Using Gaussian Processes for Deep Neural Network Predictive Uncertainty Estimation
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Updated
Mar 21, 2019 - Python
Using Gaussian Processes for Deep Neural Network Predictive Uncertainty Estimation
pytorch implementation of Parametric Noise Injection for adversarial defense
Code for our NeurIPS 2019 *spotlight* "Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers"
Implementation of paper "Transferring Robustness for Graph Neural Network Against Poisoning Attacks".
[Partial] RADLER: (adversarially) Robust Adversarial Distributional LEaRner
😎 A curated list of awesome real-world adversarial examples resources
6th place solution to KDD CUP 2020 Graph Adversarial Attacks & Defense
some examples for adversarial attack and defense with pytorch
📕 Adversarial Attacks and Defenses for Image-Based Recommendation Systems using Deep Neural Networks.
Adversarial detection and defense for deep learning systems using robust feature alignment
This is the course project for CSCE585: ML Systems. Students will build their machine learning systems based on the provided infrastructure --- Athena.
This repository contains the implementation of three adversarial example attack methods FGSM, IFGSM, MI-FGSM and one Distillation as defense against all attacks using MNIST dataset.
Final Year Thesis Project (COMP4981H) for Computer Science Students in HKUST
Code for the paper "Consistency Regularization for Certified Robustness of Smoothed Classifiers" (NeurIPS 2020)
Adversarial attacks on Deep Reinforcement Learning (RL)
Adversarial Distributional Training (NeurIPS 2020)
Adversarial Defense using Generative Adversarial Networks
Sinkhorn Adversarial Training (SAT): Optimal Transport as a Defense Against Adversarial Attacks
Provably defending pretrained classifiers including the Azure, Google, AWS, and Clarifai APIs
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