Cover Image

Asymptotic generalization error of a single-layer graph convolutional network

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Duranthon, O., Zdeborová, L.
Format:	Preprint
Published:	2024
Subjects:	Machine Learning Disordered Systems and Neural Networks
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model
by: Duranthon, O., et al.
Published: (2025)

Specialization of softmax attention heads: insights from the high-dimensional single-location model
by: Sagitova, M., et al.
Published: (2026)

Statistical Advantage of Softmax Attention: Insights from Single-Location Regression
by: Duranthon, O., et al.
Published: (2025)

Asymptotics of feature learning in two-layer networks after one gradient-step
by: Cui, Hugo, et al.
Published: (2024)

High-dimensional Asymptotics of Denoising Autoencoders
by: Cui, Hugo, et al.
Published: (2023)

Homophily modulates double descent generalization in graph convolution networks
by: Shi, Cheng, et al.
Published: (2022)

Learning with Restricted Boltzmann Machines: Asymptotics of AMP and GD in High Dimensions
by: Xu, Yizhou, et al.
Published: (2025)

Asymptotics of SGD in Sequence-Single Index Models and Single-Layer Attention Networks
by: Arnaboldi, Luca, et al.
Published: (2025)

Optimal thresholds and algorithms for a model of multi-modal learning in high dimensions
by: Keup, Christian, et al.
Published: (2024)

Rigorous Asymptotics for First-Order Algorithms Through the Dynamical Cavity Method
by: Dandi, Yatin, et al.
Published: (2026)

Building Conformal Prediction Intervals with Approximate Message Passing
by: Clarté, Lucas, et al.
Published: (2024)

Fundamental limits of learning in sequence multi-index models and deep attention networks: High-dimensional asymptotics and sharp thresholds
by: Troiani, Emanuele, et al.
Published: (2025)

The Nuclear Route: Sharp Asymptotics of ERM in Overparameterized Quadratic Networks
by: Erba, Vittorio, et al.
Published: (2025)

The Rules-and-Facts Model for Simultaneous Generalization and Memorization in Neural Networks
by: Farné, Gabriele, et al.
Published: (2026)

Computational Thresholds in Multi-Modal Learning via the Spiked Matrix-Tensor Model
by: Tabanelli, Hugo, et al.
Published: (2025)

A solvable high-dimensional model where nonlinear autoencoders learn structure invisible to PCA while test loss misaligns with generalization
by: Mendes, Vicente Conde, et al.
Published: (2026)

Fundamental Limits of Matrix Sensing: Exact Asymptotics, Universality, and Applications
by: Xu, Yizhou, et al.
Published: (2025)

Sampling with flows, diffusion and autoregressive neural networks: A spin-glass perspective
by: Ghio, Davide, et al.
Published: (2023)

Bilinear Sequence Regression: A Model for Learning from Long Sequences of High-dimensional Tokens
by: Erba, Vittorio, et al.
Published: (2024)

The committee machine: Computational to statistical gaps in learning a two-layers neural network
by: Aubin, Benjamin, et al.
Published: (2018)

Spectral Thresholds in Correlated Spiked Models and Fundamental Limits of Partial Least Squares
by: Mergny, Pierre, et al.
Published: (2025)

Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression
by: Clarté, Lucas, et al.
Published: (2024)

Training neural networks with structured noise improves classification and generalization
by: Benedetti, Marco, et al.
Published: (2023)

Properties of the geometry of solutions and capacity of multi-layer neural networks with Rectified Linear Units activations
by: Baldassi, Carlo, et al.
Published: (2019)

On the existence of consistent adversarial attacks in high-dimensional linear classification
by: Vilucchio, Matteo, et al.
Published: (2025)

Solution space and storage capacity of fully connected two-layer neural networks with generic activation functions
by: Nishiyama, Sota, et al.
Published: (2024)

Sharp description of local minima in the loss landscape of high-dimensional two-layer ReLU neural networks
by: Huang, Jie, et al.
Published: (2026)

Exact full-RSB SAT/UNSAT transition in infinitely wide two-layer neural networks
by: Annesi, Brandon L., et al.
Published: (2024)

What can we learn from quantum convolutional neural networks?
by: Umeano, Chukwudubem, et al.
Published: (2023)

Bayes-optimal learning of an extensive-width neural network from quadratically many samples
by: Maillard, Antoine, et al.
Published: (2024)

Asymptotic theory of in-context learning by linear attention
by: Lu, Yue M., et al.
Published: (2024)

Bayes optimal learning of attention-indexed models
by: Boncoraglio, Fabrizio, et al.
Published: (2025)

Finite-time Lyapunov exponents of deep neural networks
by: Storm, L., et al.
Published: (2023)

In-context denoising with one-layer transformers: connections between attention and associative memory retrieval
by: Smart, Matthew, et al.
Published: (2025)

Fundamental computational limits of weak learnability in high-dimensional multi-index models
by: Troiani, Emanuele, et al.
Published: (2024)

Single-Head Attention in High Dimensions: A Theory of Generalization, Weights Spectra, and Scaling Laws
by: Boncoraglio, Fabrizio, et al.
Published: (2025)

Regularization, early-stopping and dreaming: a Hopfield-like setup to address generalization and overfitting
by: Agliari, Elena, et al.
Published: (2023)

High-dimensional learning of narrow neural networks
by: Cui, Hugo
Published: (2024)

Emergent weight morphologies in deep neural networks
by: de Jong, Pascal, et al.
Published: (2025)

A universal approximation theorem for nonlinear resistive networks
by: Scellier, Benjamin, et al.
Published: (2023)