18558983. NEURAL NETWORK LEARNING APPARATUS, NEURAL NETWORK LEARNING METHOD, AND PROGRAM simplified abstract (Nippon Telegraph and Telephone Corporation)
Contents
NEURAL NETWORK LEARNING APPARATUS, NEURAL NETWORK LEARNING METHOD, AND PROGRAM
Organization Name
Nippon Telegraph and Telephone Corporation
Inventor(s)
NEURAL NETWORK LEARNING APPARATUS, NEURAL NETWORK LEARNING METHOD, AND PROGRAM - A simplified explanation of the abstract
This abstract first appeared for US patent application 18558983 titled 'NEURAL NETWORK LEARNING APPARATUS, NEURAL NETWORK LEARNING METHOD, AND PROGRAM
The technique described in the patent application involves training a neural network with an encoder and a decoder to adjust the size of a specific latent variable in a latent variable vector based on the magnitude of a certain property in the input vector.
- The neural network learning device includes an encoder that converts an input vector into a latent variable vector and a decoder that converts the latent variable vector into an output vector, ensuring the input and output vectors are nearly identical.
- The learning process aims to establish a monotonic relationship between the latent variable and the input vector.
Potential Applications:
- This technology could be used in image recognition systems to enhance the accuracy of identifying objects based on specific features.
- It may also find applications in natural language processing to improve the understanding of context and sentiment in text data.
Problems Solved:
- By adjusting the latent variable based on input properties, the neural network can better capture and represent important information for various tasks.
- The monotonic relationship ensures a consistent and predictable behavior of the network in response to different inputs.
Benefits:
- Improved performance and accuracy in tasks such as image recognition and natural language processing.
- Enhanced interpretability and control over the neural network's behavior.
Commercial Applications:
- Title: "Enhanced Neural Network for Precise Data Representation"
- This technology could be valuable for companies developing AI systems for image recognition, language processing, and other data-intensive applications.
- It may lead to more efficient and reliable AI solutions, attracting interest from industries such as healthcare, finance, and autonomous vehicles.
Questions about the Technology: 1. How does the adjustment of the latent variable improve the performance of the neural network? 2. What are the potential limitations or challenges in implementing this technique in real-world applications?
Frequently Updated Research:
- Stay updated on advancements in neural network training techniques and applications in image recognition and natural language processing to explore further improvements and innovations in this technology.
Original Abstract Submitted
Provided is a technique for performing learning of a neural network including an encoder and a decoder such that a certain latent variable included in a latent variable vector is larger or the certain latent variable included in the latent variable vector is smaller as a magnitude of a certain property included in an input vector is larger. A neural network learning device performs learning of a neural network including an encoder that converts an input vector into a latent variable vector and a decoder that converts the latent variable vector into an output vector such that the input vector and the output vector are substantially equal to each other, and the learning is performed to cause the latent variable to have monotonicity with respect to the input vector.
