Scaled Padding and Batchwise Weighted Loss for ML Training
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Solution Overview
Problem
Conventional digital content management systems face challenges in accuracy and efficiency due to rigid training of machine learning models from single sources, overfitting, excessive resource usage, and numerical instability, particularly when dealing with imbalanced data and multi-label classification.
Innovation Solution
The system employs scaled padding based on source density to emphasize distinctive terms and utilizes batchwise weighted loss functions to dynamically adjust weights for different classes, improving the training of digital content classification models by reducing the impact of common words and stabilizing numerical computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional systems train machine learning models from unitary sources with uniform padding, then training is simple and consistent, but accuracy deteriorates due to overfitting on sparse data and inability to handle source density variations
Solution Approach 1:
The patent applies local quality by introducing source-density-dependent padding where different portions of training data receive different padding amounts based on their source density characteristics. High-density sources receive less padding while low-density sources receive more padding, creating localized quality adjustments that improve classification accuracy without uniformly complicating the entire training process.
Solution Approach 2:
The patent implements dynamics by making padding amounts dynamic and adaptive rather than static and uniform. The system dynamically adjusts padding based on calculated source density metrics, allowing the training process to adaptively respond to varying data characteristics from different sources, thereby improving accuracy while maintaining manageable complexity through automated adaptation.
2Measurement precision
If conventional systems use uniform padding for all training samples, then processing is efficient and simple, but accuracy deteriorates due to equal weighting of common and distinctive terms
Solution Approach 1:
The patent applies local quality by differentiating padding treatment based on source density characteristics. Instead of uniform padding, the system selectively applies varying padding amounts to different training samples based on their source's density profile, emphasizing distinctive terms from low-density sources while maintaining efficiency through automated source-density calculation and application.
Solution Approach 2:
The patent implements parameter changes by dynamically adjusting the padding parameter based on source density calculations. The system changes the padding parameter from a fixed uniform value to a variable that adapts to each source's characteristics, thereby improving classification accuracy by appropriately weighting distinctive versus common terms across different data sources.
3Measurement precision
If conventional systems train over large numbers of epochs to improve accuracy, then classification precision improves, but computational resource usage increases excessively
Solution Approach 1:
The patent applies preliminary action by calculating source density metrics before the actual classification training begins. This preliminary characterization of data sources allows the system to optimize padding strategies in advance, enabling more accurate classification with fewer training epochs and thereby reducing overall computational resource consumption while maintaining or improving accuracy.
4Measurement precision
If conventional systems apply standard loss functions without weighting, then training is computationally simple, but numerical instability occurs and convergence to accurate results is undermined
Solution Approach 1:
The patent implements parameter changes by modifying the loss function to include source-density-based weighting parameters. This changes the loss function from a standard unweighted form to a weighted form that accounts for source density variations, improving numerical stability and convergence to accurate results while maintaining computational tractability through efficient weight calculation and application.
Data Source
AI summary
Methods, systems, and non-transitory computer readable storage media are disclosed for training a machine-learning model utilizing batchwise weighted loss functions and scaled padding based on source density. For example, the disclosed systems can determine a density of words or phrases in digital content from a digital content source that indicate an affinity towards one or more content classes. In some embodiments, the disclosed systems can use the determined source density to split digital content from the source into segments and pad the segments with padding characters based on the source density. The disclosed systems can also generate document embeddings using the padded segments and then train the machine-learning model using the document embeddings. Furthermore, the disclosed system can use batchwise weighted cross entropy loss for applying different class weightings on a per-batch basis during training of the machine-learning model.