18559003. 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 18559003 titled 'NEURAL NETWORK LEARNING APPARATUS, NEURAL NETWORK LEARNING METHOD, AND PROGRAM
- Simplified Explanation:**
The patent application describes a technique for training a neural network with an encoder and decoder so that a specific latent variable in the latent variable vector changes based on the magnitude of a property in the input vector. The learning process ensures that all weight parameters of the decoder are either non-negative or non-positive values.
- Key Features and Innovation:**
- Learning technique for neural networks with encoder and decoder.
- Adjustment of latent variable based on input vector property magnitude.
- Constraint on decoder weight parameters for learning process.
- Potential Applications:**
- Image recognition and processing.
- Natural language processing.
- Anomaly detection in data.
- Problems Solved:**
- Enhancing the adaptability of neural networks.
- Improving the accuracy of output based on input properties.
- Ensuring consistency in decoder weight parameters.
- Benefits:**
- Increased precision in neural network predictions.
- Enhanced performance in various machine learning tasks.
- Greater control over latent variable adjustments.
- Commercial Applications:**
- "Enhanced Neural Network Learning Technique for Improved Data Processing and Analysis"
- Potential use in healthcare for medical image analysis.
- Application in finance for fraud detection systems.
- Prior Art:**
Prior research on neural network training techniques with encoder-decoder structures and latent variable manipulation.
- Frequently Updated Research:**
Ongoing studies on optimizing neural network training methods for improved performance and efficiency.
- Questions about Neural Network Learning Techniques:**
1. How does this learning technique compare to traditional neural network training methods? 2. What are the potential limitations of adjusting latent variables based on input vector properties?
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 identical to each other, and the learning is performed in such a manner that a condition that all weight parameters of the decoder are non-negative values or all weight parameters of the decoder are non-positive values is satisfied.
