56 Open source projects that are alternatives of or similar to spatio-temporal-brain

STAGIN: Spatio-Temporal Attention Graph Isomorphism Network

Code and resources on scalable and efficient Graph Neural Networks

XAI Tutorial for the Explainable AI track in the ALPS winter school 2021

Visualization tool for Graph Neural Networks

Adaptive, interpretable wavelets across domains (NeurIPS 2021)

Collection of NLP model explanations and accompanying analysis tools

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

Code for reproducing results in GraphMix paper

Fusion ICA Toolbox (MATLAB)

Easy to use web database for statistical maps.

Python toolbox for analyzing imaging data

VisualQC : assistive tool to ease the quality control workflow of neuroimaging data.

fmriflows is a consortium of many (dependent) fMRI analysis pipelines, including anatomical and functional pre-processing, univariate 1st and 2nd-level analysis, as well as multivariate pattern analysis.

Graph theory analysis of brain MRI data

Connectome Mapper 3 is a BIDS App that implements full anatomical, diffusion, resting/state functional MRI, and recently EEG processing pipelines, from raw T1 / DWI / BOLD , and preprocessed EEG data to multi-resolution brain parcellation with corresponding connection matrices.

A Reproducible Workflow for Structural and Functional Connectome Ensemble Learning

A game theoretic approach to explain the output of any machine learning model.

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning

Fit interpretable models. Explain blackbox machine learning.

A lightweight implementation of removal-based explanations for ML models.

CARLA: A Python Library to Benchmark Algorithmic Recourse and Counterfactual Explanation Algorithms

For calculating global feature importance using Shapley values.

Papers about explainability of GNNs

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Contextual AI adds explainability to different stages of machine learning pipelines - data, training, and inference - thereby addressing the trust gap between such ML systems and their users. It does not refer to a specific algorithm or ML method — instead, it takes a human-centric view and approach to AI.

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

This project provides responsible AI user interfaces for Fairlearn, interpret-community, and Error Analysis, as well as foundational building blocks that they rely on.

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

[ICCV 2021- Oral] Official PyTorch implementation for Generic Attention-model Explainability for Interpreting Bi-Modal and Encoder-Decoder Transformers, a novel method to visualize any Transformer-based network. Including examples for DETR, VQA.

Data generator for Arena - interactive XAI dashboard

Library implementing state-of-the-art Concept-based and Disentanglement Learning methods for Explainable AI

Must-read papers on graph neural networks (GNN)

TensorFlow GNN is a library to build Graph Neural Networks on the TensorFlow platform.

PyTorch implementation of MTAD-GAT (Multivariate Time-Series Anomaly Detection via Graph Attention Networks) by Zhao et. al (2020, https://arxiv.org/abs/2009.02040).

[CVPR 2020 VL3] The repository for meta fine-tuning in cross-domain few-shot learning.

GemNet model in PyTorch, as proposed in "GemNet: Universal Directional Graph Neural Networks for Molecules" (NeurIPS 2021)

A reading list for deep graph learning acceleration.

A review of papers proposing novel GNN methods with application to brain connectivity published in 2017-2020.

Pytorch implementation of CVPR2020 paper “VectorNet: Encoding HD Maps and Agent Dynamics from Vectorized Representation”

The official PyTorch implementation of "Pathfinder Discovery Networks for Neural Message Passing" (WebConf '21)

Source code for GNN-LSPE (Graph Neural Networks with Learnable Structural and Positional Representations), ICLR 2022

List of Publications in Graph Contrastive Learning

A real-time GNN-based method. Understanding Image Retrieval Re-Ranking: A Graph Neural Network Perspective

An index of recommendation algorithms that are based on Graph Neural Networks.

This shows a basic implementation of the global nearest neighbour (GNN) multi target Tracker. Kalman filter is used for Tracking and Auction Algorithm for determining the assignment of measurments to filters.

A Tensorflow implementation of "Bayesian Graph Convolutional Neural Networks" (AAAI 2019).

Clean & Documented TF2 implementation of "An end-to-end deep learning architecture for graph classification" (M. Zhang et al., 2018).

A Pytorch implementation of CVPR 2020 paper: Multi-Modal Graph Neural Network for Joint Reasoning on Vision and Scene Text

Learning cell communication from spatial graphs of cells

Causing: CAUsal INterpretation using Graphs

Federated learning on graph, especially on graph neural networks (GNNs), knowledge graph, and private GNN.

A Keras library for multi-step time-series forecasting.

PyTorch implementation of Densely Connected Time Delay Neural Network

Source code (train/test) accompanying the paper entitled "Veritatem Dies Aperit - Temporally Consistent Depth Prediction Enabled by a Multi-Task Geometric and Semantic Scene Understanding Approach" in CVPR 2019 (https://arxiv.org/abs/1903.10764).