Graph Transforms to Quantize and Retrain Deep Neural Nets in TensorFlow

Code for "Are Powerful Graph Neural Nets Necessary? A Dissection on Graph Classification"

MatTen: Equivariant Graph Neural Nets for Tensorial Properties of Materials

Graph neural networks tutorial in pytorch (GCN, GAT, Node2vec, GraphSAge, ClusterGCN, ...)

Prepare to Technical Skills Here are the essential skills that a Machine Learning Engineer needs, as mentioned Read me files. Within each group are topics that you should be familiar with. Study Tip: Copy and paste this list into a document and save to your computer for easy referral. Computer Science Fundamentals and Programming Topics Data structures: Lists, stacks, queues, strings, hash maps, vectors, matrices, classes & objects, trees, graphs, etc. Algorithms: Recursion, searching, sorting, optimization, dynamic programming, etc. Computability and complexity: P vs. NP, NP-complete problems, big-O notation, approximate algorithms, etc. Computer architecture: Memory, cache, bandwidth, threads & processes, deadlocks, etc. Probability and Statistics Topics Basic probability: Conditional probability, Bayes rule, likelihood, independence, etc. Probabilistic models: Bayes Nets, Markov Decision Processes, Hidden Markov Models, etc. Statistical measures: Mean, median, mode, variance, population parameters vs. sample statistics etc. Proximity and error metrics: Cosine similarity, mean-squared error, Manhattan and Euclidean distance, log-loss, etc. Distributions and random sampling: Uniform, normal, binomial, Poisson, etc. Analysis methods: ANOVA, hypothesis testing, factor analysis, etc. Data Modeling and Evaluation Topics Data preprocessing: Munging/wrangling, transforming, aggregating, etc. Pattern recognition: Correlations, clusters, trends, outliers & anomalies, etc. Dimensionality reduction: Eigenvectors, Principal Component Analysis, etc. Prediction: Classification, regression, sequence prediction, etc.; suitable error/accuracy metrics. Evaluation: Training-testing split, sequential vs. randomized cross-validation, etc. Applying Machine Learning Algorithms and Libraries Topics Models: Parametric vs. nonparametric, decision tree, nearest neighbor, neural net, support vector machine, ensemble of multiple models, etc. Learning procedure: Linear regression, gradient descent, genetic algorithms, bagging, boosting, and other model-specific methods; regularization, hyperparameter tuning, etc. Tradeoffs and gotchas: Relative advantages and disadvantages, bias and variance, overfitting and underfitting, vanishing/exploding gradients, missing data, data leakage, etc. Software Engineering and System Design Topics Software interface: Library calls, REST APIs, data collection endpoints, database queries, etc. User interface: Capturing user inputs & application events, displaying results & visualization, etc. Scalability: Map-reduce, distributed processing, etc. Deployment: Cloud hosting, containers & instances, microservices, etc. Move on to the final lesson of this course to find lots of sample practice questions for each topic!

Graph Networks por Benjamin Sanchez-Lengeling

Interpretability on raw time series with graph neural nets and concept activation vectors. Featured at AAAI 2023.

Implementation of a Deep Graph Convolutional Neural Network to classify convex and concave polygons. Ultimate goal of this project is to be able to use Graph Nets as substitutes of Finite Element Methods (FEM).

useful links + started code on graph neural nets!

A package for using convolutional neural nets to learn a graph fingerprint.

Tool for converting graphs into neural nets with feedback and biological inductive biases

Training graph neural nets for millions of proteins on Amazon SageMaker and DocumentDB.

This repository contains the Tensorflow implementation of the papers "Dim but not entirely dark: Extracting the Galactic Center Excess' source-count distribution with neural nets" (arXiv:2107.09070) and "The GCE in a New Light: Disentangling the γ-ray Sky with Bayesian Graph Convolutional Neural Networks" (arXiv:2006.12504).

Getting familiar with Graph Neural Nets and the pytorch_geometric library

Learning Conjoint Attentions for Graph Neural Nets, NeurIPS 2021

In this repo I have implement different applications of GNN which covers Node Level, Edge level, Graph level tasks with different GNN variants such as GCN, GAT, Graph SAGE

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This is a collection of all my learning material and implementations of Graph Neural Nets

Here we provide an implementation of graph neural networks for multi-omics data.

Playing around with graph neural nets

Part of my thesis work. Doing convolutional neural nets on protein graphs to make predictions.

Unified framework for artificial superintelligence — 29 modules, 4,355+ tests, 215K LOC. Consciousness · Cognitive Blackboard · ZK Bridge · Knowledge Graphs · Causal Inference · Federated Learning · BCI · Spiking Neural Nets · Quantum Hybrid

Deep dive into Graph Neural Nets

Basic implementation of a 2-Layer GNN architecture for Node Level task prediction on CORA citation data.

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Fast generation of Sign Distance Fields (SDF) in PyTorch for geometrical meshes & domains in CFD and graph neural nets

Improving the speed of Visibility Graph Analysis by two orders of magnitude with the help of Neural Nets. This project was conducted as part of the Final Thesis for MSc Architectural Computation, Bartlett, University College London.

Program/Method designed to classify a triple in an AMR graph as either good or bad, based on how frequently similar triples appeared elsewhere. 'Similar' is defined as an AMR graph whose components have similar vectors in a word embedding. This project attempts to use neural nets to do the classification.

Graph Neural Nets