Canon kabushiki kaisha (20240233357). LEARNING APPARATUS AND LEARNING METHOD simplified abstract
Contents
- 1 LEARNING APPARATUS AND LEARNING METHOD
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 LEARNING APPARATUS AND LEARNING METHOD - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Key Features and Innovation
- 1.6 Potential Applications
- 1.7 Problems Solved
- 1.8 Benefits
- 1.9 Commercial Applications
- 1.10 Prior Art
- 1.11 Frequently Updated Research
- 1.12 Questions about Dynamic Task Assignment Technology for Neural Networks
- 1.13 Original Abstract Submitted
LEARNING APPARATUS AND LEARNING METHOD
Organization Name
Inventor(s)
Hiroshi Yoshikawa of Kanagawa (JP)
LEARNING APPARATUS AND LEARNING METHOD - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240233357 titled 'LEARNING APPARATUS AND LEARNING METHOD
Simplified Explanation
A learning apparatus assigns different tasks to neural networks in parallel and decides whether to assign the tasks to the same or different networks based on learning results.
- Tasks are assigned to neural networks in parallel
- Decision on network assignment based on learning results
Key Features and Innovation
- Parallel assignment of tasks to neural networks
- Dynamic network assignment based on learning results
Potential Applications
This technology can be applied in various fields such as:
- Machine learning
- Artificial intelligence
- Robotics
Problems Solved
- Efficient task assignment to neural networks
- Optimal utilization of neural network resources
Benefits
- Improved learning efficiency
- Enhanced performance of neural networks
Commercial Applications
Title: Dynamic Task Assignment Technology for Neural Networks This technology can be utilized in industries such as:
- Healthcare for medical diagnosis
- Finance for fraud detection
- Automotive for autonomous driving systems
Prior Art
Researchers can explore prior art related to dynamic task assignment in neural networks to understand the evolution of this technology.
Frequently Updated Research
Researchers are constantly exploring new methods and algorithms for optimizing task assignment in neural networks.
Questions about Dynamic Task Assignment Technology for Neural Networks
1. How does dynamic task assignment improve the efficiency of neural networks?
- Dynamic task assignment optimizes the utilization of neural network resources, leading to improved performance.
2. What are the potential applications of dynamic task assignment technology in various industries?
- Dynamic task assignment technology can be applied in fields such as healthcare, finance, and automotive for tasks like medical diagnosis, fraud detection, and autonomous driving systems.
Original Abstract Submitted
a learning apparatus performs a first assignment in which m tasks that are different from each other are assigned to n neural networks (where n<m) and perform learning processing that is related to the m tasks in parallel; and determines, based on learning results of the respective m tasks, whether to assign, in subsequent learning processing, the respective m tasks to the same neural networks as in the first assignment or to neural networks different from those of the first assignment.
