Neural Network Optimizer for Irregular Loss Landscapes
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Solution Overview
Problem
Existing optimization techniques for neural networks face challenges such as getting stuck in local minima, requiring domain-specific hand-crafted functions, and needing extensive training time and resources, especially when dealing with irregular loss functions and complex optimization problems.
Innovation Solution
The implementation of an optimizer neural network that processes inner parameters at multiple time steps to generate updates, allowing for the training of a single neural network to solve various optimization problems efficiently, using techniques like evolutionary strategies and genetic algorithms to navigate complex loss landscapes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard optimization techniques (e.g., gradient descent) are used to minimize loss functions, then optimization can proceed efficiently on smooth loss surfaces, but the optimizer becomes stuck in local minima when the loss function has many local minima and high-loss barriers
Solution Approach 1:
The patent transforms the optimization problem by changing parameters from direct loss minimization to training an optimizer neural network that predicts parameter updates. The optimizer network is trained using evolutionary strategies that can escape local minima by accepting temporary loss increases, thereby resolving the contradiction between reliable convergence and adaptability to irregular loss landscapes.
Solution Approach 2:
The patent replaces traditional mechanical optimization algorithms (gradient descent, Adam, etc.) with a neural network-based optimizer. This substitution allows the system to learn complex optimization patterns from data and adapt to various loss function characteristics, achieving both reliability and versatility simultaneously.
2Reliability
If domain experts hand-craft optimization functions and parameters for each use case, then optimization can be tailored to specific problems, but the process requires significant expert time and cannot be easily applied to new problems
Solution Approach 1:
The patent creates a universal optimizer neural network that can be trained on one set of optimization problems and then applied to multiple different problems without retraining or fine-tuning. The optimizer network learns general optimization strategies that transfer across domains, achieving both specialized performance and broad applicability.
Solution Approach 2:
The patent uses evolutionary strategies to generate and evaluate multiple candidate optimizer networks, copying and mutating network parameters to discover optimal configurations. This copying mechanism allows the system to explore the solution space efficiently and find optimizers that generalize well across different problems.
3Reliability
If existing techniques train models to perform optimization, then the models can solve optimization problems, but the training requires extremely long training times and large computational resources
Solution Approach 1:
The patent pre-trains the optimizer neural network using evolutionary strategies on a diverse set of optimization problems before deployment. This preliminary training equips the optimizer with general-purpose optimization capabilities that can be immediately applied to new problems without requiring extensive problem-specific training, thereby improving productivity while maintaining reliability.
4Reliability
If existing optimization models are used, then they can perform optimization tasks, but they require significantly more training time and computational resources while achieving lower performance
Solution Approach 1:
The patent implements a self-improving optimizer where the neural network learns to generate its own training data and optimize its own parameters through evolutionary strategies. This self-service approach allows the optimizer to efficiently adapt to different problems without requiring extensive external training resources, achieving high performance with reduced training time.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing optimization using an optimizer neural network. One of the methods includes for each optimizer network parameter, randomly sampling a perturbation value; generating a plurality of sets of candidate values for the optimizer network parameters, for each set of candidate values of the optimizer network parameters: determining a respective loss value representing a performance of the optimizer neural network in updating one or more sets of inner parameters in accordance with the set of candidate of values of the optimizer network parameters; and updating the current values of the optimizer network parameters based on the loss values for the plurality of sets of candidate values of the optimizer network parameters.