18288415. SIGNALING OF TRAINING POLICIES simplified abstract (Telefonaktiebolaget LM Ericsson (publ))
Contents
SIGNALING OF TRAINING POLICIES
Organization Name
Telefonaktiebolaget LM Ericsson (publ)
Inventor(s)
[[:Category:Henrik Ryd�n of Stockholm (SE)|Henrik Ryd�n of Stockholm (SE)]][[Category:Henrik Ryd�n of Stockholm (SE)]]
Ali Parichehrehteroujeni of Linköping (SE)
Pradeepa Ramachandra of Linköping (SE)
Angelo Centonza of Granada (ES)
Paul Schliwa-bertling of Ljungsbro (SE)
SIGNALING OF TRAINING POLICIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18288415 titled 'SIGNALING OF TRAINING POLICIES
Simplified Explanation: The patent application relates to influencing the training of a Machine Learning model based on a training policy provided by an actor node. The method involves receiving a training policy for the ML model, which includes accuracy or importance metrics for different ranges of values for a variable to be predicted. The ML model is then trained based on the training dataset and policy.
- The method involves training a Machine Learning model based on a training policy provided by an actor node.
- The training policy includes accuracy or importance metrics for different ranges of values for a variable to be predicted.
- The model is trained using the training dataset and policy.
- The first node can be a training and inferring node or a training node, while the second node is an actor node to which predictions are provided.
Potential Applications: This technology can be applied in various fields such as predictive analytics, financial forecasting, healthcare diagnostics, and autonomous systems where accurate predictions are crucial.
Problems Solved: This technology addresses the challenge of optimizing Machine Learning model training based on specific accuracy and importance metrics provided in a training policy.
Benefits: The benefits of this technology include improved accuracy in predictions, enhanced model training efficiency, and the ability to tailor training based on specific metrics provided in the training policy.
Commercial Applications: Optimizing Machine Learning model training based on specific metrics can have commercial applications in industries such as finance, healthcare, e-commerce, and autonomous vehicles, where accurate predictions are essential for decision-making.
Prior Art: Readers can explore prior art related to training policies for Machine Learning models, optimization of model training based on specific metrics, and actor node influence on model training.
Frequently Updated Research: Stay updated on research related to the influence of training policies on Machine Learning model performance, advancements in optimizing model training based on specific metrics, and the role of actor nodes in model training.
Questions about Machine Learning Model Training with Training Policy: 1. How does the training policy provided by an actor node influence the training of a Machine Learning model? 2. What are the potential applications of optimizing model training based on specific accuracy and importance metrics?
Original Abstract Submitted
Systems and methods are disclosed herein that relate to influencing training of a Machine Learning (ML) model based on a training policy provided by an actor node are disclosed herein. In one embodiment, a method performed by a first node for training a ML model comprises receiving a training policy for a ML model from a second node, the training policy comprising information that indicates two or more accuracy or importance metrics for two or more ranges of values for a variable to be predicted by the ML model. The method further comprises training the ML model based on a training dataset and the training policy. In one embodiment, the first node is either a training and inferring node or a training node that operates to train the ML model, and the second node is an actor node to which predictions made using the ML model are to be provided.
