Microsoft technology licensing, llc (20240346295). MULTI-TASK MACHINE LEARNING ARCHITECTURES AND TRAINING PROCEDURES simplified abstract

MULTI-TASK MACHINE LEARNING ARCHITECTURES AND TRAINING PROCEDURES

Organization Name

microsoft technology licensing, llc

Inventor(s)

Weizhu Chen of Kirkland WA (US)

Pengcheng He of Sammamish WA (US)

Xiaodong Liu of Bellevue WA (US)

Jianfeng Gao of Woodinville WA (US)

MULTI-TASK MACHINE LEARNING ARCHITECTURES AND TRAINING PROCEDURES - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240346295 titled 'MULTI-TASK MACHINE LEARNING ARCHITECTURES AND TRAINING PROCEDURES

• • Simplified Explanation:**

This document discusses architectures and training procedures for multi-task machine learning models, such as neural networks. One method involves using shared layers and task-specific layers in the model, along with pretraining and tuning stages.

• • Key Features and Innovation:**

- Utilizes shared layers and task-specific layers in multi-task machine learning models - Includes pretraining on shared layers using unsupervised prediction tasks - Involves tuning shared layers and task-specific layers using respective task-specific objectives

• • Potential Applications:**

- Natural language processing - Image recognition - Speech recognition - Autonomous driving - Healthcare diagnostics

• • Problems Solved:**

- Enhancing the performance of multi-task machine learning models - Improving the efficiency of training procedures - Addressing the challenges of training complex neural networks

• • Benefits:**

- Increased accuracy in multi-task learning - Faster convergence during training - Better utilization of shared information across tasks

• • Commercial Applications:**

Title: Enhanced Multi-Task Machine Learning Models for Improved Performance This technology can be applied in various industries such as healthcare, finance, e-commerce, and more. Companies can use these advanced models to optimize their processes, improve decision-making, and enhance customer experiences.

• • Prior Art:**

Researchers can explore prior studies on multi-task learning, neural network architectures, and training techniques to understand the evolution of this technology.

• • Frequently Updated Research:**

Stay updated on the latest advancements in multi-task machine learning models, neural network architectures, and training methodologies to leverage cutting-edge techniques for improved performance.

• • Questions about Multi-Task Machine Learning Models:**

1. How do shared layers contribute to the performance of multi-task machine learning models? Shared layers help in capturing common patterns across different tasks, leading to improved generalization and efficiency in training.

2. What are the potential challenges in implementing task-specific layers in multi-task machine learning models? Task-specific layers may require careful tuning and regularization to prevent overfitting and ensure optimal performance across different tasks.

Original Abstract Submitted

this document relates to architectures and training procedures for multi-task machine learning models, such as neural networks. one example method involves providing a multi-task machine learning model having one or more shared layers and two or more task-specific layers. the method can also involve performing a pretraining stage on the one or more shared layers using one or more unsupervised prediction tasks. the method can also involve performing a tuning stage on the one or more shared layers and the two or more task-specific layers using respective task-specific objectives