Emergent weight morphologies in deep neural networks

2025-01-09

AI Summary

This work investigates whether input-agnostic, spontaneously emerging weight structures exist during deep neural network training and examines their implications for model performance and safety. Employing a synergistic methodology combining nonlinear stability analysis, continuum approximation modeling, and large-scale numerical experiments across diverse architectures (CNNs and MLPs) and multiple benchmark datasets, the study provides the first theoretical and empirical evidence of a destabilization phase transition in weight reconstruction—leading to the spontaneous formation of periodic channel structures: a data-agnostic, emergent geometric pattern. This finding challenges the conventional data-driven interpretability paradigm and offers a novel dynamical systems perspective on deep learning. Moreover, the revealed structural–performance correlation establishes a foundational framework for analyzing fundamental capacity limits and designing robust, architecture-aware models.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Computer Vision: Other Foundations of Computer Vision
• Natural Language Processing: Safety and Robustness

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Web Mining and Content Analysis: Models for Web evolution
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

Whether deep neural networks can exhibit emergent behaviour is not only relevant for understanding how deep learning works, it is also pivotal for estimating potential security risks of increasingly capable artificial intelligence systems. Here, we show that training deep neural networks gives rise to emergent weight morphologies independent of the training data. Specifically, in analogy to condensed matter physics, we derive a theory that predict that the homogeneous state of deep neural networks is unstable in a way that leads to the emergence of periodic channel structures. We verified these structures by performing numerical experiments on a variety of data sets. Our work demonstrates emergence in the training of deep neural networks, which impacts the achievable performance of deep neural networks.

Problem

• Deep Neural Networks
• Weight Patterns
• AI Security

Innovation

• Deep Neural Networks
• Inherent Instability
• Self-generating Features