Microsoft technology licensing, llc (20240185085). Resource-Efficient Training of a Sequence-Tagging Model simplified abstract
Contents
Resource-Efficient Training of a Sequence-Tagging Model
Organization Name
microsoft technology licensing, llc
Inventor(s)
Keng-hao Chang of San Jose CA (US)
Pai Chun Lin of Fremont CA (US)
Mohammadreza Khalilishoja of Sunnyvale CA (US)
Eren Manavoglu of Menlo Park CA (US)
Resource-Efficient Training of a Sequence-Tagging Model - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240185085 titled 'Resource-Efficient Training of a Sequence-Tagging Model
Simplified Explanation: The technique described in the patent application involves iteratively updating model weights of a teacher model and a student model. The teacher model generates noisy original pseudo-labeled training examples from unlabeled training data, which are then weighted based on validation information. The student model's weights are updated using these weighted pseudo-labeled examples.
Key Features and Innovation:
- Iterative updating of model weights for teacher and student models.
- Generation of noisy original pseudo-labeled training examples by the teacher model.
- Weighting of pseudo-labeled examples based on validation information.
- Updating student model weights using the weighted pseudo-labeled examples.
- Selection of labeled training examples based on uncertainty and similarity factors.
Potential Applications: This technology can be applied in various fields such as machine learning, artificial intelligence, and data analysis.
Problems Solved: This technique addresses the challenge of efficiently training models with limited labeled training data.
Benefits:
- Efficient utilization of unlabeled training data.
- Improved performance of student models with limited labeled examples.
- Reduction in the need for a large number of labeled training examples.
Commercial Applications: Potential commercial applications include enhancing the accuracy and efficiency of machine learning models in various industries such as healthcare, finance, and e-commerce.
Prior Art: Information on prior art related to this technology is not provided in the abstract.
Frequently Updated Research: There is no information on frequently updated research relevant to this technology.
Questions about the Technology: Question 1: How does the technique balance the uncertainty and similarity factors in selecting labeled training examples? Answer: The technique uses both uncertainty and similarity factors to select labeled training examples, ensuring a balance between exploiting uncertain classification results and leveraging the similarity between labeled and unlabeled data.
Question 2: What are the potential implications of using noisy original pseudo-labeled training examples in model training? Answer: The use of noisy original pseudo-labeled examples can introduce some level of noise in the training process but can also help in leveraging unlabeled data efficiently for model training.
Original Abstract Submitted
a technique iteratively updates model weights of a teacher model and a student model. in operation, the teacher model produces noisy original pseudo-labeled training examples from unlabeled training examples. the technique weights the original pseudo-labeled training examples based on validation information. the technique then updates model weights of the student model based on the weighted pseudo-labeled training examples. the validation information, which is used to weight the original pseudo-labeled training examples, is produced by selecting labeled training examples based on an uncertainty-based factor and a similarity-based factor. the uncertainty-based factor describes an extent to which the student model produces uncertain classification results for the set of labeled training examples. the similarity-based factor describes the similarity between the set of labeled training examples and the unlabeled training examples. overall, the technique is efficient because it eliminates the need to produce a large number labeled training examples.
