Sample Efficient Machine Learning Training via Dimensionality Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning algorithms require large amounts of labeled training data, which is time-consuming and prone to errors, especially when labeling abstract concepts, and active learning techniques can be resource-intensive and inefficient due to the inclusion of uninformative examples.
Innovation Solution
The method involves iteratively labeling training data by mapping it to a reduced-dimension space, identifying target examples for human verification, and using combinations of techniques like fuzzy rule matching and neural network embeddings to reduce bias and increase diversity, thereby minimizing the need for human labeling and computational resources.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human labelers are used to label training data, then labeling accuracy can be maintained, but time consumption and error rates increase significantly
Solution Approach 1:
The system enables automated self-labeling of training data by using the model under development to generate labels for unlabeled examples. The model processes unlabeled data and produces labels that are then used to train the model, creating a self-service labeling loop that eliminates the need for time-consuming human labelers while maintaining labeling accuracy through iterative refinement.
Solution Approach 2:
The system implements feedback mechanisms where the model's generated labels are continuously evaluated and used to refine the model. Unlabeled examples are processed by the model, the generated labels are fed back into the training process, and the model iteratively improves its labeling capability, creating a feedback loop that maintains high labeling accuracy without human intervention.
2Productivity
If active learning techniques are used to reduce human labeling, then the number of human-labeled examples decreases, but computational resources and processing time increase significantly
Solution Approach 1:
The system performs preliminary actions by pre-processing and filtering unlabeled examples before they enter the main labeling pipeline. The model identifies and processes only the most informative examples first, preparing them for efficient labeling. This preliminary action reduces the overall computational burden by avoiding unnecessary processing of redundant or low-value examples.
Solution Approach 2:
The system extracts and removes uninformative examples from the training dataset through filtering and selection mechanisms. By identifying and excluding examples that provide little value for model training, the system reduces the computational resources required for labeling while maintaining the quality and effectiveness of the training data.
3Measurement precision
If large datasets are collected for training, then model accuracy improves, but the proportion of uninformative examples increases, wasting computational resources
Solution Approach 1:
The system applies local quality by treating different examples differently based on their informativeness. Instead of uniformly processing all examples, the model identifies and prioritizes informative examples for labeling while filtering out uninformative ones. This localized approach ensures that computational resources are concentrated on examples that will actually improve model accuracy.
Solution Approach 2:
The system extracts and removes uninformative examples from the training dataset through filtering mechanisms. By identifying and excluding examples that provide little value for model training, the system reduces the computational resources required for labeling while maintaining the quality and effectiveness of the training data, thereby improving model accuracy without wasting energy on redundant examples.
4Measurement precision
If the model under development is complex to achieve high accuracy, then model performance improves, but the time and resources required for automatic labeling increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and preparing training data before it is used for model training. The model first processes and filters unlabeled examples to identify the most informative ones, preparing them for efficient labeling. This preliminary action reduces the overall time and resources required for automatic labeling by avoiding unnecessary processing of redundant examples.
Solution Approach 2:
The system extracts and removes uninformative examples from the training dataset through filtering mechanisms. By identifying and excluding examples that provide little value for model training, the system reduces the computational resources and time required for automatic labeling while maintaining the quality and effectiveness of the training data.
Data Source
AI summary
Systems, methods and computer program products for sample efficient training of machine learning models are provided. A process may proceed, starting with an initial set of labeled examples and the initial set of unlabeled examples, to label unlabeled examples in an iterative manner, with the input dataset for a next iteration comprising an augmented set of labeled examples from a current iteration and selected unlabeled examples, until a final set of labeled examples is created. The final set of labeled examples is used to train a machine learning model. Each iteration includes mapping the input dataset to a reduced dimension space and using the reduced dimension space to identify high value examples to label.