Neural Network Architecture Selection Through Expressivity And Trainability
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Solution Overview
Problem
Selecting a high-performing neural network architecture for a given machine learning problem is challenging, leading to inefficiencies and unnecessary computational costs due to the training of non-optimal architectures.
Innovation Solution
A system and method that computes expressivity and trainability measures for untrained candidate neural networks to select the most suitable architecture, which is then trained to solve the machine learning problem, thereby reducing computational time and increasing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If multiple candidate neural network architectures are evaluated through training to find the optimal one, then the reliability of selecting the best architecture is improved, but the loss of time and computational resources increases significantly
Solution Approach 1:
The patent applies preliminary action by computing expressivity and trainability measures for candidate neural network architectures before actual training begins. These preliminary computations evaluate architectural properties such as gradient flow, weight initialization suitability, and potential for convergence, allowing the system to identify and eliminate poor-performing architectures in advance. This preliminary assessment reduces the need to train all candidate architectures, thereby saving significant computational time while maintaining reliable selection of high-performing architectures.
2Reliability
If multiple candidate neural network architectures are evaluated through training to find the optimal one, then the reliability of selecting the best architecture is improved, but the loss of energy and computational costs increases
Solution Approach 1:
The patent computes expressivity and trainability metrics as preliminary assessments before committing computational energy to full training cycles. These metrics evaluate architectural soundness including gradient propagation characteristics, weight initialization compatibility, and theoretical convergence properties. By performing these preliminary evaluations, the system avoids wasting computational energy on architectures with fundamental flaws, thereby reducing overall energy consumption while maintaining high reliability in architecture selection.
3Manufacturing precision
If comprehensive measures are taken to evaluate neural network architectures, then the manufacturing precision of architecture selection is improved, but the device complexity increases
Solution Approach 1:
The patent segments the architecture evaluation process into distinct computational modules: expressivity measurement (evaluating the network's ability to represent target functions), trainability measurement (assessing gradient flow and optimization landscape), and selection logic (comparing metrics against thresholds). This segmentation allows each aspect to be evaluated independently using specialized algorithms, improving selection precision while managing system complexity through modular design. Each module can be implemented separately and combined, making the overall system more manageable despite its comprehensive nature.
Data Source
AI summary
Systems and methods, for selecting a neural network for a machine learning (ML) problem, are disclosed. A method includes accessing an input matrix, and accessing an ML problem space associated with an ML problem and multiple untrained candidate neural networks for solving the ML problem. The method includes computing, for each untrained candidate neural network, at least one expressivity measure capturing an expressivity of the candidate neural network with respect to the ML problem. The method includes computing, for each untrained candidate neural network, at least one trainability measure capturing a trainability of the candidate neural network with respect to the ML problem. The method includes selecting, based on the at least one expressivity measure and the at least one trainability measure, at least one candidate neural network for solving the ML problem. The method includes providing an output representing the selected at least one candidate neural network.