Neural Network Weight Space Path Traversal for Task Adaptation
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Solution Overview
Problem
Artificial neural networks lack the flexibility and robustness of human intelligence, struggling to adapt to changing tasks and goals without significant performance decay, and are vulnerable to adversarial attacks.
Innovation Solution
A method of generating neural networks by traversing functionally invariant paths in weight space, allowing networks to maintain performance on initial tasks while gaining performance on new tasks and enhancing robustness through the construction of path-connected sets of networks that accommodate secondary objectives like sparsification and adversarial resistance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If standard training methods are used to teach new tasks to neural networks, then new task performance is improved, but performance on initial tasks deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing a path through weight space that connects the initial network configuration to the target configuration. This path is computed before the actual task transfer, ensuring that intermediate configurations along the path maintain performance on initial tasks while progressively acquiring new task capabilities. The path computation involves solving an optimization problem that minimizes performance degradation on initial tasks while maximizing progress toward new task performance.
Solution Approach 2:
The patent implements dynamics by treating the network weights as dynamic variables that evolve continuously along a pre-computed trajectory. Instead of discrete weight updates that cause catastrophic forgetting, the system dynamically adjusts weights along a continuous path where each intermediate point represents a valid network configuration. This dynamic evolution allows the network to adapt to new tasks while maintaining stability on initial tasks through controlled interpolation between weight configurations.
2Measurement precision
If neural networks are designed for high performance on specific tasks, then task accuracy is improved, but robustness against adversarial attacks deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing a robustness-enhanced path through weight space that incorporates adversarial defense considerations before deployment. This path is computed in advance by optimizing for both task accuracy and adversarial robustness, ensuring that networks positioned along this path inherently possess both high accuracy and resistance to adversarial attacks. The pre-computation involves solving a multi-objective optimization problem that balances accuracy and robustness metrics.
Solution Approach 2:
The patent introduces an intermediary mechanism in the form of a path-computation algorithm that mediates between the conflicting objectives of task accuracy and adversarial robustness. This intermediary computes optimal weight configurations that satisfy both objectives simultaneously by navigating through the weight space along carefully constructed trajectories. The path computation acts as an intermediary process that transforms a network from high accuracy to high robustness while maintaining both properties at intermediate stages.
3Quantity of substance
If neural networks are pruned for compression, then model size is reduced, but performance deteriorates
Solution Approach 1:
The patent applies preliminary action by pre-computing a sparsity-aware path through weight space that guides the network toward compressed configurations while maintaining performance. Before actual pruning is applied, the system computes an optimal trajectory that passes through sparse regions of weight space, ensuring that performance is preserved throughout the compression process. This pre-computed path provides a roadmap for progressive pruning that avoids performance-critical weight configurations.
Solution Approach 2:
The patent implements parameter changes by systematically varying the sparsity parameter along a pre-computed path. Instead of abrupt pruning that causes performance collapse, the system continuously adjusts the sparsity level from 0% to the target percentage, with each intermediate value corresponding to a valid network configuration along the path. This continuous parameter change allows for controlled compression while monitoring and maintaining performance thresholds.
Data Source
AI summary
Disclosed herein include systems, devices, and methods for flexible machine learning by traversing functionally invariant paths in weight space.