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aggcn

Attention Guided Graph Convolutional Networks for Relation Extraction (authors' PyTorch implementation for the ACL19 paper)

arga

This is a TensorFlow implementation of the Adversarially Regularized Graph Autoencoder(ARGA) model as described in our paper: Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018). Adversarially Regularized Graph Autoencoder for Graph Embedding, [https://www.ijcai.org/proceedings/2018/0362.pdf].

attention-based-bilstm-relation-extraction

Tensorflow Implementation of "Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification" (ACL 2016)

bert-aggcn

Attention Guided Graph Convolutional Networks over pretrained BERT Embeddings for Relation Extraction

deepsignalingpathwayprediction

disease-symptoms-relation-extraction

This is a complete approach for disease-symptoms relations extraction from web articles. In this approach implemented a novel algorithm for text analysis and relationship extraction by combining a machine learning method and a pattern-based method.

gae

Implementation of Graph Auto-Encoders in TensorFlow

gae-pytorch

Graph Auto-Encoder in PyTorch

gae_vgae

Reimplementation of VGAE by Pytorch. The paper is "Variational Graph Auto-Encoders".

gat

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

gate

Graph Attention Auto-Encoders

gcn

Implementation of Graph Convolutional Networks in TensorFlow

gluon-nlp

NLP made easy

graph-vae

graph2go

Graph-based representation learning method for protein function prediction

graphgan

A tensorflow implementation of GraphGAN (Graph Representation Learning with Generative Adversarial Nets)

graphsage

Representation learning on large graphs using stochastic graph convolutions.

informationextraction

knowledge_graph_cmput656

using knowledge graph in Relation Extraction

machine_learning_refined

Notes, examples, and Python demos for the textbook "Machine Learning Refined" (published by Cambridge University Press).

medcat

Medical Concept Annotation Tool

mmd-vae

Pytorch implementation of Maximum Mean Discrepancy Variational Autoencoder, a member of the InfoVAE family that maximizes Mutual Information between the Isotropic Gaussian Prior (as the latent space) and the Data Distribution.

momix-notebook

multiomic_predictions

negspacy

spaCy pipeline object for negating concepts in text

paper-reading-for-relation-extraction-2019

paper list from ACL2019 and EMNLP2019 for relation extraction

relation-autoencoder

this is the code used in the paper "Discrete-State Variational Autoencoders for Joint Discovery and Factorization of Relations"

rna-seq-vae

Synthetic gene expression data generation using Variational Auto-Encoder

s-vgae

trvae

Conditional out-of-distribution prediction