18064091. NETWORK-LIGHTWEIGHT MODEL FOR MULTI DEEP-LEARNING TASKS simplified abstract (International Business Machines Corporation)
Contents
NETWORK-LIGHTWEIGHT MODEL FOR MULTI DEEP-LEARNING TASKS
Organization Name
International Business Machines Corporation
Inventor(s)
NETWORK-LIGHTWEIGHT MODEL FOR MULTI DEEP-LEARNING TASKS - A simplified explanation of the abstract
This abstract first appeared for US patent application 18064091 titled 'NETWORK-LIGHTWEIGHT MODEL FOR MULTI DEEP-LEARNING TASKS
- Simplified Explanation:**
The patent application describes a method, computer program, and computer system that can perform multiple machine learning tasks using a shared framework. Data for various machine learning tasks is received and processed by a shared backbone model and multiple sub-networks.
- Key Features and Innovation:**
- Method, computer program, and system for handling multiple machine learning tasks.
- Shared framework for processing data related to different machine learning tasks.
- Utilizes a shared backbone model and multiple sub-networks to complete tasks efficiently.
- Potential Applications:**
This technology can be applied in various fields such as:
- Healthcare for medical diagnosis and treatment planning.
- Finance for fraud detection and risk assessment.
- Marketing for customer segmentation and personalized recommendations.
- Problems Solved:**
- Streamlines the process of performing multiple machine learning tasks.
- Enhances efficiency and accuracy in handling diverse data sets.
- Reduces the need for separate models for each task, saving time and resources.
- Benefits:**
- Improved performance in handling multiple machine learning tasks.
- Increased efficiency and accuracy in data processing.
- Cost-effective solution for organizations dealing with various machine learning tasks.
- Commercial Applications:**
- "Multi-Task Machine Learning Framework for Efficient Data Processing in Various Industries"
- Potential commercial uses include data analytics platforms, AI-driven applications, and predictive modeling tools.
- Market implications include increased adoption of machine learning technologies in diverse sectors.
- Prior Art:**
Readers interested in exploring prior art related to this technology can start by researching existing patents or publications in the field of machine learning frameworks and data processing methods.
- Frequently Updated Research:**
Stay updated on the latest advancements in multi-task machine learning frameworks and their applications in various industries to leverage the full potential of this technology.
- Questions about Multi-Task Machine Learning Framework:**
1. How does this technology improve the efficiency of handling multiple machine learning tasks? 2. What are the key advantages of using a shared framework for processing diverse data sets in machine learning applications?
Original Abstract Submitted
A method, computer program, and computer system are provided for performing multiple machine learning tasks through a shared framework. Data corresponding to a plurality of predetermined machine learning tasks is received. One or more steps of the machine learning tasks associated with the received data is performed on the received data by a shared backbone of a machine learning model. The predetermined plurality of machine learning tasks is completed on the received data by a plurality of sub-networks associated with each of the plurality of predetermined machine learning tasks.
