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To respond to these scenarios, KGLIB is the centre of all research projects conducted at Grakn Labs. In particular, its focus is on the integration of machine learning with the Grakn Knowledge Graph. More on this below, in Knowledge Graph Tasks. At present this repo contains one project: Knowledge Graph Convolutional Networks (KGCNs). Go there for more info on getting started with a working example.

https://vaticle.comhttps://github.com/vaticle/kglib

StellarGraph is a Python library for machine learning on graphs and networks. StellarGraph is built on TensorFlow 2 and its Keras high-level API, as well as Pandas and NumPy. It is thus user-friendly, modular and extensible. It interoperates smoothly with code that builds on these, such as the standard Keras layers and scikit-learn, so it is easy to augment the core graph machine learning algorithms provided by StellarGraph. It is thus also easy to install with pip or Anaconda.

machine-learning graphs machine-learning-algorithms networkx graph-data graph-analysis graph-machine-learning link-prediction graph-convolutional-networks gcn saliency-map interpretability geometric-deep-learning graph-neural-networks heterogeneous-networks stellargraph-libraryGorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.

machine-learning deep-neural-networks deep-learning neural-network automatic-differentiation artificial-intelligence computation-graph deeplearning gradient-descent hacktoberfest differentiation symbolic-differentiation graph-computationOpen source library based on TensorFlow that predicts links between concepts in a knowledge graph. AmpliGraph is a suite of neural machine learning models for relational Learning, a branch of machine learning that deals with supervised learning on knowledge graphs.

machine-learning knowledge-graph relational-learning representation-learning graph-representation-learning graph-embeddings knowledge-graph-embeddingsThe Microsoft Cognitive Toolkit is a free, easy-to-use, open-source, commercial-grade toolkit that trains deep learning algorithms to learn like the human brain. It is a unified deep-learning toolkit that describes neural networks as a series of computational steps via a directed graph.

deep-learning neural-networks artificial-intelligenceWe also have a slack channel for real-time discussion. If you want to join the channel, contact mufeili1996@gmail.com. Deep learning on graphs has been an arising trend in the past few years. There are a lot of graphs in life science such as molecular graphs and biological networks, making it an import area for applying deep learning on graphs. DGL-LifeSci is a DGL-based package for various applications in life science with graph neural networks.

bioinformatics deep-learning cheminformatics molecule drug-discovery geometric-deep-learning graph-neural-networks dglGraph-Learn (formerly AliGraph) is a distributed framework designed for the development and application of large-scale graph neural networks. It abstracts a set of programming paradigms suitable for common graph neural network models from the practical problems of large-scale graph training, and has been successfully applied to many scenarios such as search recommendation, network security, knowledge graph, etc. within Alibaba. Graph-Learn provides both Python and C++ interfaces for graph sampling operations, and provides a gremlin-like GSL (Graph Sampling Language) interface. For upper layer graph learning models, Graph-Learn provides a set of paradigms and processes for model development. It is compatible with TensorFlow and PyTorch, and provides data layer, model layer interfaces and rich model examples.

graph tensorflow pytorch graph-neural-networks gnn aligraph graphlearn gnn-frameworkJraph (pronounced "giraffe") is a lightweight library for working with graph neural networks in jax. It provides a data structure for graphs, a set of utilities for working with graphs, and a 'zoo' of forkable graph neural network models. Jraph is designed to provide utilities for working with graphs in jax, but doesn't prescribe a way to write or develop graph neural networks.

machine-learning deep-learning jax graph-neural-networksGraph Nets is DeepMind's library for building graph networks in Tensorflow and Sonnet. Contact graph-nets@google.com for comments and questions.

graph-networks graphs deep-learning neural-networks tensorflow sonnet artificial-intelligenceDGL is an easy-to-use, high performance and scalable Python package for deep learning on graphs. DGL is framework agnostic, meaning if a deep graph model is a component of an end-to-end application, the rest of the logics can be implemented in any major frameworks, such as PyTorch, Apache MXNet or TensorFlow. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks.

deep-learning graph-neural-networksKnowledge graphs (KGs) are data structures that store information about different entities (nodes) and their relations (edges). A common approach of using KGs in various machine learning tasks is to compute knowledge graph embeddings. DGL-KE is a high performance, easy-to-use, and scalable package for learning large-scale knowledge graph embeddings. The package is implemented on the top of Deep Graph Library (DGL) and developers can run DGL-KE on CPU machine, GPU machine, as well as clusters with a set of popular models, including TransE, TransR, RESCAL, DistMult, ComplEx, and RotatE. This command will download the FB15k dataset, train the transE model and save the trained embeddings into the file.

machine-learning knowledge-graph knowledge-graphs-embeddings graph-learning dglTensorFlow is a library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code.

artificial-intelligence neural-networks machine-learning deep-learning numerical-computationA distributed graph deep learning framework.

graph graph-learning network-embedding deep-learning graph-convolutional-networks graph-neural-networks graphsage random-walk node2vec graph-embedding gcn ggnnThe Knowledge Graph

grakn graql knowledge-base knowledge-graph knowledge-representation reasoning relational-databases hyper-relational database graph graph-database graph-visualization logic deductions knowledge-engineering enterprise-knowledge-graph knowledge-engine query-language hyper-relational-database inferenceA comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

machine-learning deep-learning tensorflow pytorch keras matplotlib aws kaggle pandas scikit-learn torch artificial-intelligence neural-network convolutional-neural-networks tensorflow-tutorials python-data ipython-notebook capsule-networkTensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within

Graph Auto-Encoders (GAEs) are end-to-end trainable neural network models for unsupervised learning, clustering and link prediction on graphs. T. N. Kipf, M. Welling, Semi-Supervised Classification with Graph Convolutional Networks, ICLR (2017).

Gorgonia is a library that helps facilitate machine learning in Go. Write and evaluate mathematical equations involving multidimensional arrays easily. If this sounds like Theano or TensorFlow, it's because the idea is quite similar. Specifically, the library is pretty low-level, like Theano, but has higher goals like Tensorflow.The main reason to use Gorgonia is developer comfort. If you're using a Go stack extensively, now you have access to the ability to create production-ready machine learning systems in an environment that you are already familiar and comfortable with.

machine-learning artificial-intelligence neural-network computation-graph differentiation gradient-descent gorgonia deep-learning deeplearning deep-neural-networks automatic-differentiation symbolic-differentiation go-libraryA Machine Learning library written in pure Go designed to support relevant neural architectures in Natural Language Processing. spaGO is self-contained, in that it uses its own lightweight computational graph framework for both training and inference, easy to understand from start to finish.

nlp machine-learning natural-language-processing deep-learning neural-network automatic-differentiation artificial-intelligence recurrent-networks lstm computation-graph question-answering bart automatic-translation deeplearning language-model bert transformer-architecture bert-as-service named-entities-recognitionSuperGlue is a CVPR 2020 research project done at Magic Leap. The SuperGlue network is a Graph Neural Network combined with an Optimal Matching layer that is trained to perform matching on two sets of sparse image features. This repo includes PyTorch code and pretrained weights for running the SuperGlue matching network on top of SuperPoint keypoints and descriptors. Given a pair of images, you can use this repo to extract matching features across the image pair. Full paper PDF: SuperGlue: Learning Feature Matching with Graph Neural Networks.

deep-learning pose-estimation feature-matching graph-neural-networksWelcome to the open-source repository for the Intel® nGraph™ Library. Our code base provides a Compiler and runtime suite of tools (APIs) designed to give developers maximum flexibility for their software design, allowing them to create or customize a scalable solution using any framework while also avoiding device-level hardware lock-in that is so common with many AI vendors. A neural network model compiled with nGraph can run on any of our currently-supported backends, and it will be able to run on any backends we support in the future with minimal disruption to your model. With nGraph, you can co-evolve your software and hardware's capabilities to stay at the forefront of your industry. The nGraph Compiler is Intel's graph compiler for Artificial Neural Networks. Documentation in this repo describes how you can program any framework to run training and inference computations on a variety of Backends including Intel® Architecture Processors (CPUs), Intel® Nervana™ Neural Network Processors (NNPs), cuDNN-compatible graphics cards (GPUs), custom VPUs like Movidius, and many others. The default CPU Backend also provides an interactive Interpreter mode that can be used to zero in on a DL model and create custom nGraph optimizations that can be used to further accelerate training or inference, in whatever scenario you need.

ngraph tensorflow mxnet deep-learning compiler performance onnx paddlepaddle neural-network deep-neural-networks pytorch caffe2
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