Popular GitHub Repositories for Ad Blocker

High-performance automatic differentiation of LLVM and MLIR.

A library created to revitalize C++ as a machine learning front end. Per aspera ad astra.

CTR prediction models based on deep learning(基于深度学习的广告推荐CTR预估模型)

哔哩哔哩浏览器插件：基于大语言模型，对B站视频中的植入广告进行检测。一键跳过视频中的植入/口播广告。

[CVPR 2023 Workshop] VAND Challenge: 1st Place on Zero-shot AD and 4th Place on Few-shot AD

An MCP tool that connects Google Ads with Claude AI/Cursor and others, allowing you to analyze your advertising data through natural language conversations. This integration gives you access to campaign information, performance metrics, keyword analytics, and ad management—all through simple chat with Claude, Cursor or Windsurf.

Repository for the Explainable Deep One-Class Classification paper

World’s first Open Source AI content writing platform that empowers users to create SEO-friendly blog posts, ad copy for social media, website landing pages, and more.

K-NRM: End-to-End Neural Ad-hoc Ranking with Kernel Pooling

Vanilla torch and timm industrial knn-based anomaly detection for images.

TSB-AD: Towards A Reliable Time-Series Anomaly Detection Benchmark

PantheonRL is a package for training and testing multi-agent reinforcement learning environments. PantheonRL supports cross-play, fine-tuning, ad-hoc coordination, and more.

A differentiable physics engine and multibody dynamics library for control and robot learning.

Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events

[NeurIPS 2023] Offical code for <Real3D-AD: A Dataset of Point Cloud Anomaly Detection>. A 3D point cloud anomaly detection dataset and benchmark.

Official Implementation for the "Back to the Feature: Classical 3D Features are (Almost) All You Need for 3D Anomaly Detection" paper (VAND Workshop - CVPR 2023).

MCP Server for Facebook ADs Library - Get instant answers from FB's ad library

World’s first Open Source AI content writing platform that empowers users to create SEO-friendly blog posts, ad copy for social media, website landing pages, and more.

Code underlying our publication "Modeling the Distribution of Normal Data in Pre-Trained Deep Features for Anomaly Detection" at ICPR2020

This is an unofficial implementation of Reconstruction by inpainting for visual anomaly detection (RIAD).