Cover Image

Boundary between noise and information applied to filtering neural network weight matrices

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Staats, Max, Thamm, Matthias, Rosenow, Bernd
Format:	Preprint
Published:	2022
Subjects:	Disordered Systems and Neural Networks Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Small Singular Values Matter: A Random Matrix Analysis of Transformer Models
by: Staats, Max, et al.
Published: (2024)

Enhancing Noise-Robust Losses for Large-Scale Noisy Data Learning
by: Staats, Max, et al.
Published: (2023)

A Boundary-Layer Mechanism for One-Third Scaling in Online Softmax Classification
by: Kühn, Marcel, et al.
Published: (2026)

Anti-Correlated Noise in Epoch-Based Stochastic Gradient Descent: Implications for Weight Variances in Flat Directions
by: Kühn, Marcel, et al.
Published: (2023)

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

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

Unified Description of Learning Dynamics in the Soft Committee Machine from Finite to Ultra-Wide Regimes
by: Afanah, Assem, et al.
Published: (2025)

Continuous Specialization Transition in the Soft Committee Machine with ReLU Activation
by: Afanah, Assem, et al.
Published: (2026)

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

BBP Phase Transition for an Extensive Number of Outliers
by: Forner, Niklas, et al.
Published: (2025)

Supervised and Unsupervised protocols for hetero-associative neural networks
by: Alessandrelli, Andrea, et al.
Published: (2025)

Deep neural networks from the perspective of ergodic theory
by: Zhang, Fan
Published: (2023)

Computing frustration and near-monotonicity in deep neural networks
by: Wendin, Joel, et al.
Published: (2025)

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

How does training shape the Riemannian geometry of neural network representations?
by: Zavatone-Veth, Jacob A., et al.
Published: (2023)

Adaptive kernel predictors from feature-learning infinite limits of neural networks
by: Lauditi, Clarissa, et al.
Published: (2025)

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

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)

Implicit bias produces neural scaling laws in learning curves, from perceptrons to deep networks
by: D'Amico, Francesco, et al.
Published: (2025)

How noise affects memory in linear recurrent networks
by: Guan, JingChuan, et al.
Published: (2024)

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)

Pruning-induced phases in fully-connected neural networks: the eumentia, the dementia, and the amentia
by: Pan, Haining, et al.
Published: (2026)

Non-equilibrium active noise enhances generative memory in diffusion models
by: Behera, Agnish Kumar, et al.
Published: (2024)

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

Role of scrambling and noise in temporal information processing with quantum systems
by: Xiong, Weijie, et al.
Published: (2025)

Coding schemes in neural networks learning classification tasks
by: van Meegen, Alexander, et al.
Published: (2024)

New non-Euclidean neural quantum states from additional types of hyperbolic recurrent neural networks
by: Dao, H. L.
Published: (2026)

Spring-block theory of feature learning in deep neural networks
by: Shi, Cheng, et al.
Published: (2024)

On the role of non-linear latent features in bipartite generative neural networks
by: Bonnaire, Tony, et al.
Published: (2025)

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

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

The Quantization Model of Neural Scaling
by: Michaud, Eric J., et al.
Published: (2023)

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

Phase transitions from linear to nonlinear information processing in neural networks
by: Matsumura, Masaya, et al.
Published: (2025)

Self-attention as an attractor network: transient memories without backpropagation
by: D'Amico, Francesco, et al.
Published: (2024)

Towards a theory of how the structure of language is acquired by deep neural networks
by: Cagnetta, Francesco, et al.
Published: (2024)

The autoregressive neural network architecture of the Boltzmann distribution of pairwise interacting spins systems
by: Biazzo, Indaco
Published: (2023)

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

The twin peaks of learning neural networks
by: Demyanenko, Elizaveta, et al.
Published: (2024)