Imagen de Portada

From Density Matrices to Phase Transitions in Deep Learning: Spectral Early Warnings and Interpretability

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Hennick, Max, Corlouer, Guillaume
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Artificial Intelligence
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Almost Bayesian: The Fractal Dynamics of Stochastic Gradient Descent
por: Hennick, Max, et al.
Publicado: (2025)

Research on Early Warning Model of Cardiovascular Disease Based on Computer Deep Learning
por: Hu, Yuxiang, et al.
Publicado: (2024)

Deep Learning for Disease Outbreak Prediction: A Robust Early Warning Signal for Transcritical Bifurcations
por: Miry, Reza, et al.
Publicado: (2025)

Early Warning of Intraoperative Adverse Events via Transformer-Driven Multi-Label Learning
por: Wang, Xueyao, et al.
Publicado: (2026)

Learning Displacement-Robust Representations for Landslide Early Warning under Rainfall Forecast Uncertainty
por: Ozeki, Ren, et al.
Publicado: (2026)

Learning with Density Matrices and Random Features
por: González, Fabio A., et al.
Publicado: (2021)

Early-Warning Signals of Grokking via Loss-Landscape Geometry
por: Xu, Yongzhong
Publicado: (2026)

Two-Valued Symmetric Circulant Matrices: Applications in Deep Learning
por: Amathi, Jayakrishna, et al.
Publicado: (2026)

Physics-Guided Tiny-Mamba Transformer for Reliability-Aware Early Fault Warning
por: Li, Changyu, et al.
Publicado: (2026)

An Attention-based Framework with Multistation Information for Earthquake Early Warnings
por: Huang, Yu-Ming, et al.
Publicado: (2024)

Grokking as a Variance-Limited Phase Transition: Spectral Gating and the Epsilon-Stability Threshold
por: Acharya, Pratyush, et al.
Publicado: (2026)

Spectral Edge Dynamics: An Analytical-Empirical Study of Phase Transitions in Neural Network Training
por: Xu, Yongzhong
Publicado: (2026)

Spectral Entropy Collapse as a Phase Transition in Delayed Generalisation: An Interventional and Predictive Framework for Grokkin
por: Khanh, Truong Xuan, et al.
Publicado: (2026)

Spectral Convolution on Orbifolds for Geometric Deep Learning
por: Mangliers, Tim, et al.
Publicado: (2026)

Anomaly Detection and Early Warning Mechanism for Intelligent Monitoring Systems in Multi-Cloud Environments Based on LLM
por: Jin, Yihong, et al.
Publicado: (2025)

The Confusion is Real: GRAPHIC -- A Network Science Approach to Confusion Matrices in Deep Learning
por: Fröhlich, Johanna S., et al.
Publicado: (2026)

Structured Sparse Transition Matrices to Enable State Tracking in State-Space Models
por: Terzić, Aleksandar, et al.
Publicado: (2025)

Approaching Deep Learning through the Spectral Dynamics of Weights
por: Yunis, David, et al.
Publicado: (2024)

Addressing Spectral Bias of Deep Neural Networks by Multi-Grade Deep Learning
por: Fang, Ronglong, et al.
Publicado: (2024)

Fairness is Not Flat: Geometric Phase Transitions Against Shortcut Learning
por: Rodriguez-Alvarez, Nicolas, et al.
Publicado: (2026)

Sparse Interpretable Deep Learning with LIES Networks for Symbolic Regression
por: Montazerin, Mansooreh, et al.
Publicado: (2025)

Admission Prediction in Undergraduate Applications: an Interpretable Deep Learning Approach
por: Priyadarshini, Amisha, et al.
Publicado: (2024)

DPCformer: An Interpretable Deep Learning Model for Genomic Prediction in Crops
por: Deng, Pengcheng, et al.
Publicado: (2025)

Stochastic Gradient Descent in the Saddle-to-Saddle Regime of Deep Linear Networks
por: Corlouer, Guillaume, et al.
Publicado: (2026)

Interpretable Operator Learning for Inverse Problems via Adaptive Spectral Filtering: Convergence and Discretization Invariance
por: Dong, Hang-Cheng, et al.
Publicado: (2026)

Towards Interpretable Deep Reinforcement Learning Models via Inverse Reinforcement Learning
por: Xie, Sean, et al.
Publicado: (2022)

Benchmarking Counterfactual Interpretability in Deep Learning Models for Time Series Classification
por: Kan, Ziwen, et al.
Publicado: (2024)

Early prediction of the risk of ICU mortality with Deep Federated Learning
por: Randl, Korbinian, et al.
Publicado: (2022)

From Eigenmodes to Proofs: Integrating Graph Spectral Operators with Symbolic Interpretable Reasoning
por: Kiruluta, Andrew, et al.
Publicado: (2025)

TransPL: VQ-Code Transition Matrices for Pseudo-Labeling of Time Series Unsupervised Domain Adaptation
por: Kim, Jaeho, et al.
Publicado: (2025)

Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm
por: Cheng, Yu, et al.
Publicado: (2024)

Systemic Risk Radar: A Multi-Layer Graph Framework for Early Market Crash Warning
por: Neela, Sandeep
Publicado: (2025)

From Order to Distribution: A Spectral Characterization of Forgetting in Continual Learning
por: Xu, Zonghuan, et al.
Publicado: (2026)

Interpretable Deep Reinforcement Learning for Element-level Bridge Life-cycle Optimization
por: Moayyedi, Seyyed Amirhossein, et al.
Publicado: (2026)

Reinfier and Reintrainer: Verification and Interpretation-Driven Safe Deep Reinforcement Learning Frameworks
por: Yang, Zixuan, et al.
Publicado: (2024)

ProtoNAM: Prototypical Neural Additive Models for Interpretable Deep Tabular Learning
por: Xiong, Guangzhi, et al.
Publicado: (2024)

Embracing Biased Transition Matrices for Complementary-Label Learning with Many Classes
por: Mai, Tan-Ha, et al.
Publicado: (2026)

BLAST: Block-Level Adaptive Structured Matrices for Efficient Deep Neural Network Inference
por: Lee, Changwoo, et al.
Publicado: (2024)

Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning
por: Weltevrede, Max, et al.
Publicado: (2024)

Bayesian Inverse Transition Learning: Learning Dynamics From Near-Optimal Trajectories
por: Benac, Leo, et al.
Publicado: (2024)