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ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

auto-gpt

An experimental open-source attempt to make GPT-4 fully autonomous.

awesome-anomaly-detection

A curated list of awesome anomaly detection resources

awesome-anomaly-detection-1

A complete list of papers on anomaly detection.

baselines

OpenAI Baselines: high-quality implementations of reinforcement learning algorithms

ddpg

Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

ddpg-1

Implementation of Deep Deterministic Policy Gradients using TensorFlow and OpenAI Gym

ddpg_motor

edward

A probabilistic programming language in TensorFlow. Deep generative models, variational inference.

eif

hitbishe

hstree

Half Space Tree for Streaming Data

icnn

Input Convex Neural Networks

milof

Online Anomaly Detection for HPC Performance Data

models

Models and examples built with TensorFlow

nab

The Numenta Anomaly Benchmark

nju-thesis

南京大学学位论文XeLaTeX模板

optimal-control-via-neural-networks

Code repo for ICLR paper: Optimal Control Via Neural Networks: A Convex Approach

paper_result

This is the experiment code and result of my research paper, including both my own method and the method used for comparision.

pilco

PILCO in TensorFlow

prml

PRML algorithms implemented in Python

pyaad

Implementation of Active Anomaly Discovery (AAD) in Python

qixing-anhuiuniversity.github.io

简单直接可用博客模板

rrcf

🌲 Implementation of the Robust Random Cut Forest Algorithm for anomaly detection by Guha et al. (2016)

sindyautoencoders

some-anomoly-detection-methods

suyan.github.io

My Blog.

virtual-node-isolation-forest

This is the source code of "Virtual Node Isolation Forest", a low cost one-class novelty detection method.