18509790. MULTITASK LEARNING APPARATUS AND METHOD FOR HETEROGENEOUS SPARSE DATASETS simplified abstract (ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE)

MULTITASK LEARNING APPARATUS AND METHOD FOR HETEROGENEOUS SPARSE DATASETS

Organization Name

ELECTRONICS AND TELECOMMUNICATIONS RESEARCH INSTITUTE

Inventor(s)

Jiwon Yang of Daejeon (KR)

MULTITASK LEARNING APPARATUS AND METHOD FOR HETEROGENEOUS SPARSE DATASETS - A simplified explanation of the abstract

This abstract first appeared for US patent application 18509790 titled 'MULTITASK LEARNING APPARATUS AND METHOD FOR HETEROGENEOUS SPARSE DATASETS

Simplified Explanation

The multitask learning apparatus and method described in the patent application aim to improve learning performance of heterogeneous small datasets by utilizing a neural network with task-specific layers and a shared layer between tasks.

• The first layer of the apparatus generates feature vectors by projecting training data pairs from different tasks to a common feature space.
• The second layer extracts a common feature from the projected feature vectors.
• The third layer draws individual inferences from the extracted common feature.
• The first and third layers are task-specific, while the second layer is shared between tasks.
• The three layers work together to perform forward propagation in one artificial neural network.

Potential Applications

This technology could be applied in various fields such as healthcare, finance, and marketing for tasks involving small datasets and multiple related tasks.

Problems Solved

This technology addresses the challenge of improving learning performance when dealing with small and diverse datasets by leveraging shared features between tasks.

Benefits

The multitask learning apparatus can enhance the efficiency and accuracy of learning models, especially in scenarios where data is limited and tasks are related.

Potential Commercial Applications

• "Enhancing Learning Performance with Multitask Learning Apparatus in Healthcare Analytics"

Possible Prior Art

There may be prior art related to multitask learning methods in machine learning and artificial intelligence research.

How does this technology compare to traditional machine learning methods for small datasets?

This technology could potentially outperform traditional methods by leveraging shared features between tasks and improving overall learning performance.

What are the limitations of this multitask learning apparatus in real-world applications?

The limitations of this technology may include scalability issues with large datasets and the need for extensive computational resources to train the neural network effectively.

Original Abstract Submitted

Provided are a multitask learning apparatus and method for improving learning performance of heterogeneous small datasets. The multitask learning apparatus includes a first layer configured to generate feature vectors by projecting training data pairs generated from different tasks to one feature space, a second layer configured to extract a common feature from the projected feature vectors, and a third layer configured to draw each individual inference from the extracted common feature. Here, the first layer and the third layer are task-specific layers, and the second layer is a layer shared between tasks. The first layer, the second layer, and the third layer perform forward propagation in one artificial neural network.