Active Learning Uncertainty Estimation via Projected Labels Matrix
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Solution Overview
Problem
Existing combinations of active learning and data programming in machine learning systems face challenges in accurately estimating uncertainty without sufficient labeled training data, leading to inefficient annotation processes and costly re-training of models, especially when using deep neural networks.
Innovation Solution
A computer-implemented machine learning method that computes a labeling matrix using labeling functions, generates a projected labels matrix, estimates uncertainty for labeled data points based on labeling functions' outputs and projected labels, selects data points for annotation based on uncertainty, and updates the labeling matrix with oracle responses, thereby reducing the need for extensive labeling and re-training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing active learning systems re-train the end model for every cycle to improve uncertainty estimation, then measurement precision improves, but loss of time and productivity deteriorate
Solution Approach 1:
The system performs preliminary actions by pre-computing the labeling matrix and projected labels matrix before active learning cycles begin. These pre-computed structures are then reused across multiple cycles, eliminating the need to re-train the end model for each cycle while maintaining accurate uncertainty estimation.
Solution Approach 2:
Instead of re-training the end model repeatedly, the system creates and reuses copies of the pre-computed labeling matrix and projected labels matrix. These matrix copies serve as stable references for uncertainty estimation across multiple active learning cycles, avoiding redundant training computations.
2Manufacturing precision
If labeling functions are expanded to cover more corner cases to improve classification accuracy, then manufacturing precision improves, but device complexity increases
Solution Approach 1:
The system introduces an intermediary component - the projected labels matrix - that bridges the gap between simple labeling functions and accurate classification. This matrix computes probable labels for corner cases that labeling functions miss, improving classification accuracy without requiring complex labeling functions.
Solution Approach 2:
The system replaces the mechanical approach of creating complex labeling functions with a computational approach using the projected labels matrix. Instead of manually designing complex rules for corner cases, the system uses algorithmic computation to infer probable labels, reducing complexity while maintaining accuracy.
3Reliability
If the number of labeling functions is increased to improve label coverage, then reliability improves, but ease of operation deteriorates
Solution Approach 1:
The projected labels matrix serves a universal function by handling multiple purposes: it fills gaps in labeling function coverage, provides probable labels for corner cases, and enables uncertainty estimation. This single multi-functional component improves label coverage without requiring proportional increases in the number of individual labeling functions.
Data Source
AI summary
A computer-implemented machine learning (ML) method is provided. The method includes computing a labeling matrix by applying a set of labeling functions (LFs) to data points of an unlabeled dataset. A projected labels matrix is generated by computing, based on the labeling matrix, LFs labels projections to undefined labels. An uncertainty of a respective label of the each labeled data point is estimated for each labeled data point based on an output of the LFs and the LFs labels projections. Data points are selected depending on the uncertainty estimated for the respective label of the each data point, and a labeling request for the selected data points is submitted to an oracle and updating the labeling matrix according to responses of the oracle.