Telefonaktiebolaget lm ericsson (publ) (20240095525). BUILDING AN EXPLAINABLE MACHINE LEARNING MODEL simplified abstract
Contents
- 1 BUILDING AN EXPLAINABLE MACHINE LEARNING MODEL
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 BUILDING AN EXPLAINABLE MACHINE LEARNING MODEL - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
BUILDING AN EXPLAINABLE MACHINE LEARNING MODEL
Organization Name
telefonaktiebolaget lm ericsson (publ)
Inventor(s)
Perepu Satheesh Kumar of Chennai (IN)
Sai Hareesh Anamandra of BANGALORE KARNATAKA (IN)
BUILDING AN EXPLAINABLE MACHINE LEARNING MODEL - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240095525 titled 'BUILDING AN EXPLAINABLE MACHINE LEARNING MODEL
Simplified Explanation
The abstract describes a computer-implemented method for building a machine learning model by training the model with input data, obtaining output data, determining working values for filters in each layer, identifying dominant filters based on thresholds, and building a subset model based on the dominant filters.
- Training a machine learning model with input data and class labels
- Obtaining output data with class probabilities values
- Determining working values for filters in each layer using class labels and probabilities
- Identifying dominant filters in each layer based on working values exceeding a threshold
- Building a subset machine learning model based on dominant filters for each layer
Potential Applications
This technology can be applied in various fields such as image recognition, natural language processing, and pattern recognition.
Problems Solved
This technology helps in simplifying complex machine learning models and improving model efficiency by focusing on dominant filters.
Benefits
The benefits of this technology include faster model training, reduced computational resources, and potentially improved model performance.
Potential Commercial Applications
Potential commercial applications of this technology include developing more efficient machine learning models for industries such as healthcare, finance, and e-commerce.
Possible Prior Art
One possible prior art could be techniques for optimizing machine learning models by identifying and focusing on key features or filters within the model.
Unanswered Questions
How does this technology compare to existing methods for simplifying machine learning models?
This article does not provide a direct comparison with existing methods for simplifying machine learning models. Further research or experimentation may be needed to evaluate the effectiveness of this technology compared to other approaches.
What impact could this technology have on the development of more interpretable machine learning models?
The article does not discuss the potential impact of this technology on the interpretability of machine learning models. Understanding how this method affects model interpretability could be crucial for its adoption in industries where model transparency is essential.
Original Abstract Submitted
a computer-implemented method for building a machine learning (ml) model is provided. the method includes training a ml model using a set of input data, wherein the ml model includes a plurality of layers and each layer includes a plurality of filters, and wherein the set of input data includes class labels; obtaining a set of output data from training the ml model, wherein the set of output data includes class probabilities values; determining, for each layer in the ml model, by using the class labels and the class probabilities values, a working value for each filter in the layer; determining, for each layer in the ml model, a dominant filter, wherein the dominant filter is determined based on whether the working value for the filter exceeds a threshold; and building a subset ml model based on each dominant filter for each layer, wherein the subset ml model is a subset of the ml model.
