Adaptive Neural Network Structure Learning via Dynamic Neuron Pruning
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Solution Overview
Problem
Existing machine learning models face inefficiencies in training neural networks, as they either fix the structure and optimize parameters or train multiple networks with different structures from scratch, leading to suboptimal performance and resource consumption.
Innovation Solution
A system that jointly learns the structure and parameters of neural networks using a shrinking and expansion engine, which adaptively adjusts the number of neurons and adds new ones based on performance and resource constraints, generating multiple neural networks with varying structures and parameters optimized for specific conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the neural network structure is fixed and only parameters are optimized, then the training process is simpler and faster, but the prediction accuracy and performance are suboptimal
Solution Approach 1:
The patent applies dynamics by making the neural network structure adaptive rather than fixed. The system dynamically adjusts the network structure during training by adding or removing neurons based on their contribution to performance, allowing the architecture to evolve and optimize itself for the specific task at hand.
Solution Approach 2:
The system implements self-service through automatic structure optimization. The training process automatically determines the optimal network architecture by evaluating neuron contributions and adjusting the structure accordingly, eliminating the need for manual architecture design and selection.
2Measurement precision
If multiple neural networks with different structures are trained from scratch, then the best structure can be found, but the resource consumption and training time increase significantly
Solution Approach 1:
The patent applies preliminary action by starting with a predefined neural network structure before training begins. This initial structure serves as a foundation that is then refined during training, avoiding the need to train multiple networks from scratch while still enabling structure optimization.
Solution Approach 2:
The system changes parameters by dynamically modifying the network structure (number of neurons, connections) during training based on performance metrics. This allows the architecture to adapt to the task requirements without requiring multiple separate training processes.
3Productivity
If the neural network structure is simplified to reduce resource usage, then training and inference become faster, but the prediction accuracy decreases
Solution Approach 1:
The patent applies parameter changes by dynamically adjusting the network structure parameters (number of neurons, connectivity) during training based on performance feedback. This allows the system to find the optimal balance between model complexity and accuracy for each specific task.
Solution Approach 2:
The system implements feedback mechanisms where the performance of the neural network is continuously evaluated during training, and this feedback is used to automatically adjust the network structure by adding or removing neurons, creating a closed-loop optimization process.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training neural networks. In one aspect, a system includes a neural network shrinking engine that is configured to receive a neural network being trained and generate a reduced neural network by a shrinking process. The shrinking process includes training the neural network based on a shrinking engine loss function that includes terms penalizing active neurons of the neural network and removing inactive neurons from the neural network. The system includes a neural network expansion engine that is configured to receive the neural network being trained and generate an expanded neural network by an expansion process including adding new neurons to the neural network and training the neural network based on an expanding engine loss function. The system includes a training subsystem that generates reduced neural networks and expanded neural networks.