Fetching the paper…
Reading the bibliography…
Statistical generative models for molecular graphs attract attention from many researchers from the fields of bio- and chemo-informatics.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Michael F Hutchinson · 1990
Earlier work this paper cites.
log det a= tr log a
Christopher S Withers and Saralees Nadarajah · 2010
Earlier work this paper cites.
Zinc: a free tool to discover chemistry for biology
John J Irwin, Teague Sterling, Michael M Mysinger, Erin S Bolstad, and Ryan G Coleman · 2012
Earlier work this paper cites.
Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
Earlier work this paper cites.
Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
Earlier work this paper cites.
Quantum chemistry structures and properties of 134 kilo molecules
Raghunathan Ramakrishnan, Pavlo O Dral, Matthias Rupp, and O Anatole Von Lilienfeld · 2014
Earlier work this paper cites.
Nice: Non-linear independent components estimation
Laurent Dinh, David Krueger, and Yoshua Bengio · 2015
Earlier work this paper cites.
Lie groups, Lie algebras, and representations: an elementary introduction , volume 222
Brian Hall · 2015
Earlier work this paper cites.
Adam: a Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Lei Ba · 2015
Earlier work this paper cites.
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2015
Earlier work this paper cites.
Deep Residual Learning for Image Recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Density estimation using real nvp
Laurent Dinh, Jascha Sohl-Dickstein, and Samy Bengio · 2017
Cited alongside, same era.
Semi-supervised Classification with Graph Convolutional Networks
Thomas N. Kipf and Max Welling · 2017
Cited alongside, same era.
Grammar variational autoencoder
Matt J Kusner, Brooks Paige, and José Miguel Hernández-Lobato · 2017
Cited alongside, same era.
Modeling Relational Data with Graph Convolutional Networks
Michael Schlichtkrull, Thomas N. Kipf, Peter Bloem, Rianne van den Berg, Ivan Titov, and Max Welling · 2017
Cited alongside, same era.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Cited alongside, same era.
Graph convolutional policy network for goal-directed molecular graph generation
Jiaxuan You, Bowen Liu, Zhitao Ying, Vijay Pande, and Jure Leskovec · 2018
Later among the works it cites.
Optuna: A next-generation hyperparameter optimization framework
Takuya Akiba, Shotaro Sano, Toshihiko Yanase, Takeru Ohta, and Masanori Koyama · 2019
Closest in time.
Invertible residual networks
Jens Behrmann, Will Grathwohl, Ricky T. Q. Chen, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
Closest in time.
Residual flows for invertible generative modeling
Ricky T. Q. Chen, Jens Behrmann, David Duvenaud, and Jörn-Henrik Jacobsen · 2019
Closest in time.
Normalizing Flows: Introduction and Ideas
Ivan Kobyzev, Simon Prince, and Marcus A Brubaker · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rafael Gómez-Bombarelli, Jennifer N Wei, David Duvenaud, José Miguel Hernández-Lobato, Benjamín Sánchez-Lengeling, Dennis Sheberla, Jorge Aguilera-Iparraguirre, Timothy D Hirzel, Ryan P Adams, and Alán Aspuru-Guzik · 2018
Cited alongside, same era.
Junction tree variational autoencoder for molecular graph generation
Wengong Jin, Regina Barzilay, and Tommi Jaakkola · 2018
Cited alongside, same era.
Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
Cited alongside, same era.
Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander Gaunt · 2018
Cited alongside, same era.
Constrained generation of semantically valid graphs via regularizing variational autoencoders
Tengfei Ma, Jie Chen, and Cao Xiao · 2018
Cited alongside, same era.
Spectral normalization for generative adversarial networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama, and Yuichi Yoshida · 2018
Cited alongside, same era.
Jenny Liu, Aviral Kumar, Jimmy Ba, Jamle Kiros, and Kevin Swersky · 2019
Closest in time.
Graphnvp: An invertible flow model for generating molecular graphs
Kaushalya Madhawa, Katushiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2019
Closest in time.
On asymptotic behaviors of graph cnns from dynamical systems perspective, 2019
Kenta Oono and Taiji Suzuki · 2019
Closest in time.
Mintnet: Building invertible neural networks with masked convolutions
Yang Song, Chenlin Meng, and Stefano Ermon · 2019
Closest in time.
Simplifying Graph Convolutional Networks
Felix Wu, Tianyi Zhang, Amauri Jr. Holanda de Souza, Christopher Fifty, Tao Yu, and Kilian Q. Weinberger · 2019
Closest in time.