Displaying 1 to 6 from 6 results

gossipnet - Non-maximum suppression for object detection in a neural network

  •    Python

This 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.

bayesgrad - BayesGrad: Explaining Predictions of Graph Convolutional Networks

  •    Jupyter

The paper is available on arXiv, https://arxiv.org/abs/1807.01985. From left: tox21 pyridine (C5H5N), tox21 SR-MMP, delaney solubility visualization.

chainer-chemistry - Chainer Chemistry: A Library for Deep Learning in Biology and Chemistry

  •    Python

Chainer 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.




chainer-graph-cnn - Chainer implementation of 'Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering' (https://arxiv

  •    Python

Disclaimer: 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.

decagon - Graph convolutional neural network for multirelational link prediction

  •    Jupyter

This 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.