Popular GitHub Repositories for Moving Target Defense

An AI-Powered Speech Processing Toolkit and Open Source SOTA Pretrained Models, Supporting Speech Enhancement, Separation, and Target Speaker Extraction, etc.

AI Search & RAG Without Moving Your Data. Get instant answers from your company's knowledge across 100+ apps while keeping data secure. Deploy in minutes, not months.

Meltano: the declarative code-first data integration engine that powers your wildest data and ML-powered product ideas. Say goodbye to writing, maintaining, and scaling your own API integrations.

FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai

Baidu's open-source Sentiment Analysis System.

Provide an input CSV and a target field to predict, generate a model + code to run it.

Find big moving stocks before they move using machine learning and anomaly detection

Must-read Papers on Textual Adversarial Attack and Defense

[ICLR 2019] ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

Chinese safety prompts for evaluating and improving the safety of LLMs. 中文安全prompts，用于评估和提升大模型的安全性。

A pytorch adversarial library for attack and defense methods on images and graphs

Sentiment Analysis, Text Classification, Text Augmentation, Text Adversarial defense, etc.;

Simple and Efficient Tensorflow implementations of NER models with tf.estimator and tf.data

We present MocapNET, a real-time method that estimates the 3D human pose directly in the popular Bio Vision Hierarchy (BVH) format, given estimations of the 2D body joints originating from monocular color images. Our contributions include: (a) A novel and compact 2D pose NSRM representation. (b) A human body orientation classifier and an ensemble of orientation-tuned neural networks that regress the 3D human pose by also allowing for the decomposition of the body to an upper and lower kinematic hierarchy. This permits the recovery of the human pose even in the case of significant occlusions. (c) An efficient Inverse Kinematics solver that refines the neural-network-based solution providing 3D human pose estimations that are consistent with the limb sizes of a target person (if known). All the above yield a 33% accuracy improvement on the

:book: 收集NLP领域相关的数据集、论文、开源实现，尤其是情感分析、情绪原因识别、评价对象和评价词抽取方面。

An extension of XGBoost to probabilistic modelling

? Detect , track and extract the optimal face in multi-target faces (exclude side face and select the optimal face).

Official implementation of the paper: MonoRec: Semi-Supervised Dense Reconstruction in Dynamic Environments from a Single Moving Camera (CVPR 2021)

Model Merging in LLMs, MLLMs, and Beyond: Methods, Theories, Applications and Opportunities. arXiv:2408.07666.

ConvLSTM/ConvGRU (Encoder-Decoder) with PyTorch on Moving-MNIST