Neural Network Weight-Space Geodesics for Rapid Damage Recovery
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The resilience of artificial neural networks is poorly understood, and autonomous recovery algorithms have yet to be developed, necessitating the need for systems with resilience and rapid-recovery routines to enable deployment in critical applications.
Innovation Solution
A method for updating neural network weights using a geodesic path in the weight space, determined by a geodesic equation or its approximation, to generate an updated neural network, with the use of Riemannian or pseudo-Riemannian metrics, and positive semi-definite or definite symmetric matrices, allowing for resilience and recovery from damage.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional retraining is used to recover from damage, then network performance can be restored, but the time and computational resources required are excessive
Solution Approach 1:
The patent pre-computes geodesic paths in weight space from the initial trained weights to various damaged weight configurations before actual damage occurs. When damage happens, the system simply follows the pre-computed path to the appropriate damaged state, avoiding the need for time-consuming retraining. This preliminary preparation enables rapid recovery by having recovery trajectories ready in advance.
Solution Approach 2:
The patent replaces the traditional gradient descent optimization mechanism (mechanical iterative adjustment) with a geometric approach using geodesic paths in Riemannian manifold weight space. Instead of iteratively adjusting weights through multiple training epochs, the system uses closed-form geometric calculations to directly determine the optimal weight adjustments, dramatically reducing recovery time.
2Productivity
If autonomous recovery algorithms are implemented, then rapid recovery is achieved, but the system complexity increases
Solution Approach 1:
The patent implements autonomous recovery where the neural network automatically detects weight damage, identifies the appropriate geodesic path in weight space, and adjusts its own undamaged weights without external intervention. The system serves itself by having built-in recovery capabilities that activate automatically upon damage detection, eliminating the need for complex external recovery systems.
Solution Approach 2:
The patent changes the parameter space from standard Euclidean weight space to a Riemannian manifold with a specifically designed metric tensor. This parameter transformation allows the use of geodesic equations that naturally account for the curvature of the loss landscape, enabling simpler autonomous recovery algorithms that work effectively without requiring complex adaptive mechanisms.
3Reliability
If all weights are protected from damage, then network resilience improves, but the cost and complexity of protection mechanisms increase
Solution Approach 1:
The patent extracts and identifies only the damaged weights from the network, rather than protecting or processing all weights uniformly. By selectively detecting and isolating the subset of damaged weights, the system can focus recovery efforts only where needed, reducing the complexity of protection and recovery mechanisms while maintaining overall network resilience.
Solution Approach 2:
The patent applies partial action by recovering only the necessary portion of the network (undamaged weights adjusted along geodesic paths) rather than retraining the entire network. This partial recovery approach achieves sufficient resilience with less complexity and computational resources than complete retraining or comprehensive protection of all weights.
Data Source
AI summary
Disclosed herein include systems, devices, computer readable media, and methods for resilience determination and damage recovery in neural networks using a weight space and a metric that together form a manifold (such as a pseudo-Riemannian manifold or a Riemannian manifold).