132 Open source projects that are alternatives of or similar to robust-gcn

Implementation of the WSDM 2021 paper "Node Similarity Preserving Graph Convolutional Networks"

(ICML 2022) Official PyTorch implementation of “Blurs Behave Like Ensembles: Spatial Smoothings to Improve Accuracy, Uncertainty, and Robustness”.

Source code and dataset for the CCKS201 paper "On Robustness and Bias Analysis of BERT-based Relation Extraction"

Code and models for ICCV2021 paper "Robust Object Detection via Instance-Level Temporal Cycle Confusion".

Square Attack: a query-efficient black-box adversarial attack via random search [ECCV 2020]

Material for the EPFL master course "A Network Tour of Data Science", edition 2019.

Code and data for the ICLR 2021 paper "Perceptual Adversarial Robustness: Defense Against Unseen Threat Models".

Minimal implementation of Denoised Smoothing (https://arxiv.org/abs/2003.01908) in TensorFlow.

Reinforced Neighborhood Selection Guided Multi-Relational Graph Neural Networks

Code for NeurIPS 2019 Paper

Reachability analysis for closed-loop control systems

The official code and datasets for "Zero-Shot Multi-View Indoor Localization via Graph Location Networks" (ACMMM 2020)

GalaXC: Graph Neural Networks with Labelwise Attention for Extreme Classification

NIPS Adversarial Vision Challenge

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning (authors' MXNet implementation for the TACL19 paper)

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

Visualization tool for Graph Neural Networks

Figures & code from the paper "Shortcut Learning in Deep Neural Networks" (Nature Machine Intelligence 2020)

An Algorithm to Quantify Robustness of Recurrent Neural Networks

关于domain generalization，domain adaptation，causality，robutness，prompt，optimization，generative model各式各样研究的阅读笔记

Pre-Training Buys Better Robustness and Uncertainty Estimates (ICML 2019)

[NeurIPS'2019] Shupeng Gui, Haotao Wang, Haichuan Yang, Chen Yu, Zhangyang Wang, Ji Liu, “Model Compression with Adversarial Robustness: A Unified Optimization Framework”

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

Disentagnled Graph Collaborative Filtering, SIGIR2020

Benchmarks for Corruption Invariant Person Re-identification. [NeurIPS 2021 Track on Datasets and Benchmarks]

A distributed graph deep learning framework.

Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach, AAAI2020

Code for the CHAMPS Predicting Molecular Properties Kaggle competition

PyTorch Implementation of Google's Natural TTS Synthesis by Conditioning WaveNet on Mel Spectrogram Predictions. This implementation supports both single-, multi-speaker TTS and several techniques to enforce the robustness and efficiency of the model.

StellarGraph - Machine Learning on Graphs

Generative Forests in Python

A PyTorch implementation of "SelfGNN: Self-supervised Graph Neural Networks without explicit negative sampling" paper, which appeared in The International Workshop on Self-Supervised Learning for the Web (SSL'21) @ the Web Conference 2021 (WWW'21).

Adversarial Distributional Training (NeurIPS 2020)

Code for "Distributed, Egocentric Representations of Graphs for Detecting Critical Structures" (ICML 2019)

Robust error-handling for Kotlin and Android

[NeurIPS 2021]: Are Transformers More Robust Than CNNs? (Pytorch implementation & checkpoints)

RayS: A Ray Searching Method for Hard-label Adversarial Attack (KDD2020)

Code and data for ECML-PKDD paper "Social Influence Attentive Neural Network for Friend-Enhanced Recommendation"

[CVPR 2022] S-attack library. Official implementation of two papers "Vehicle trajectory prediction works, but not everywhere" and "Are socially-aware trajectory prediction models really socially-aware?".

Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.

Contains code for the paper "Vision Transformers are Robust Learners" (AAAI 2022).

Summary of social recommendation papers and codes

A Closer Look at Accuracy vs. Robustness

Demo of RouteNet in ACM SIGCOMM'19

Code used during my Chaos Engineering and Resiliency Patterns talk.

Implementation of the KDD 2020 paper "Graph Structure Learning for Robust Graph Neural Networks"

Python toolbox to evaluate graph vulnerability and robustness (CIKM 2021)

Code for the paper "How Attentive are Graph Attention Networks?" (ICLR'2022)

Code for "Depth Uncertainty in Neural Networks" (https://arxiv.org/abs/2006.08437)

Context-Aware Visual Compatibility Prediction (https://arxiv.org/abs/1902.03646)

PyBullet CartPole and Quadrotor environments—with CasADi symbolic a priori dynamics—for learning-based control and RL

Making self-supervised learning work on molecules by using their 3D geometry to pre-train GNNs. Implemented in DGL and Pytorch Geometric.

Sensor data aggregation tool for any numerical sensor data. Robust and privacy-friendly.

A curated (most recent) list of resources for Learning with Noisy Labels

Recentrifuge: robust comparative analysis and contamination removal for metagenomics

Code for AAAI 2018 accepted paper: "Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients"

阿里云MNS

PyTorch code for CVIU paper "AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction"

Code and resources on scalable and efficient Graph Neural Networks

odr.chalmers.se/handle/20.500.12380/256629?locale=en