Fetching the paper…
Reading the bibliography…
This work addresses one-shot set and graph generation, and, more specifically, the parametrization of probabilistic decoders that map a vector-shaped prior to a distribution over sets or graphs.
Approximation by superpositions of a sigmoidal function
George Cybenko · 1989
Earlier work this paper cites.
On the use of gromov-hausdorff distances for shape comparison
Facundo Mémoli · 2007
Earlier work this paper cites.
Group theoretical methods in machine learning
Imre Risi Kondor · 2008
Earlier work this paper cites.
Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17
Lars Ruddigkeit, Ruud Van Deursen, Lorenz C Blum, and Jean-Louis Reymond · 2012
Earlier work this paper cites.
Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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.
Dipol-gan: Generating molecular graphs adversarially with relational differentiable pooling
Michael Guarino, Alexander Shah, and Pablo Rivas · 2017
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Deep sets
Manzil Zaheer, Satwik Kottur, Siamak Ravanbakhsh, Barnabas Poczos, Russ R Salakhutdinov, and Alexander J Smola · 2017
Earlier work this paper cites.
Learning representations and generative models for 3d point clouds
Panos Achlioptas, Olga Diamanti, Ioannis Mitliagkas, and Leonidas Guibas · 2018
Earlier work this paper cites.
Relational inductive biases, deep learning, and graph networks
Peter Battaglia, Jessica Blake Chandler Hamrick, Victor Bapst, Alvaro Sanchez, Vinicius Zambaldi, Mateusz Malinowski, Andrea Tacchetti, David Raposo, Adam Santoro, Ryan Faulkner, Caglar Gulcehre, Francis Song, Andy Ballard, Justin Gilmer, George E. Dahl, Ashish Vaswani, Kelsey Allen, Charles Nash, Victoria Jayne Langston, Chris Dyer, Nicolas Heess, Daan Wierstra, Pushmeet Kohli, Matt Botvinick, Oriol Vinyals, Yujia Li, and Razvan Pascanu · 2018
Earlier work this paper cites.
Molgan: An implicit generative model for small molecular graphs
Nicola De Cao and Thomas Kipf · 2018
Earlier work this paper cites.
Multi-objective de novo drug design with conditional graph generative model
Yibo Li, Liangren Zhang, and Zhenming Liu · 2018
Earlier work this paper cites.
Constrained graph variational autoencoders for molecule design
Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, and Alexander L. Gaunt · 2018
Earlier work this paper cites.
Film: Visual reasoning with a general conditioning layer
Ethan Perez, Florian Strub, Harm De Vries, Vincent Dumoulin, and Aaron Courville · 2018
Cited alongside, same era.
Graphvae: Towards generation of small graphs using variational autoencoders
Martin Simonovsky and Nikos Komodakis · 2018
Cited alongside, same era.
A two-step graph convolutional decoder for molecule generation
Xavier Bresson and Thomas Laurent · 2019
Cited alongside, same era.
Interpolating between optimal transport and mmd using sinkhorn divergences
Jean Feydy, Thibault Séjourné, François-Xavier Vialard, Shun-ichi Amari, Alain Trouve, and Gabriel Peyré · 2019
Cited alongside, same era.
Graph u-nets
Hongyang Gao and Shuiwang Ji · 2019
Cited alongside, same era.
Efficient learning of non-autoregressive graph variational autoencoders for molecular graph generation
Graph{nvp}: an invertible flow-based model for generating molecular graphs, 2020
Kaushalya Madhawa, Katsuhiko Ishiguro, Kosuke Nakago, and Motoki Abe · 2020
Later among the works it cites.
Polygen: An autoregressive generative model of 3d meshes
Charlie Nash, Yaroslav Ganin, SM Ali Eslami, and Peter Battaglia · 2020
Later among the works it cites.
Set2graph: Learning graphs from sets
Hadar Serviansky, Nimrod Segol, Jonathan Shlomi, Kyle Cranmer, Eilam Gross, Haggai Maron, and Yaron Lipman · 2020
Later among the works it cites.
Generative adversarial set transformers
Karl Stelzner, Kristian Kersting, and Adam R Kosiorek · 2020
Later among the works it cites.
Pointgrow: Autoregressively learned point cloud generation with self-attention
Yongbin Sun, Yue Wang, Ziwei Liu, Joshua Siegel, and Sanjay Sarma · 2020
Later among the works it cites.
Fspool: Learning set representations with featurewise sort pooling
Yan Zhang, Jonathon Hare, and Adam Prügel-Bennett · 2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Youngchun Kwon, Jiho Yoo, Youn-Suk Choi, Won-Joon Son, Dongseon Lee, and Seokho Kang · 2019
Cited alongside, same era.
Set transformer: A framework for attention-based permutation-invariant neural networks
Juho Lee, Yoonho Lee, Jungtaek Kim, Adam Kosiorek, Seungjin Choi, and Yee Whye Teh · 2019
Cited alongside, same era.
Efficient graph generation with graph recurrent attention networks
Renjie Liao, Yujia Li, Yang Song, Shenlong Wang, Charlie Nash, William L. Hamilton, David Duvenaud, Raquel Urtasun, and Richard Zemel · 2019
Cited alongside, same era.
Weisfeiler and leman go neural: Higher-order graph neural networks
Christopher Morris, Martin Ritzert, Matthias Fey, William L Hamilton, Jan Eric Lenssen, Gaurav Rattan, and Martin Grohe · 2019
Cited alongside, same era.
Deep set prediction networks
Yan Zhang, Jonathon Hare, and Adam Prugel-Bennett · 2019
Cited alongside, same era.
Principal neighbourhood aggregation for graph nets
Gabriele Corso, Luca Cavalleri, Dominique Beaini, Pietro Liò, and Petar Veličković · 2020
Cited alongside, same era.
Equivariant flows: exact likelihood generative learning for symmetric densities
Jonas Köhler, Leon Klein, and Frank Noé · 2020
Cited alongside, same era.
Later among the works it cites.
Equivariant normalizing flows for point processes and sets, 2021
Marin Biloš and Stephan Günnemann · 2021
Closest in time.
Geometric deep learning: Grids, groups, graphs, geodesics, and gauges
Michael M Bronstein, Joan Bruna, Taco Cohen, and Petar Veličković · 2021
Closest in time.
Setvae: Learning hierarchical composition for generative modeling of set-structured data
Jinwoo Kim, Jaehoon Yoo, Juho Lee, and Seunghoon Hong · 2021
Closest in time.
Gg-gan: A geometric graph generative adversarial network, 2021
Igor Krawczuk, Pedro Abranches, Andreas Loukas, and Volkan Cevher · 2021
Closest in time.
GraphEBM: Molecular graph generation with energy-based models
Meng Liu, Keqiang Yan, Bora Oztekin, and Shuiwang Ji · 2021
Closest in time.
Generating stable molecules using imitation and reinforcement learning
Søren Ager Meldgaard, Jonas Köhler, Henrik Lund Mortensen, Mads-Peter Verner Christiansen, Frank Noé, and Bjork Hammer · 2021
Closest in time.
A graph vae and graph transformer approach to generating molecular graphs
Joshua Mitton, Hans M Senn, Klaas Wynne, and Roderick Murray-Smith · 2021
Closest in time.
E (n) equivariant normalizing flows for molecule generation in 3d
Victor Garcia Satorras, Emiel Hoogeboom, Fabian B Fuchs, Ingmar Posner, and Max Welling · 2021
Closest in time.