optimization
by: Weissman, Lowell, et al.
Published: (2025)
by: Weissman, Lowell, et al.
Published: (2025)
On Neural Scaling Laws for Weather Emulation through Continual Training
by: Subramanian, Shashank, et al.
Published: (2026)
by: Subramanian, Shashank, et al.
Published: (2026)
A Dynamical Model of Neural Scaling Laws
by: Bordelon, Blake, et al.
Published: (2024)
by: Bordelon, Blake, et al.
Published: (2024)
Diversity of Transformer Layers: One Aspect of Parameter Scaling Laws
by: Kamigaito, Hidetaka, et al.
Published: (2025)
by: Kamigaito, Hidetaka, et al.
Published: (2025)
Scaling Laws for Precision
by: Kumar, Tanishq, et al.
Published: (2024)
by: Kumar, Tanishq, et al.
Published: (2024)
Similar Items
-
A Boundary-Layer Mechanism for One-Third Scaling in Online Softmax Classification
by: Kühn, Marcel, et al.
Published: (2026) -
Scaling Laws and Spectra of Shallow Neural Networks in the Feature Learning Regime
by: Defilippis, Leonardo, et al.
Published: (2025) -
Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning
by: Staats, Max, et al.
Published: (2023) -
Scaling Laws for Emulation of Stellar Spectra
by: Różański, Tomasz, et al.
Published: (2025) -
AlphaZero Neural Scaling and Zipf's Law: a Tale of Board Games and Power Laws
by: Neumann, Oren, et al.
Published: (2024)