Displaying 1 to 8 from 8 results

A 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 ggnnStellarGraph 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-libraryTo 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.

machine-learning ai neural-network graph tensorflow graphs ml artificial-intelligence knowledge-graph knowledgebase knowledge-graph-completion relational-learning link-prediction graph-convolutional-networks grakn graql geometric-deep-learning graph-networksThis is the code for the paper Learning non-maximum suppression. Jan Hosang, Rodrigo Benenson, Bernt Schiele. CVPR 2017. Run make to compile C++ code and protobufs.

tensorflow object-detection non-maximum-suppression graph-convolutional-networksThe paper is available on arXiv, https://arxiv.org/abs/1807.01985. From left: tox21 pyridine (C5H5N), tox21 SR-MMP, delaney solubility visualization.

deep-learning chemistry neural-network graph-convolutional-networks chainer saliency interpretabilityChainer Chemistry is a collection of tools to train and run neural networks for tasks in biology and chemistry using Chainer[1]. It supports various state-of-the-art deep learning neural network models (especially Graph Convolution Neural Network) for chemical molecule property prediction.

deep-learning chemistry neural-network biology chainer graph-convolutional-networksDisclaimer: PFN provides no warranty or support for this implementation. Use it at your own risk. See license for details. This is not the original author's implementation. This implementation was based on https://github.com/mdeff/cnn_graph.

graph-convolutional-networks chainerThis repository contains code necessary to run the Decagon algorithm. Decagon is a method for learning node embeddings in multimodal graphs, and is especially useful for link prediction in highly multi-relational settings. See our paper for details on the algorithm. Decagon is used to address a burning question in pharmacology, which is that of predicting safety of drug combinations.

graph-convolutional-networks deep-learning representation-learning embeddings pharmacology
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**