Telefonaktiebolaget lm ericsson (publ) (20240095587). METHODS AND APPARATUSES FOR PROVIDING TRANSFER LEARNING OF A MACHINE LEARNING MODEL simplified abstract
METHODS AND APPARATUSES FOR PROVIDING TRANSFER LEARNING OF A MACHINE LEARNING MODEL
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Andreas Johnsson of Uppsala (SE)
Jalil Taghia of Stockholm (SE)
METHODS AND APPARATUSES FOR PROVIDING TRANSFER LEARNING OF A MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095587 titled 'METHODS AND APPARATUSES FOR PROVIDING TRANSFER LEARNING OF A MACHINE LEARNING MODEL
Simplified Explanation
The computer-implemented method described in the abstract is for determining the suitability of a machine learning model trained with one or more features in a candidate source domain for use in a target domain. The method involves analyzing the similarity between data generation configurations for each feature in the candidate source domains and the target domain to select the most suitable source domains for the model.
- Explanation of the patent/innovation:
- The method analyzes the similarity between data generation configurations for features in candidate source domains and a target domain to determine the suitability of a machine learning model for use in the target domain.
- Potential applications of this technology:
- This technology can be applied in various industries such as healthcare, finance, and marketing for transferring machine learning models between different domains.
- Problems solved by this technology:
- This technology helps in assessing the compatibility of machine learning models trained in one domain for use in another domain, reducing the need for retraining models from scratch.
- Benefits of this technology:
- The method saves time and resources by identifying the most suitable source domains for transferring machine learning models, leading to improved efficiency and performance in the target domain.
- Potential commercial applications of this technology:
- "Optimizing Machine Learning Model Transfer Between Domains" can be used in software development companies, research institutions, and consulting firms to streamline the process of adapting machine learning models for different domains.
- Possible prior art:
- Prior art in the field of domain adaptation and transfer learning techniques may exist, where methods for transferring machine learning models between domains have been explored.
- Unanswered questions:
- How does the method handle discrepancies in data distribution between source and target domains?
The abstract does not provide details on how the method addresses discrepancies in data distribution, which could impact the performance of the transferred machine learning model.
- What are the limitations of the method in selecting suitable source domains for transferring machine learning models?
The abstract does not discuss any potential limitations or challenges that may arise in the process of selecting source domains based on similarity metrics, which could affect the effectiveness of the technology.
Original Abstract Submitted
a computer-implemented method is provided for determining whether a machine learning model in a candidate source domain is suitable for use in a target domain, wherein the machine learning model is trained with one or more features. the method comprises for each feature: determining one or more target measurement configurations indicating how data for the feature can be generated in the target domain; for each feature, performing, for each of a plurality of candidate source domains, the steps of: determining one or more source measurement configurations indicating how data for the feature can be generated in the candidate source domain, determining a similarity metric indicative of a similarity between the one or more source measurement configurations and the one or more target measurement configurations; and based on the similarity metrics determined for each feature for the plurality of candidate source domains, selecting one or more selected source domains from the plurality of candidate source domains.
