Kabushiki kaisha toshiba (20240095520). REPRESENTATION LEARNING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM simplified abstract

REPRESENTATION LEARNING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

Organization Name

kabushiki kaisha toshiba

Inventor(s)

Kentaro Takagi of Yokohama Kanagawa (JP)

Toshiyuki Oshima of Tokyo (JP)

REPRESENTATION LEARNING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240095520 titled 'REPRESENTATION LEARNING APPARATUS, METHOD, AND NON-TRANSITORY COMPUTER READABLE MEDIUM

Simplified Explanation

The abstract describes a representation learning apparatus that calculates latent vectors in different latent spaces using model parameters, corrects similarities between these vectors, and updates the model parameters based on a loss function.

• The representation learning apparatus calculates latent vectors in a latent space of the target data x and non-interest latent vectors in latent spaces of non-interest features and data.
• It then calculates similarities between the latent vectors and their representative values, correcting these similarities based on the non-interest latent vectors.
• The first and/or second model parameters are updated based on the loss function that includes the corrected similarities.

Potential Applications

This technology can be applied in various fields such as image recognition, natural language processing, and recommendation systems.

Problems Solved

1. Efficient representation learning of complex data. 2. Improved similarity calculations between data points.

Benefits

1. Enhanced accuracy in data representation. 2. Better performance in similarity-based tasks. 3. Adaptability to different types of data.

Potential Commercial Applications

"Representation Learning Apparatus for Enhanced Data Similarity Calculations" can be utilized in industries like e-commerce, healthcare, and finance for personalized recommendations, patient diagnosis, and fraud detection.

Possible Prior Art

Prior art in representation learning and similarity calculations includes various machine learning algorithms like autoencoders, Siamese networks, and metric learning techniques.

How does this technology compare to existing methods in terms of computational efficiency?

This technology aims to improve the efficiency of representation learning and similarity calculations, but it is essential to compare its computational requirements with other methods to assess its practicality in real-world applications.

What are the potential limitations or challenges in implementing this technology in different domains?

While the abstract highlights the benefits and applications of the technology, it is crucial to consider the potential limitations or challenges that may arise when implementing it in diverse fields. Understanding these aspects can help in addressing any obstacles during deployment.

Original Abstract Submitted

a representation learning apparatus executing: calculating a latent vector sx in a latent space of the target data x using a first model parameter, calculate a non-interest latent vector zx in a latent space of an non-interest feature included in the target data x and a non-interest latent vector zb in the latent space of a non-interest data using a second model parameter, calculate a similarity s obtained by correcting a similarity between the latent vector sx and its representative value s′x by a similarity between the latent vector zx and its representative value z′x, and a similarity s between the latent vector zb and its representative value z′b, and update the first and/or the second model parameter based on the loss function including the similarity s and s