Stars
A curated list of Awesome-LLM-Ensemble papers for the survey "Harnessing Multiple Large Language Models: A Survey on LLM Ensemble"
An overview of LLMs for cybersecurity.
Get started with building Fullstack Agents using Gemini 2.5 and LangGraph
This project aims to consolidate and share high-quality resources and tools across the cybersecurity domain.
本项目是自动化学报中AUTOPLAN的代码地址，使用大语言模型完成了复杂任务的任务规划以及任务执行
🍒 Cherry Studio is a desktop client that supports for multiple LLM providers.
Model Context Protocol (MCP) Server for dify workflows
Build effective agents using Model Context Protocol and simple workflow patterns
MCP 资源精选， MCP指南，Claude MCP，MCP Servers, MCP Clients
Model Context Protocol(MCP) 编程极速入门
The most reliable AI agent framework that supports MCP.
🐫 CAMEL: The first and the best multi-agent framework. Finding the Scaling Law of Agents. https://www.camel-ai.org
Lightweight, composable framework for building muti-agent systems with Model Context Protocol (MCP)
An APE(Automatic Prompt Engineer) implementation with Azure OpenAI.
Large Language Model guided Protocol Fuzzing (NDSS'24)
9FFACode for TNNLS paper "Deep Clustering and Visualization for End-to-End High Dimensional Data analysis"
This repository holds the code for the paper "Deep Conditional Gaussian Mixture Model forConstrained Clustering".
State of the art Semantic Sentence Embeddings
This repository contains the code for our paper "Robust Representation Learning with Reliable Pseudo-labels Generation via Self-Adaptive Optimal Transport for Short Text Clustering" (ACL 2023).
Official implementation of DPT: Dual Prompt Tuning based Contrastive Learning for Hierarchical Text Classification
TDEC: Evidential Clustering Based on Transfer Learning and Deep Autoencoder
Extensive tutorials for the Advanced NLP Workshop in Open Data Science Conference Europe 2020. We will leverage deep learning and deep transfer learning to solve popular tasks in NLP including Clas…
"Learning to Discover Novel Visual Categories via Deep Transfer Clustering" by Kai Han, Andrea Vedaldi, Andrew Zisserman (ICCV 2019)
Deep Attention Embedding Graph Clustering(19IJCAI)
Python code for paper - Variational Deep Embedding : A Generative Approach to Clustering