Knowledge-Sharing Neural Networks for Interpretable Node Activation
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Solution Overview
Problem
Existing deep neural networks lack mechanisms for interpreting node activation patterns and communicating this knowledge to humans or other nodes, and there is no method to deliberately train nodes for improved interpretability.
Innovation Solution
Implement data-dependent node-to-node knowledge sharing through knowledge sharing links, regulated by a cooperative human-AI learning supervisor system, to guide nodes towards more interpretable activation patterns.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If deep neural networks are used to achieve high performance, then accuracy and productivity are improved, but interpretability and ease of operation deteriorate
Solution Approach 1:
The patent implements feedback mechanisms where activation patterns from hidden nodes are fed back to guide the training process. The system uses interpretability signals from node activations to adjust weights and biases, creating a loop where performance and interpretability reinforce each other through continuous feedback during training.
Solution Approach 2:
The patent changes training parameters dynamically based on activation patterns. By adjusting learning rates, weight decay, and regularization parameters according to the interpretability of node activations, the system optimizes both performance and interpretability through parameter adaptation during the training process.
2Productivity
If nodes are trained to achieve high accuracy, then productivity is improved, but the ability to communicate knowledge to humans and other nodes deteriorates
Solution Approach 1:
The patent introduces intermediary nodes that serve as bridges between high-level accurate processing and human-understandable representations. These intermediary nodes are specifically designed to translate complex internal representations into more interpretable forms that can be communicated to humans or other nodes in the system.
Solution Approach 2:
The patent segments the neural network into functional modules where different nodes perform specific interpretability functions. By dividing the network into segments that handle accuracy versus interpretability, the system can optimize each segment for its specific purpose while maintaining overall system performance.
3Productivity
If nodes are trained independently, then productivity is improved, but the ability to share knowledge across the network deteriorates
Solution Approach 1:
The patent merges independent node training with knowledge sharing by implementing mechanisms where nodes share learned representations. Through weight sharing and collaborative training, nodes maintain individual training efficiency while simultaneously benefiting from collective knowledge, reducing overfitting through distributed learning.
Solution Approach 2:
The patent uses parameter changes to balance independent training with knowledge sharing. By dynamically adjusting regularization parameters and weight decay based on node relationships, the system enables nodes to train independently while sharing knowledge, preventing overfitting through controlled parameter adaptation.
4Ease of operation
If nodes are trained to be interpretable, then ease of operation is improved, but the network complexity increases
Solution Approach 1:
The patent applies local quality by making only specific nodes interpretable rather than the entire network. By selectively training and making interpretable only the necessary nodes for a given task, the system maintains interpretability where needed while keeping the rest of the network optimized for performance, thus managing overall complexity.
Solution Approach 2:
The patent segments interpretability into specific functional regions of the network. By dividing the network into interpretable and non-interpretable segments, the system provides interpretability only where necessary for specific tasks, avoiding the complexity of making the entire network interpretable while maintaining performance.
Data Source
AI summary
Data-dependent node-to-node knowledge sharing to increase the interpretability of the activation pattern of one or more nodes in a neural network, is implemented by a set of knowledge sharing links. Each link may comprise a knowledge providing node or other source P and a knowledge receiving node R. A knowledge sharing link can impose a node-specific regularization on the knowledge receiving node R to help guide the knowledge receiving node R to have an activation pattern that is more easily interpreted. The specification and training of the knowledge sharing links may be controlled by a cooperative human-AI learning supervisor system in which a human and an artificial intelligence system work cooperatively to improve the interpretability and performance of the client system.