17932212. CLASSIFICATION USING A MACHINE LEARNING MODEL TRAINED WITH TRIPLET LOSS simplified abstract (MICROSOFT TECHNOLOGY LICENSING, LLC)

CLASSIFICATION USING A MACHINE LEARNING MODEL TRAINED WITH TRIPLET LOSS

Organization Name

MICROSOFT TECHNOLOGY LICENSING, LLC

Inventor(s)

Pramod Kumar Sharma of Seattle WA (US)

Andy Daniel Martinez of Pembroke Pines FL (US)

Liang Du of Redmond WA (US)

Robin Abraham of Redmond WA (US)

Saurabh Chandrakant Thakur of Bellevue WA (US)

CLASSIFICATION USING A MACHINE LEARNING MODEL TRAINED WITH TRIPLET LOSS - A simplified explanation of the abstract

This abstract first appeared for US patent application 17932212 titled 'CLASSIFICATION USING A MACHINE LEARNING MODEL TRAINED WITH TRIPLET LOSS

Simplified Explanation

The patent application describes a machine learning model trained with a triplet loss function to classify input strings into hierarchical categories. The model is pre-trained using masking language modeling on unlabeled strings and includes an attention-based bi-directional transformer layer. After initial training, the model is further refined with cross-entropy loss and triplet loss functions to improve classification accuracy.

• The machine learning model is trained with a triplet loss function to classify input strings into hierarchical categories.
• The model is pre-trained using masking language modeling on unlabeled strings and includes an attention-based bi-directional transformer layer.
• After initial training, the model is refined with cross-entropy loss and triplet loss functions to enhance classification accuracy.

Potential Applications

The technology can be applied in various fields such as natural language processing, text classification, and information retrieval systems.

Problems Solved

This technology addresses the challenge of accurately classifying input strings into multiple hierarchical categories, improving the efficiency and accuracy of text classification tasks.

Benefits

The machine learning model provides a deep learning solution for classifying input strings into hierarchical categories, capturing language similarities and improving classification accuracy.

Potential Commercial Applications

The technology can be utilized in industries such as e-commerce, customer service, and content recommendation systems to enhance text classification and information retrieval processes.

Possible Prior Art

Prior art in this field includes existing machine learning models for text classification and information retrieval, but the specific use of a triplet loss function for hierarchical categorization may be novel.

How does the triplet loss function improve classification accuracy compared to other loss functions?

The triplet loss function helps the model learn better embeddings by enforcing a margin between positive and negative examples, leading to improved classification performance.

What are the computational requirements for training and deploying this machine learning model in real-world applications?

The computational resources needed for training and deploying the model depend on the size of the dataset, complexity of the model architecture, and desired level of accuracy, which may vary for different applications.

Original Abstract Submitted

A machine learning model trained with a triplet loss function classifies input strings into one of multiple hierarchical categories. The machine learning model is pre-trained using masking language modeling on a corpus of unlabeled strings. The machine learning module includes an attention-based bi-directional transformer layer. Following initial training, the machine learning model is refined by additional training with a loss function that includes cross-entropy loss and triplet loss. This provides a deep learning solution to classify input strings into one or more hierarchical categories. Embeddings generated from inputs to the machine learning model capture language similarities that can be visualized in a cartesian plane where strings with similar meanings are grouped together.