Neural Network Weight Generation via Attention Layer Reshaping
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Solution Overview
Problem
Conventional generative machine learning models face limitations when processing tabular data due to token restrictions, leading to inefficiencies in resource usage and scalability.
Innovation Solution
A foundation machine learning model is developed to directly generate a fully trained neural network by processing input training and prediction datasets as tokens using a classification-based machine learning model, which includes an attention layer to reshape weighting features into weights for the neural network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional generative machine learning models are used to process tabular data, then the models can be adapted to work on tabular data, but the token limits severely limit the amount of data that can be ingested and require significant resources to operate
Solution Approach 1:
The patent segments the processing task by dividing tabular data into fixed-size training sets and prediction sets, each containing a limited number of rows. This segmentation allows the model to process multiple smaller batches rather than being constrained by a single large token limit, effectively increasing the total amount of data that can be ingested through iterative processing
Solution Approach 2:
The patent transforms the conventional approach by shifting from processing entire datasets at once to generating model weights through processing smaller training sets. This dimensional shift in the processing paradigm allows bypassing the token limit constraint while maintaining adaptability to tabular data through the weight generation mechanism
2Adaptability or versatility
If conventional generative machine learning models are used to process tabular data, then the models can be adapted to work on tabular data, but significant resources are required to operate
Solution Approach 1:
The patent applies preliminary action by pre-processing tabular data into structured training sets with fixed numbers of rows and features before model processing. This pre-structuring optimizes the input format for efficient processing, reducing the computational resources required during the actual model execution phase while maintaining adaptability to different tabular datasets
Solution Approach 2:
The patent uses copying by generating a trained neural network model through processing a smaller training set, then copying and applying the generated weights to process prediction data. This approach avoids repeatedly processing large datasets with resource-intensive generative models, significantly reducing computational resource requirements while maintaining processing capability
3Productivity
If a foundation machine learning model generates a trained neural network by processing training and prediction datasets, then inference cost and scalability are improved, but the model requires processing both training and prediction data as tokens
Solution Approach 1:
The patent merges the training and prediction processing into a unified weight generation approach. By combining both datasets in the processing pipeline and using an attention layer to integrate information from both, the system generates comprehensive model weights that capture patterns from training data while being optimized for prediction tasks, achieving high inference speed without excessive architectural complexity
Data Source
AI summary
A method, computer program product, and computing system for processing training data and prediction data as a plurality of tokens using a classification-based machine learning model. A plurality of weighting features associated with the training data and the prediction data are defined by processing the output of the machine learning model with an attention layer. The plurality of weighting features are reshaped to generate weights for a trained neural network by processing the plurality of weighting features with an attention layer.