Controller Neural Network Update Rules for Task-Specific Training
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Solution Overview
Problem
Conventional neural network training techniques use pre-determined update rules that are not tailored to the specific task, leading to inefficient use of computational resources and suboptimal performance.
Innovation Solution
A system that uses a controller neural network to determine a tailored update rule for neural network training by exploring a large space of possible rules through reinforcement learning, generating a domain-specific language string to define the update rule, and evaluating its effectiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If pre-determined update rules are used for neural network training, then the training process is simple and easy to implement, but the training efficiency is low and computational resources are wasted
Solution Approach 1:
The patent applies dynamics by transitioning from static, pre-determined update rules to dynamic, task-adaptive update rules. A controller neural network is trained to generate update rules that adapt to specific neural network tasks, allowing the update rule to change based on the task requirements rather than remaining fixed. This resolves the contradiction by making the system more complex in structure (adding a controller network) but more efficient in operation (faster convergence, better resource utilization).
Solution Approach 2:
The patent changes the parameters of the update rule generation process by using a trained controller neural network to output task-specific parameters. Instead of using fixed update rule parameters, the system learns optimal parameters through training the controller network on task performance feedback, thereby improving training efficiency while maintaining ease of implementation through automated parameter selection.
2Productivity
If task-specific update rules are determined through reinforcement learning, then training efficiency and performance are improved, but the system complexity increases
Solution Approach 1:
The patent introduces an intermediary component - the controller neural network - that mediates between the task description and the update rule generation. This intermediary network absorbs the complexity of reinforcement learning by being pre-trained offline, while the online training process remains simple. The controller network acts as a bridge that translates task requirements into optimized update rules without requiring complex real-time computations during training.
Solution Approach 2:
The patent applies preliminary action by training the controller neural network offline before actual task execution. The reinforcement learning process is performed in advance to learn task-specific update rules, and these learned rules are then applied during normal training operations. This separates the complex learning phase from the simpler application phase, reducing system complexity during operational use while maintaining high training efficiency.
3Use of energy by stationary object
If conventional pre-determined update rules are used, then computational resources are used consistently, but the training requires more iterations and time
Solution Approach 1:
The patent changes the parameters of resource allocation by using task-adaptive update rules that optimize computational resource usage based on specific task requirements. The controller neural network learns to generate update rules that converge faster for different task types, reducing the number of iterations needed and thereby reducing both computational resource consumption and training time simultaneously.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining update rules for training neural networks. One of the methods includes generating, using a controller neural network, a batch of output sequences, each output sequence in the batch defining a respective update rule; for each output sequence in the batch: training a respective instance of a child neural network using the update rule defined by the output sequence; evaluating a performance of the trained instance of the child neural network on the particular neural network task to determine a performance metric for the trained instance of the child neural network on the particular neural network task; and using the performance metrics for the trained instances of the child neural network to adjust the current values of the controller parameters of the controller neural network.