Official implementation of CVPR'24 paper 'Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample Prompts'.

Gorilla: Training and Evaluating LLMs for Function Calls (Tool Calls)

Llama中文社区，Llama3在线体验和微调模型已开放，实时汇总最新Llama3学习资料，已将所有代码更新适配Llama3，构建最好的中文Llama大模型，完全开源可商用

RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation.

The paper list of the 86-page paper "The Rise and Potential of Large Language Model Based Agents: A Survey" by Zhiheng Xi et al.

悟空财务管理系统（悟空FS） 实现凭证管理、账簿管理、资产负债表、现金流量表、利润表等管理。开启数智财务新时代。

悟空CRM-基于Spring Cloud Alibaba微服务架构 +vue ElementUI的前后端分离CRM系统

悟空无代码平台正式开源，通过悟空无代码平台开发工具，企业可自主地快速开发出适合企业需要的信息化系统，开发过程只需要业务人员参与，开发效率极高，维护性很强。

An anomaly detection library comprising state-of-the-art algorithms and features such as experiment management, hyper-parameter optimization, and edge inference.

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

SeaTunnel is a next-generation super high-performance, distributed, massive data integration tool.

This repository stores the code to reproduce the results published in "TiWS-iForest: Isolation Forest in Weakly Supervised and Tiny ML scenarios"

Deep anomaly detection with scale learning

中文langchain项目|小必应，Q.Talk，强聊，QiangTalk

Open-sourced codes for MiniGPT-4 and MiniGPT-v2 (https://minigpt-4.github.io, https://minigpt-v2.github.io/)

👽 Out-of-Distribution Detection with PyTorch

CCNet: Criss-Cross Attention for Semantic Segmentation (TPAMI 2020 & ICCV 2019).

This is a toolbox for Deep Active Learning, an extension from previous work https://github.com/ej0cl6/deep-active-learning (DeepAL toolbox).

Everything you need about Active Learning (AL).

Sample Jupyter Notebook for playing around with the Anomaly Detection service to be made available on API Hub

TensorLayerX: A Unified Deep Learning and Reinforcement Learning Framework for All Hardwares, Backends and OS.

Code for ECCV 2020 paper "Backpropagated Gradient Representations for Anomaly Detection"

R2-AD2: Detecting Anomalies by Analysing the Raw Gradient

A Collection of Resources for Weakly-supervised Anomaly Detection (WSAD)

A unified framework for privacy-preserving data analysis and machine learning

A list of awesome research on log analysis, anomaly detection, fault localization, and AIOps

ML powered analytics engine for outlier detection and root cause analysis.

(WWW'21) ATON - an Outlier Interpreation / Outlier explanation method