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Related Experiment Video

Updated: Nov 1, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Recent Advances in Large Margin Learning.

Yiwen Guo, Changshui Zhang

IEEE Transactions on Pattern Analysis and Machine Intelligence

|June 23, 2021

Summary

This summary is machine-generated.

This survey explores large margin training for deep neural networks (DNNs), enhancing generalization and robustness. It categorizes methods and highlights the large margin principle

Related Experiment Videos

Last Updated: Nov 1, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Machine Learning
Deep Learning
Computer Science

Background:

Deep neural networks (DNNs) are prominent machine learning models for large-scale data.
Large margin training is a key concept in classical machine learning with implications for DNNs.

Purpose of the Study:

To survey recent advances in large margin training for DNNs.
To generalize margin formulations to DNNs and explore theoretical connections.
To categorize and discuss methods for enlarging margins in DNNs.

Main Methods:

Generalizing classification margin formulations to DNNs.
Summarizing theoretical links between margin, generalization, and robustness.
Categorizing and comparing various large margin training methods for DNNs.

Main Results:

Recent efforts in large margin training for DNNs are comprehensively reviewed.
Theoretical connections between margin, generalization, and robustness are established.
A categorization of methods provides a framework for comparison and discussion.

Conclusions:

The large margin principle offers theoretical insights into DNN performance and regularization.
This survey aims to inspire new research in improving DNNs via large margin learning.
Further research can verify the large margin principle's role in effective DNN regularization.