Meta-Learning Apparatus for Low-Data Prediction Accuracy
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Solution Overview
Problem
Existing meta-learning methods fail to achieve sufficient performance with limited training data across different tasks.
Innovation Solution
A learning apparatus that samples tasks and indices from a dataset, generates task vectors using a first neural network, predicts response variables using a second neural network, and updates both networks based on errors between predicted and actual values, enabling high-performance prediction models even with a small amount of training data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a large amount of task-specific training data is used to achieve high performance, then prediction accuracy is improved, but preparation cost increases
Solution Approach 1:
The patent applies universality by creating a meta-learning framework that learns generalizable patterns across multiple tasks. The meta-model trained on diverse task data can be adapted to new tasks with minimal additional training data, making the system universally applicable across different prediction problems rather than requiring task-specific large-scale training for each individual task.
Solution Approach 2:
The patent implements preliminary action by pre-training a meta-model on a broad set of tasks before applying it to specific new tasks. This preliminary training phase extracts transferable knowledge and patterns that can be quickly adapted to new tasks, eliminating the need to start from scratch for each new prediction task and reducing the preparation cost for subsequent tasks.
2Quantity of substance
If existing meta-learning methods are used to reduce training data requirements, then preparation cost is reduced, but prediction accuracy deteriorates
Solution Approach 1:
The patent applies feedback by implementing a meta-learning framework that uses performance feedback from multiple tasks to continuously improve the meta-model. The system learns from the outcomes of previous tasks and adjusts its parameters to optimize future performance, enabling high accuracy even with limited training data per task through cumulative learning across tasks.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting model parameters through meta-learning. The system learns optimal parameter configurations across different tasks and can adapt parameters for new tasks based on learned patterns, maintaining high prediction accuracy while requiring fewer task-specific training data compared to traditional methods that use fixed parameters.
3Adaptability or versatility
If meta-learning methods are applied to learn from multiple tasks, then adaptability is improved, but model complexity increases
Solution Approach 1:
The patent applies segmentation by dividing the learning process into distinct phases: a meta-training phase on multiple tasks and a meta-testing/adaptation phase on new tasks. This segmentation allows the complex meta-learning operations to be confined to the training phase, while the adaptation phase remains relatively simple, managing overall system complexity while maintaining high adaptability.
Solution Approach 2:
The patent introduces an intermediary meta-model that acts as a bridge between diverse task data and specific task predictions. This meta-model learns generalizable representations from multiple tasks and mediates the transfer of knowledge to new tasks, enabling high adaptability while keeping the complexity manageable through the use of this intermediate learning layer rather than directly complex task-specific models.
Data Source
AI summary
A learning apparatus includes a memory and a processor to execute: receiving as input, when denoting a set of indices representing response variables of a task r in a set of tasks R, as Cr, a data set Drc composed of pairs of the response variables and explanatory variable; sampling the task r from R, an index c from Cr, and a first subset from Drc and a second subset from a set of Drc excluding the first subset; generating a task vector representing a property of a task corresponding to the first subset with a first neural network; calculating, from the task vector and explanatory variables in the second subset, predicted values of response variables for the explanatory variables with a second neural network; and updating the first and second neural networks using an error between response variables in the second subset and the predicted values thereof.