Constrained Feature Masking for Deterministic Neural Sparsification
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Solution Overview
Problem
Existing machine learning architectures face inefficiencies due to the extraction of irrelevant features, leading to increased computational load and potential overfitting, with conventional feature masking techniques being non-deterministic and lacking flexibility.
Innovation Solution
Implement a mask generator trained with polarization and activation constraints to generate feature masks, using a loss function with multiple components to control the masking process, enabling efficient and adaptive feature selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional feature masking techniques are used, then feature selection is achieved, but the process is non-deterministic and lacks flexibility
Solution Approach 1:
The patent applies dynamics by making the masking process adaptive through a trained neural network mask generator that dynamically adjusts masks based on input features, rather than using static or random masking. The system transitions from fixed masking rules to dynamic, data-driven masking that adapts to different inputs while maintaining determinism through the trained model
Solution Approach 2:
The patent replaces conventional mechanical or rule-based masking systems with a neural network-based mask generator. This substitution enables the system to learn optimal masking strategies from data, providing both determinism (through the trained model) and flexibility (through adaptive feature selection based on input characteristics)
2Productivity
If all features are extracted and processed, then model completeness is maintained, but computational load increases and overfitting occurs
Solution Approach 1:
The patent extracts only the relevant features by using a trained mask generator to identify and mask irrelevant features. This extraction principle removes unnecessary computational processing of redundant features while maintaining the essential information needed for accurate predictions, thereby improving efficiency without sacrificing model reliability
Solution Approach 2:
The patent applies partial action by selectively processing only the unmasked (relevant) features rather than all extracted features. This partial processing approach reduces computational load and prevents overfitting by focusing resources on the most informative features while still maintaining complete model functionality
3Speed
If feature masking is applied to improve efficiency, then computational speed increases, but feature selection accuracy must be maintained
Solution Approach 1:
The patent applies preliminary action by pre-training the mask generator on training data to learn optimal masking patterns before deployment. This preliminary training ensures that when the system operates at high speed during inference, the feature selection accuracy is already optimized from the pre-training phase, allowing fast computation without sacrificing selection precision
Data Source
AI summary
Certain aspects of the present disclosure provide techniques and apparatus for feature masking. A feature tensor is accessed in a neural network, and a feature mask is generated by processing the feature tensor using a masking subnetwork, where the masking subnetwork was trained based at least in part on a polarization constraint and an activation constraint to generate feature masks. A masked feature tensor is generated based on the feature tensor and the feature mask, and an output inference is generated using the neural network based at least in part on the masked feature tensor.