Presenter
Papers
Paper URL
Our Slides
Generate
Maximum-Likelihood Augmented Discrete Generative Adversarial Networks
PDF
Tkach PDF + GaoJi Pdf
Generate
Graphical Generative Adversarial Networks
PDF
Arshdeep PDF
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GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models, ICML2018
PDF
Arshdeep PDF
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Inference in probabilistic graphical models by Graph Neural Networks
PDF
Arshdeep PDF
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Encoding robust representation for graph generation
Pdf
Arshdeep PDF
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Junction Tree Variational Autoencoder for Molecular Graph Generation
Pdf
Tkach PDF + Arshdeep Pdf
Generate
Graph Convolutional Policy Network for Goal-Directed Molecular Graph Generation NeurIPS18
Tkach PDF
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Towards Variational Generation of Small Graphs
Pdf
Tkach PDF + Arshdeep Pdf
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Convolutional Imputation of Matrix Networks
Pdf
Tkach PDF
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Graph Convolutional Matrix Completion
Pdf
Tkach PDF
Generate
NetGAN: Generating Graphs via Random Walks ICML18
[ULR]
Tkach PDF
Beam
Stochastic Beams and Where to Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement
URL
Tkach PDF
Tags:
Autoencoder ,
Beam ,
discrete ,
GAN ,
generative ,
graph ,
graphical-model ,
imputation ,
Matrix-Completion ,
molecule ,
NLP ,
random ,
RL ,
RNN ,
robustness ,
Variational
Categories:
2GraphsNN ,
5Generative
Updated: March 15, 2019