Neural Network Regularization Links for Faster Continual Learning
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Solution Overview
Problem
Current machine learning systems, particularly deep neural networks, face challenges in continual or lifelong learning, including forgetting previous tasks, slow learning rates, and difficulty in generalizing to new data. They also struggle with ease of interpretation, robustness against perturbations and adversarial attacks, and sensible decision boundaries.
Innovation Solution
The approach of 'targeted incremental growth' for neural networks allows for efficient training and continual learning by adding nodes and arcs to the network while using node-to-node relationship regularization. This method targets individual nodes or data points to correct errors or accelerate training, maintaining previous performance levels and enhancing interpretability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If nodes and arcs are added to the network for continual learning, then the ability to learn new tasks is improved, but the network complexity increases
Solution Approach 1:
The patent segments the neural network into modular components (nodes and arcs) that can be independently added and trained. Each new task is learned by adding specific nodes and arcs rather than retraining the entire network, enabling incremental growth while maintaining manageability of complexity.
Solution Approach 2:
The network structure is made dynamic through incremental growth, where nodes and arcs are added based on learning needs. The regularization parameters are also dynamically adjusted during training to balance plasticity (ability to learn new tasks) and stability (retention of previous knowledge), resolving the contradiction between adaptability and complexity.
2Reliability
If regularization strength is increased to maintain previous performance, then knowledge retention is improved, but the learning speed of new tasks decreases
Solution Approach 1:
The patent employs periodic adjustment of regularization strength during the training process. The regularization parameter lambda is cyclically modified to alternate between phases of knowledge retention (higher regularization) and new task learning (lower regularization), enabling the network to achieve both reliability and productivity over time.
Solution Approach 2:
Before training new tasks, the patent preliminarily adjusts the regularization strength to optimal levels. This preliminary action prepares the network by temporarily reducing regularization to accelerate initial learning, then gradually increasing it to consolidate knowledge, thereby optimizing both learning speed and retention.
3Reliability
If the network is trained from scratch, then robustness is improved, but training time increases
Solution Approach 1:
The patent applies preliminary training to foundational nodes and arcs before adding new task-specific components. This preliminary action establishes a robust base network that can be efficiently extended, avoiding the need to train the entire network from scratch while maintaining robustness through progressive refinement.
Solution Approach 2:
The patent maintains continuity of useful action by continuously training and refining network components as new tasks are added. Rather than periodic retraining from scratch, the system continuously improves the network through incremental learning, reducing total training time while maintaining robustness through ongoing optimization.
Data Source
AI summary
Computer systems and computer-implemented methods train a neural network, by:(a) computing for each datum in a set of training data, activation values for nodes in the neural network and estimates of partial derivatives of an objective function for the neural network for the nodes in the neural network; (b) selecting a target node of the neural network and/or a target datum in the set of training data; (c) selecting a target-specific improvement model for the neural network, wherein the target-specific improvement model, when added to the neural network, improves performance of the neural network for the target node and/or the target datum, as the case may be; (d) training the target-specific improvement model; (e) merging the target-specific improvement model with the neural network to form an expanded neural network; and (f) training the expanded neural network.