Pinned Repositories
100days-ML-code
100天机器学习 （翻译+ 实操）
2019-CCF-BDCI-Car_sales
2019年CCF大数据与计算智能大赛乘用车细分市场销量预测冠军解决方案
adv-attacks-vae
Code to reproduce the experiments of the paper "Adversarial Attacks on Variational Autoencoders" - Gondim-Ribeiro et al., 2018.
AdvBox
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
adversarial-attack-on-GMM-i-vector-based-speaker-verification-systems
Implementation of Adversarial Attacks on GMM i-vector based Speaker Verification Systems (ICASSP2020) https://arxiv.org/abs/1911.03078
Adversarial-Contrastive-Learning
[NeurIPS 2020] “ Robust Pre-Training by Adversarial Contrastive Learning”, Ziyu Jiang, Tianlong Chen, Ting Chen, Zhangyang Wang
adversarial-recommender-systems-survey
The goal of this survey is two-fold: (i) to present recent advances on adversarial machine learning (AML) for the security of RS (i.e., attacking and defense recommendation models), (ii) to show another successful application of AML in generative adversarial networks (GANs) for generative applications, thanks to their ability for learning (high-dimensional) data distributions. In this survey, we provide an exhaustive literature review of 74 articles published in major RS and ML journals and conferences. This review serves as a reference for the RS community, working on the security of RS or on generative models using GANs to improve their quality.
AdversarialAttack
Creating an Adversarial Attack and Implementing Techniques to prevent them
Chinese_Rumor_Dataset
中文谣言数据
Deep-Learning-with-PyTorch-Tutorials
深度学习与PyTorch入门实战视频教程 配套源代码和PPT
baobunuo's Repositories
baobunuo/AdvBox
Advbox is a toolbox to generate adversarial examples that fool neural networks in PaddlePaddle、PyTorch、Caffe2、MxNet、Keras、TensorFlow and Advbox can benchmark the robustness of machine learning models. Advbox give a command line tool to generate adversarial examples with Zero-Coding.
baobunuo/attack_vae
Diagnosing Vulnerability of Variational Auto-Encoders to Adversarial Attacks
baobunuo/Awesome-Federated-Learning
Federated Learning Library: https://fedml.ai
baobunuo/BUAAthesis
北航毕设论文LaTeX模板
baobunuo/CSBook
计算机类常用电子书整理，并且附带下载链接，包括Java，Python，Linux，Go，C，C++，数据结构与算法，人工智能，计算机基础，面试，设计模式，数据库，前端等书籍
baobunuo/datasets
🤗 The largest hub of ready-to-use datasets for ML models with fast, easy-to-use and efficient data manipulation tools
baobunuo/EvalNE
Source code for EvalNE, a Python library for evaluating Network Embedding methods.
baobunuo/FATE
An Industrial Grade Federated Learning Framework
baobunuo/grabnel
[NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)
baobunuo/Graph-Adversarial-Learning
A curated collection of adversarial attack and defense on graph data.
baobunuo/graph-de-anonymization
Seed based and seed-free graph de-anonymization.
baobunuo/graph-information-bottleneck-for-Subgraph-Recognition
baobunuo/GraphLeaks
Code for the paper "Quantifying Privacy Leakage in Graph Embedding" published in MobiQuitous 2020
baobunuo/GraphMI
Official Pytorch implementation of IJCAI'21 paper "GraphMI: Extracting Private Graph Data from Graph Neural Networks"
baobunuo/MGE
implementation of MGE: Multi-view Graph Contrastive Encoding for Graph Neural Networks Pre-training
baobunuo/models
baobunuo/openfl
An open framework for Federated Learning.
baobunuo/OpenNE
An Open-Source Package for Network Embedding (NE)
baobunuo/PySyft
A library for answering questions using data you cannot see
baobunuo/ReFine
Official code of "Towards Multi-Grained Explainability for Graph Neural Networks" (2021 NeurIPS)
baobunuo/reliable_gnn_via_robust_aggregation
This repository contains the official implementation of the paper "Reliable Graph Neural Networks via Robust Aggregation" (NeurIPS, 2020).
baobunuo/robustness_of_gnns_at_scale
This repository contains the official implementation of the paper "Robustness of Graph Neural Networks at Scale" (NeurIPS, 2021).
baobunuo/Rosetta
A Privacy-Preserving Framework Based on TensorFlow
baobunuo/SEAL_OGB
An open-source implementation of SEAL for link prediction in open graph benchmark (OGB) datasets.
baobunuo/Secure-Network-Release-with-Link-Privacy
baobunuo/Social-Circles
Discovering Social Circles- A Topological Data Analysis Project
baobunuo/Variational-Recurrent-Autoencoder-Tensorflow
A tensorflow implementation of "Generating Sentences from a Continuous Space"
baobunuo/VGAE_base_privacy_protect
baobunuo/VGNAE
(CIKM'21) Variational Graph Normalized Auto-Encoders
baobunuo/WARGA
The experimental implementation for the paper Wasserstein Adversarially Regularized Graph Autoencoder