View source for 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==

[[:Category:HUAWEI TECHNOLOGIES CO., LTD.|HUAWEI TECHNOLOGIES CO., LTD.]]


[[Category:HUAWEI TECHNOLOGIES CO., LTD.]]

==Inventor(s)==

[[:Category:Habib Hajimolahoseini of Toronto (CA)|Habib Hajimolahoseini of Toronto (CA)]][[Category:Habib Hajimolahoseini of Toronto (CA)]]

[[:Category:Ali Saheb Pasand of Waterloo (CA)|Ali Saheb Pasand of Waterloo (CA)]][[Category:Ali Saheb Pasand of Waterloo (CA)]]

[[:Category:Ehsan Kamalloo of Waterloo (CA)|Ehsan Kamalloo of Waterloo (CA)]][[Category:Ehsan Kamalloo of Waterloo (CA)]]

[[:Category:Mehdi Rezagholi Zadeh of Vaughan (CA)|Mehdi Rezagholi Zadeh of Vaughan (CA)]][[Category:Mehdi Rezagholi Zadeh of Vaughan (CA)]]

[[:Category:Yang Liu of Toronto (CA)|Yang Liu of Toronto (CA)]][[Category:Yang Liu of Toronto (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.



[[Category:G06N3/08]]
[[Category:G06F18/15]]
[[Category:G06F18/211]]
[[Category:CPC_G06N3/08]]