Cover Image

Phase transitions reveal hierarchical structure in deep neural networks

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ersoy, Ibrahim Talha, Licha, Andrés Fernando Cardozo, Wiesner, Karoline
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Data Analysis, Statistics and Probability
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Phase Transitions between Accuracy Regimes in L2 regularized Deep Neural Networks
by: Ersoy, Ibrahim Talha, et al.
Published: (2025)

Fisher information flow in artificial neural networks
by: Weimar, Maximilian, et al.
Published: (2025)

Similarity Learning with neural networks
by: Sanfins, Gabriel, et al.
Published: (2024)

Physics-informed neural operators for the in situ characterization of locally reacting sound absorbers
by: Schmid, Jonas M., et al.
Published: (2026)

Exploring how deep learning decodes anomalous diffusion via Grad-CAM
by: Bae, Jaeyong, et al.
Published: (2024)

Uncertainty estimation via ensembles of deep learning models and dropout layers for seismic traces
by: Messuti, Giovanni, et al.
Published: (2024)

Learning from the past: predicting critical transitions with machine learning trained on surrogates of historical data
by: Ma, Zhiqin, et al.
Published: (2024)

Scintillation pulse characterization with spectrum-inspired temporal neural networks: case studies on particle detector signals
by: Ai, Pengcheng, et al.
Published: (2024)

Conditional updates of neural network weights for increased out of training performance
by: Saynisch-Wagner, Jan, et al.
Published: (2025)

Low-power multi-mode fiber projector overcomes shallow neural networks classifiers
by: Ancora, Daniele, et al.
Published: (2022)

Tunable correlation retention: A statistical method for generating synthetic data
by: Jävergård, Nicklas, et al.
Published: (2024)

Statistical limits of correlation detection in trees
by: Ganassali, Luca, et al.
Published: (2022)

High dimensional analysis reveals conservative sharpening and a stochastic edge of stability
by: Agarwala, Atish, et al.
Published: (2024)

Data-driven topology design using a deep generative model
by: Yamasaki, Shintaro, et al.
Published: (2020)

Hierarchical geometric deep learning enables scalable analysis of molecular dynamics
by: Pengmei, Zihan, et al.
Published: (2025)

Training-set-free two-stage deep learning for spectroscopic data de-noising
by: Huang, Dongchen, et al.
Published: (2024)

An exact multiple-time-step variational formulation for the committor and the transition rate
by: Lorpaiboon, Chatipat, et al.
Published: (2025)

Scientific Data Compression and Super-Resolution Sampling
by: Vu, Minh, et al.
Published: (2025)

GAMMA_FLOW: Guided Analysis of Multi-label spectra by MAtrix Factorization for Lightweight Operational Workflows
by: Rädle, Viola, et al.
Published: (2025)

Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking)
by: Nam, Yoonsoo, et al.
Published: (2025)

Detail Across Scales: Multi-Scale Enhancement for Full Spectrum Neural Representations
by: Ni, Yuan, et al.
Published: (2025)

Reliable Statistical Guarantees for Conformal Predictors with Small Datasets
by: Sánchez-Domínguez, Miguel, et al.
Published: (2025)

Data augmentation using diffusion models to enhance inverse Ising inference
by: Lim, Yechan, et al.
Published: (2025)

Variational Garrote for Statistical Physics-based Sparse and Robust Variable Selection
by: Soh, Hyungjoon, et al.
Published: (2025)

SpecTUS: Spectral Translator for Unknown Structures annotation from EI-MS spectra
by: Hájek, Adam, et al.
Published: (2025)

Data-Driven Extreme Response Estimation
by: Edwards, Samuel J., et al.
Published: (2025)

Exploring the Limitations of kNN Noisy Feature Detection and Recovery for Self-Driving Labs
by: Shi, Qiuyu, et al.
Published: (2025)

Comparative study of machine learning and statistical methods for automatic identification and quantification in γ-ray spectrometry
by: Phan, Dinh Triem, et al.
Published: (2025)

Cooperative effects in feature importance of individual patterns: application to air pollutants and Alzheimer disease
by: Ontivero-Ortega, M., et al.
Published: (2025)

Probabilistic Emissivity Retrieval from Hyperspectral Data via Physics-Guided Variational Inference
by: Tempelman, Joshua R., et al.
Published: (2025)

Out-of-Sample Hydrocarbon Production Forecasting: Time Series Machine Learning using Productivity Index-Driven Features and Inductive Conformal Prediction
by: Idris, Mohamed Hassan Abdalla, et al.
Published: (2025)

Learnability Window in Gated Recurrent Neural Networks
by: Livi, Lorenzo
Published: (2025)

Short-Term Forecasting of Energy Production and Consumption Using Extreme Learning Machine: A Comprehensive MIMO based ELM Approach
by: Voyant, Cyril, et al.
Published: (2025)

Temporal Graph Neural Networks for Early Anomaly Detection and Performance Prediction via PV System Monitoring Data
by: Mukherjee, Srijani, et al.
Published: (2025)

The Ensemble Kalman Inversion Race
by: Gjini, Rebecca, et al.
Published: (2025)

OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions
by: Young, Chris, et al.
Published: (2025)

Stochastic Clock Attention for Aligning Continuous and Ordered Sequences
by: Soh, Hyungjoon, et al.
Published: (2025)

Information-theoretic Quantification of High-order Feature Effects in Classification Problems
by: Lazic, Ivan, et al.
Published: (2025)

Learning effective good variables from physical data
by: Barletta, Giulio, et al.
Published: (2024)

Development of a high-resolution indoor radon map using a new machine learning-based probabilistic model and German radon survey data
by: Petermann, Eric, et al.
Published: (2023)