Learned Neural Network Optimizers Using Historical Gradient States
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Solution Overview
Problem
Existing neural network training methods are inefficient and resource-intensive, particularly in determining optimal parameter updates, leading to suboptimal performance and increased computational requirements.
Innovation Solution
Implementing a per-parameter optimizer neural network that processes optimizer network inputs to generate parameter updates for each network parameter, utilizing historical gradient data to improve training efficiency and performance, and leveraging parallel processing hardware for efficient execution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If hand-designed optimization rules are used to train neural networks, then the training process is simple to implement, but the training efficiency and adaptability to specific tasks are poor
Solution Approach 1:
The optimizer neural network learns to optimize itself through self-supervised training on historical gradient data. The system automatically adapts optimization strategies without external intervention, allowing the optimizer to improve training efficiency while maintaining ease of implementation through automated hyperparameter learning.
Solution Approach 2:
The patent transforms fixed hand-designed optimization parameters into learnable parameters of the neural network. By representing optimization hyperparameters as neural network outputs that are trained on historical performance data, the system achieves adaptive parameter adjustment that improves training efficiency while keeping the implementation framework simple.
2Device complexity
If hand-designed optimization rules are used, then the system complexity is low, but the adaptability to different architectures and tasks is limited
Solution Approach 1:
The optimizer neural network is designed with universal applicability across different neural network architectures and tasks. By training on diverse historical gradient data from various sources and using architecture-agnostic optimization strategies, the single optimizer network can adapt to multiple different trainee networks without requiring architecture-specific customization.
Solution Approach 2:
The system incorporates feedback loops where the optimizer neural network receives performance feedback from the trainee network's training progress and adjusts its parameters accordingly. This feedback mechanism enables the optimizer to adapt to different architectures and tasks by learning from historical performance patterns while maintaining a unified system structure.
3Use of energy by moving object
If traditional optimizers are used, then the computational resources required are minimal, but the training time is excessive
Solution Approach 1:
The optimizer neural network performs preliminary learning on historical gradient data before actual training begins. By pre-learning optimization patterns from historical data, the network is prepared to make more efficient parameter updates during actual training, reducing the number of iterations needed and thereby reducing training time while maintaining reasonable computational resource usage.
Solution Approach 2:
The patent replaces traditional mechanical optimization algorithms with a neural network-based learning system. This substitution allows the optimizer to capture complex patterns in gradient data that traditional algorithms miss, leading to more efficient convergence and reduced training time, while the neural network's parallel processing capabilities help manage computational resource requirements.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training neural networks using learned optimizers. One of method is for training a neural network layer comprising a plurality of network parameters having a plurality of dimensions each having a plurality of indices, the method comprising: maintaining a set of values corresponding to respective sets of indices of each dimension, each value representing a measure of central tendency of past gradients of the network parameters having an index in the dimension that is in the set of indices; performing a training step to obtain a new gradient for each network parameter; updating each set of values using the new gradients; and for each network parameter: generating an input from the updated sets of values; processing the input using an optimizer neural network to generate an output defining an update for the network parameter; and applying the update.