18099364. SYSTEMS, APPARATUSES, METHODS, AND NON-TRANSITORY COMPUTER-READABLE STORAGE DEVICES FOR TRAINING ARTIFICIAL-INTELLIGENCE MODELS USING ADAPTIVE DATA-SAMPLING simplified abstract (HUAWEI TECHNOLOGIES CO., LTD.)
SYSTEMS, APPARATUSES, METHODS, AND NON-TRANSITORY COMPUTER-READABLE STORAGE DEVICES FOR TRAINING ARTIFICIAL-INTELLIGENCE MODELS USING ADAPTIVE DATA-SAMPLING
Organization Name
Inventor(s)
Habib Hajimolahoseini of Toronto (CA)
Ali Saheb Pasand of Waterloo (CA)
Ehsan Kamalloo of Waterloo (CA)
Mehdi Rezagholi Zadeh of Vaughan (CA)
SYSTEMS, APPARATUSES, METHODS, AND NON-TRANSITORY COMPUTER-READABLE STORAGE DEVICES FOR TRAINING ARTIFICIAL-INTELLIGENCE MODELS USING ADAPTIVE DATA-SAMPLING - A simplified explanation of the abstract
This abstract first appeared for US patent application 18099364 titled 'SYSTEMS, APPARATUSES, METHODS, AND NON-TRANSITORY COMPUTER-READABLE STORAGE DEVICES FOR TRAINING ARTIFICIAL-INTELLIGENCE MODELS USING ADAPTIVE DATA-SAMPLING
- Simplified Explanation:**
The method described in the patent application involves using an artificial intelligence model to calculate importance metrics of data samples, without using labels or a learning rate. Sampling probabilities are then calculated based on these metrics, and a subset of data samples is selected for training the AI model.
- Key Features and Innovation:**
- Importance metrics of data samples calculated without labels or learning rate - Sampling probabilities determined based on these metrics - Selection of subset of data samples for training the AI model
- Potential Applications:**
- Machine learning - Data analysis - Predictive modeling
- Problems Solved:**
- Efficient training of AI models without relying on labels - Improved selection of data samples for training
- Benefits:**
- Enhanced model performance - Reduced reliance on labeled data - Increased efficiency in training AI models
- Commercial Applications:**
Potential commercial applications include: - Predictive analytics software - Data-driven decision-making tools - AI-powered solutions for various industries
- Questions about the Technology:**
1. How does this method improve the efficiency of training AI models? 2. What are the implications of not using labels in calculating importance metrics?
- Frequently Updated Research:**
Stay updated on advancements in AI model training techniques and data sampling methods to enhance the efficiency and performance of AI systems.
Original Abstract Submitted
A method has the steps of: calculating importance metrics of a plurality of data samples based on predictions of an artificial-intelligence (AI) model obtained from the plurality of data samples in a plurality of previous training epochs without using labels of the plurality of data samples and without using a learning rate of the AI model; calculating sampling probabilities of the plurality of data samples based on the importance metrics thereof; selecting a subset of the plurality of data samples based on the sampling probabilities of the of plurality of data samples; and training the AI model using the selected subset of the plurality of data samples for one or more epochs.
