17986803. TRUST-REGION AWARE NEURAL NETWORK ARCHITECTURE SEARCH FOR KNOWLEDGE DISTILLATION simplified abstract (QUALCOMM Incorporated)
TRUST-REGION AWARE NEURAL NETWORK ARCHITECTURE SEARCH FOR KNOWLEDGE DISTILLATION
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TRUST-REGION AWARE NEURAL NETWORK ARCHITECTURE SEARCH FOR KNOWLEDGE DISTILLATION - A simplified explanation of the abstract
This abstract first appeared for US patent application 17986803 titled 'TRUST-REGION AWARE NEURAL NETWORK ARCHITECTURE SEARCH FOR KNOWLEDGE DISTILLATION
Simplified Explanation
The patent application describes a method for searching and selecting a neural network architecture for knowledge distillation using a trust-region Bayesian optimization technique.
- The method involves defining a search space that includes various convolutional and transformer operators for student neural network architectures.
- The trust-region Bayesian optimization is used to select the most suitable student neural network architecture based on a pre-defined teacher model.
- The innovation allows for efficient and effective knowledge distillation in neural networks.
Potential Applications
This technology has potential applications in various fields that utilize neural networks, including:
- Computer vision: Enhancing image recognition and object detection algorithms.
- Natural language processing: Improving language translation and sentiment analysis models.
- Speech recognition: Enhancing voice recognition and speech-to-text systems.
Problems Solved
The technology addresses the following problems:
- Finding the optimal neural network architecture for knowledge distillation can be time-consuming and resource-intensive.
- Existing methods may not effectively explore the search space of possible architectures.
- The selection of a suitable student neural network architecture is crucial for successful knowledge distillation.
Benefits
The technology offers the following benefits:
- Efficient search and selection of neural network architectures for knowledge distillation.
- Improved performance and accuracy of student neural networks through optimized architecture selection.
- Reduction in the time and resources required for knowledge distillation.
Original Abstract Submitted
A processor-implemented method of searching for a neural network architecture includes defining a search space of student neural network architectures for knowledge distillation. The search space includes multiple convolutional operators and multiple transformer operators. A trust-region Bayesian optimization is performed to select a student neural network architecture from the search space based on a pre-defined teacher model.