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2,028 results on '"Yang, Hongjun"'

1. Demonstration of DB-GPT: Next Generation Data Interaction System Empowered by Large Language Models

Author

Xue, Siqiao, Qi, Danrui, Jiang, Caigao, Shi, Wenhui, Cheng, Fangyin, Chen, Keting, Yang, Hongjun, Zhang, Zhiping, He, Jianshan, Zhang, Hongyang, Wei, Ganglin, Zhao, Wang, Zhou, Fan, Yi, Hong, Liu, Shaodong, and Chen, Faqiang

Subjects
Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. The technologies of interacting with data particularly have an important entanglement with LLMs as efficient and intuitive data interactions are paramount. In this paper, we present DB-GPT, a revolutionary and product-ready Python library that integrates LLMs into traditional data interaction tasks to enhance user experience and accessibility. DB-GPT is designed to understand data interaction tasks described by natural language and provide context-aware responses powered by LLMs, making it an indispensable tool for users ranging from novice to expert. Its system design supports deployment across local, distributed, and cloud environments. Beyond handling basic data interaction tasks like Text-to-SQL with LLMs, it can handle complex tasks like generative data analysis through a Multi-Agents framework and the Agentic Workflow Expression Language (AWEL). The Service-oriented Multi-model Management Framework (SMMF) ensures data privacy and security, enabling users to employ DB-GPT with private LLMs. Additionally, DB-GPT offers a series of product-ready features designed to enable users to integrate DB-GPT within their product environments easily. The code of DB-GPT is available at Github(https://github.com/eosphoros-ai/DB-GPT) which already has over 10.7k stars. Please install DB-GPT for your own usage with the instructions(https://github.com/eosphoros-ai/DB-GPT#install) and watch a 5-minute introduction video on Youtube(https://youtu.be/n_8RI1ENyl4) to further investigate DB-GPT.

Published
2024

4. DB-GPT: Empowering Database Interactions with Private Large Language Models

Author

Xue, Siqiao, Jiang, Caigao, Shi, Wenhui, Cheng, Fangyin, Chen, Keting, Yang, Hongjun, Zhang, Zhiping, He, Jianshan, Zhang, Hongyang, Wei, Ganglin, Zhao, Wang, Zhou, Fan, Qi, Danrui, Yi, Hong, Liu, Shaodong, and Chen, Faqiang

Subjects
Computer Science - Databases
Abstract

The recent breakthroughs in large language models (LLMs) are positioned to transition many areas of software. Database technologies particularly have an important entanglement with LLMs as efficient and intuitive database interactions are paramount. In this paper, we present DB-GPT, a revolutionary and production-ready project that integrates LLMs with traditional database systems to enhance user experience and accessibility. DB-GPT is designed to understand natural language queries, provide context-aware responses, and generate complex SQL queries with high accuracy, making it an indispensable tool for users ranging from novice to expert. The core innovation in DB-GPT lies in its private LLM technology, which is fine-tuned on domain-specific corpora to maintain user privacy and ensure data security while offering the benefits of state-of-the-art LLMs. We detail the architecture of DB-GPT, which includes a novel retrieval augmented generation (RAG) knowledge system, an adaptive learning mechanism to continuously improve performance based on user feedback and a service-oriented multi-model framework (SMMF) with powerful data-driven agents. Our extensive experiments and user studies confirm that DB-GPT represents a paradigm shift in database interactions, offering a more natural, efficient, and secure way to engage with data repositories. The paper concludes with a discussion of the implications of DB-GPT framework on the future of human-database interaction and outlines potential avenues for further enhancements and applications in the field. The project code is available at https://github.com/eosphoros-ai/DB-GPT. Experience DB-GPT for yourself by installing it with the instructions https://github.com/eosphoros-ai/DB-GPT#install and view a concise 10-minute video at https://www.youtube.com/watch?v=KYs4nTDzEhk.

Published
2023

38. Group Feature Learning and Domain Adversarial Neural Network for aMCI Diagnosis System Based on EEG

Author

Fan, Chen-Chen, Xie, Haiqun, Peng, Liang, Yang, Hongjun, Ni, Zhen-Liang, Wang, Guan'an, Zhou, Yan-Jie, Chen, Sheng, Fang, Zhijie, Huang, Shuyun, and Hou, Zeng-Guang

Subjects
Computer Science - Machine Learning, Computer Science - Robotics, Electrical Engineering and Systems Science - Signal Processing
Abstract

Medical diagnostic robot systems have been paid more and more attention due to its objectivity and accuracy. The diagnosis of mild cognitive impairment (MCI) is considered an effective means to prevent Alzheimer's disease (AD). Doctors diagnose MCI based on various clinical examinations, which are expensive and the diagnosis results rely on the knowledge of doctors. Therefore, it is necessary to develop a robot diagnostic system to eliminate the influence of human factors and obtain a higher accuracy rate. In this paper, we propose a novel Group Feature Domain Adversarial Neural Network (GF-DANN) for amnestic MCI (aMCI) diagnosis, which involves two important modules. A Group Feature Extraction (GFE) module is proposed to reduce individual differences by learning group-level features through adversarial learning. A Dual Branch Domain Adaptation (DBDA) module is carefully designed to reduce the distribution difference between the source and target domain in a domain adaption way. On three types of data set, GF-DANN achieves the best accuracy compared with classic machine learning and deep learning methods. On the DMS data set, GF-DANN has obtained an accuracy rate of 89.47%, and the sensitivity and specificity are 90% and 89%. In addition, by comparing three EEG data collection paradigms, our results demonstrate that the DMS paradigm has the potential to build an aMCI diagnose robot system., Comment: This paper has been accepted by 2021 International Conference on Robotics and Automation (ICRA 2021)

Published
2021

39. A Classification Framework Based on Multi-modal Features for Detection of Cognitive Impairments

Author

Chen, Sheng, Xie, Haiqun, Yang, Hongjun, Fan, Chenchen, Hou, Zengguang, Zhang, Chutian, Filipe, Joaquim, Editorial Board Member, Ghosh, Ashish, Editorial Board Member, Prates, Raquel Oliveira, Editorial Board Member, Zhou, Lizhu, Editorial Board Member, Yu, Zhiwen, editor, Hei, Xinhong, editor, Li, Duanling, editor, Song, Xianhua, editor, and Lu, Zeguang, editor

Published
2023
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