Detecting overfitting in Neural Networks during long-horizon grokking using Random Matrix Theory
Fuente:
arXiv
Guardado en:
| Autores principales: | Prakash, Hari K., Martin, Charles H |
|---|---|
| Formato: | Preprint |
| Publicado: |
2026
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
Late-Stage Generalization Collapse in Grokking: Detecting anti-grokking with Weightwatcher
por: Prakash, Hari K, et al.
Publicado: (2026)
por: Prakash, Hari K, et al.
Publicado: (2026)
Grokking and Generalization Collapse: Insights from \texttt{HTSR} theory
por: Prakash, Hari K., et al.
Publicado: (2025)
por: Prakash, Hari K., et al.
Publicado: (2025)
Preventing overfitting in deep learning using differential privacy
por: Khatri, Alizishaan Anwar Hussein
Publicado: (2026)
por: Khatri, Alizishaan Anwar Hussein
Publicado: (2026)
Be aware of overfitting by hyperparameter optimization!
por: Tetko, Igor V., et al.
Publicado: (2024)
por: Tetko, Igor V., et al.
Publicado: (2024)
Spectral Geometry for Deep Learning: Compression and Hallucination Detection via Random Matrix Theory
por: Ettori, Davide
Publicado: (2026)
por: Ettori, Davide
Publicado: (2026)
Efficient local linearity regularization to overcome catastrophic overfitting
por: Rocamora, Elias Abad, et al.
Publicado: (2024)
por: Rocamora, Elias Abad, et al.
Publicado: (2024)
A Generalized Singular Value Theory for Neural Networks
por: Brown, Brian Charles, et al.
Publicado: (2026)
por: Brown, Brian Charles, et al.
Publicado: (2026)
Structure and Redundancy in Large Language Models: A Spectral Study via Random Matrix Theory
por: Ettori, Davide
Publicado: (2026)
por: Ettori, Davide
Publicado: (2026)
Graph Neural Networks at a Fraction
por: Joshi, Rucha Bhalchandra, et al.
Publicado: (2025)
por: Joshi, Rucha Bhalchandra, et al.
Publicado: (2025)
Privacy-Preserving Intrusion Detection using Convolutional Neural Networks
por: Kodys, Martin, et al.
Publicado: (2024)
por: Kodys, Martin, et al.
Publicado: (2024)
Interpretable Neural Networks with Random Constructive Algorithm
por: Nan, Jing, et al.
Publicado: (2023)
por: Nan, Jing, et al.
Publicado: (2023)
Emergence of Structure in Ensembles of Random Neural Networks
por: Muscarnera, Luca, et al.
Publicado: (2025)
por: Muscarnera, Luca, et al.
Publicado: (2025)
Maximizing the Potential of Synthetic Data: Insights from Random Matrix Theory
por: Firdoussi, Aymane El, et al.
Publicado: (2024)
por: Firdoussi, Aymane El, et al.
Publicado: (2024)
NeuralMatrix: Compute the Entire Neural Networks with Linear Matrix Operations for Efficient Inference
por: Sun, Ruiqi, et al.
Publicado: (2023)
por: Sun, Ruiqi, et al.
Publicado: (2023)
Random-Set Graph Neural Networks
por: Woodley, Tommy, et al.
Publicado: (2026)
por: Woodley, Tommy, et al.
Publicado: (2026)
Survey on Generalization Theory for Graph Neural Networks
por: Vasileiou, Antonis, et al.
Publicado: (2025)
por: Vasileiou, Antonis, et al.
Publicado: (2025)
Fractional-order Jacobian Matrix Differentiation and Its Application in Artificial Neural Networks
por: zhou, Xiaojun, et al.
Publicado: (2025)
por: zhou, Xiaojun, et al.
Publicado: (2025)
Graph Neural Networks with Feature and Structure Aware Random Walk
por: Zhuo, Wei, et al.
Publicado: (2021)
por: Zhuo, Wei, et al.
Publicado: (2021)
Deep Neural Networks via Complex Network Theory: a Perspective
por: La Malfa, Emanuele, et al.
Publicado: (2024)
por: La Malfa, Emanuele, et al.
Publicado: (2024)
Low-Rank Tensor Decompositions for the Theory of Neural Networks
por: Borsoi, Ricardo, et al.
Publicado: (2025)
por: Borsoi, Ricardo, et al.
Publicado: (2025)
GB-RVFL: Fusion of Randomized Neural Network and Granular Ball Computing
por: Sajid, M., et al.
Publicado: (2024)
por: Sajid, M., et al.
Publicado: (2024)
Dynamic Universal Approximation Theory: Foundations for Parallelism in Neural Networks
por: Wang, Wei, et al.
Publicado: (2024)
por: Wang, Wei, et al.
Publicado: (2024)
Bridging Theory and Practice in Link Representation with Graph Neural Networks
por: Lachi, Veronica, et al.
Publicado: (2025)
por: Lachi, Veronica, et al.
Publicado: (2025)
Testing Components of the Attention Schema Theory in Artificial Neural Networks
por: Farrell, Kathryn T., et al.
Publicado: (2024)
por: Farrell, Kathryn T., et al.
Publicado: (2024)
Online Neural Networks for Change-Point Detection
por: Hushchyn, Mikhail, et al.
Publicado: (2020)
por: Hushchyn, Mikhail, et al.
Publicado: (2020)
RandomNet: Clustering Time Series Using Untrained Deep Neural Networks
por: Li, Xiaosheng, et al.
Publicado: (2024)
por: Li, Xiaosheng, et al.
Publicado: (2024)
Transformers are Graph Neural Networks
por: Joshi, Chaitanya K.
Publicado: (2025)
por: Joshi, Chaitanya K.
Publicado: (2025)
Spectral Theory for Edge Pruning in Asynchronous Recurrent Graph Neural Networks
por: Bessone, Nicolas
Publicado: (2025)
por: Bessone, Nicolas
Publicado: (2025)
On the Convergence and Size Transferability of Continuous-depth Graph Neural Networks
por: Yan, Mingsong, et al.
Publicado: (2025)
por: Yan, Mingsong, et al.
Publicado: (2025)
Transferring Graph Neural Networks for Soft Sensor Modeling using Process Topologies
por: Theisen, Maximilian F., et al.
Publicado: (2025)
por: Theisen, Maximilian F., et al.
Publicado: (2025)
Financial Fraud Detection using Quantum Graph Neural Networks
por: Innan, Nouhaila, et al.
Publicado: (2023)
por: Innan, Nouhaila, et al.
Publicado: (2023)
Detection of Odor Presence via Deep Neural Networks
por: Hassanloo, Matin, et al.
Publicado: (2025)
por: Hassanloo, Matin, et al.
Publicado: (2025)
Evaluating Neural Networks for Early Maritime Threat Detection
por: Tella, Dhanush, et al.
Publicado: (2024)
por: Tella, Dhanush, et al.
Publicado: (2024)
Quasi-Random Physics-informed Neural Networks
por: Yu, Tianchi, et al.
Publicado: (2025)
por: Yu, Tianchi, et al.
Publicado: (2025)
Dual-Branched Spatio-temporal Fusion Network for Multi-horizon Tropical Cyclone Track Forecast
por: Liu, Zili, et al.
Publicado: (2022)
por: Liu, Zili, et al.
Publicado: (2022)
Unified Sparse-Matrix Representations for Diverse Neural Architectures
por: Zhu, Yuzhou
Publicado: (2025)
por: Zhu, Yuzhou
Publicado: (2025)
Conjugate Learning Theory: Uncovering the Mechanisms of Trainability and Generalization in Deep Neural Networks
por: Qi, Binchuan
Publicado: (2026)
por: Qi, Binchuan
Publicado: (2026)
Comprehensive Evaluation of Prototype Neural Networks
por: Schlinge, Philipp, et al.
Publicado: (2025)
por: Schlinge, Philipp, et al.
Publicado: (2025)
Reinforcement Learning Based Escape Route Generation in Low Visibility Environments
por: Srikanth, Hari
Publicado: (2024)
por: Srikanth, Hari
Publicado: (2024)
Detecting High-Potential SMEs with Heterogeneous Graph Neural Networks
por: Qi, Yijiashun, et al.
Publicado: (2026)
por: Qi, Yijiashun, et al.
Publicado: (2026)
Ejemplares similares
-
Late-Stage Generalization Collapse in Grokking: Detecting anti-grokking with Weightwatcher
por: Prakash, Hari K, et al.
Publicado: (2026) -
Grokking and Generalization Collapse: Insights from \texttt{HTSR} theory
por: Prakash, Hari K., et al.
Publicado: (2025) -
Preventing overfitting in deep learning using differential privacy
por: Khatri, Alizishaan Anwar Hussein
Publicado: (2026) -
Be aware of overfitting by hyperparameter optimization!
por: Tetko, Igor V., et al.
Publicado: (2024) -
Spectral Geometry for Deep Learning: Compression and Hallucination Detection via Random Matrix Theory
por: Ettori, Davide
Publicado: (2026)