Partition and Code: learning how to compress graphs

5 July 2021

Papers citing "Partition and Code: learning how to compress graphs"

50 / 66 papers shown

Title
Autoregressive Diffusion Models Emiel Hoogeboom Alexey A. Gritsenko Jasmijn Bastings Ben Poole Rianne van den Berg Tim Salimans DiffM 83 153 0 05 Oct 2021
Compressing Multisets with Large Alphabets using Bits-Back Coding Daniel de Souza Severo James Townsend Ashish Khisti Alireza Makhzani Karen Ullrich 54 8 0 15 Jul 2021
Variational Diffusion Models Diederik P. Kingma Tim Salimans Ben Poole Jonathan Ho DiffM 178 1,121 0 01 Jul 2021
Improving Lossless Compression Rates via Monte Carlo Bits-Back Coding Yangjun Ruan Karen Ullrich Daniel de Souza Severo James Townsend Ashish Khisti Arnaud Doucet Alireza Makhzani Chris J. Maddison 62 25 0 22 Feb 2021
Online Graph Dictionary Learning Cédric Vincent-Cuaz Titouan Vayer Rémi Flamary Marco Corneli Nicolas Courty 51 46 0 12 Feb 2021
GraphDF: A Discrete Flow Model for Molecular Graph Generation Youzhi Luo Keqiang Yan Shuiwang Ji DRL 230 199 0 01 Feb 2021
GraphEBM: Molecular Graph Generation with Energy-Based Models Meng Liu Keqiang Yan Bora Oztekin Shuiwang Ji 70 88 0 31 Jan 2021
Reducing the Computational Cost of Deep Generative Models with Binary Neural Networks Thomas Bird F. Kingma David Barber SyDa MQ AI4CE 108 9 0 26 Oct 2020
Directional Graph Networks Dominique Beaini Saro Passaro Vincent Létourneau William L. Hamilton Gabriele Corso Pietro Lio 88 192 0 06 Oct 2020
TUDataset: A collection of benchmark datasets for learning with graphs Christopher Morris Nils M. Kriege Franka Bause Kristian Kersting Petra Mutzel Marion Neumann 236 821 0 16 Jul 2020
Scalable Deep Generative Modeling for Sparse Graphs H. Dai Azade Nazi Yujia Li Bo Dai Dale Schuurmans BDL 61 78 0 28 Jun 2020
Building powerful and equivariant graph neural networks with structural message-passing Clément Vignac Andreas Loukas P. Frossard 61 121 0 26 Jun 2020
Object-Centric Learning with Slot Attention Francesco Locatello Dirk Weissenborn Thomas Unterthiner Aravindh Mahendran G. Heigold Jakob Uszkoreit Alexey Dosovitskiy Thomas Kipf OCL 222 849 0 26 Jun 2020
IDF++: Analyzing and Improving Integer Discrete Flows for Lossless Compression Rianne van den Berg A. Gritsenko Mostafa Dehghani C. Sønderby Tim Salimans 66 60 0 22 Jun 2020
Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting Giorgos Bouritsas Fabrizio Frasca Stefanos Zafeiriou M. Bronstein 127 432 0 16 Jun 2020
Learning Better Lossless Compression Using Lossy Compression Fabian Mentzer Luc Van Gool Michael Tschannen 120 71 0 23 Mar 2020
COPT: Coordinated Optimal Transport for Graph Sketching Yihe Dong W. Sawin OT 77 26 0 09 Mar 2020
Benchmarking Graph Neural Networks Vijay Prakash Dwivedi Chaitanya K. Joshi Anh Tuan Luu T. Laurent Yoshua Bengio Xavier Bresson 417 940 0 02 Mar 2020
Permutation Invariant Graph Generation via Score-Based Generative Modeling Chenhao Niu Yang Song Jiaming Song Shengjia Zhao Aditya Grover Stefano Ermon DiffM 69 271 0 02 Mar 2020
Can Graph Neural Networks Count Substructures? Zhengdao Chen Lei Chen Soledad Villar Joan Bruna GNN 112 325 0 10 Feb 2020
GraphAF: a Flow-based Autoregressive Model for Molecular Graph Generation Chence Shi Minkai Xu Zhaocheng Zhu Weinan Zhang Ming Zhang Jian Tang 168 438 0 26 Jan 2020
HiLLoC: Lossless Image Compression with Hierarchical Latent Variable Models James Townsend Thomas Bird Julius Kunze David Barber BDL VLM 123 56 0 20 Dec 2019
Efficient Graph Generation with Graph Recurrent Attention Networks Renjie Liao Yujia Li Yang Song Shenlong Wang C. Nash William L. Hamilton David Duvenaud R. Urtasun R. Zemel GNN 131 335 0 02 Oct 2019
What graph neural networks cannot learn: depth vs width Andreas Loukas GNN 84 299 0 06 Jul 2019
End to end learning and optimization on graphs Bryan Wilder Eric Ewing B. Dilkina Milind Tambe GNN 61 107 0 31 May 2019
Solving graph compression via optimal transport Vikas Garg Tommi Jaakkola OT 45 16 0 29 May 2019
Discrete Flows: Invertible Generative Models of Discrete Data Dustin Tran Keyon Vafa Kumar Krishna Agrawal Laurent Dinh Ben Poole DRL 142 116 0 24 May 2019
Compression with Flows via Local Bits-Back Coding Jonathan Ho Evan Lohn Pieter Abbeel 74 53 0 21 May 2019
Integer Discrete Flows and Lossless Compression Emiel Hoogeboom Jorn W. T. Peters Rianne van den Berg Max Welling 108 159 0 17 May 2019
Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables F. Kingma Pieter Abbeel Jonathan Ho 54 97 0 16 May 2019
Fast Graph Representation Learning with PyTorch Geometric Matthias Fey J. E. Lenssen 3DH GNN 3DPC 226 4,341 0 06 Mar 2019
GAP: Generalizable Approximate Graph Partitioning Framework Azade Nazi W. Hang Anna Goldie Sujith Ravi Azalia Mirhoseini 63 61 0 02 Mar 2019
A Review of Stochastic Block Models and Extensions for Graph Clustering Clement Lee D. Wilkinson 73 201 0 01 Mar 2019
Practical Lossless Compression with Latent Variables using Bits Back Coding James Townsend Thomas Bird David Barber DRL 66 142 0 15 Jan 2019
Practical Full Resolution Learned Lossless Image Compression Fabian Mentzer E. Agustsson Michael Tschannen Radu Timofte Luc Van Gool 61 197 0 30 Nov 2018
DeepZip: Lossless Data Compression using Recurrent Neural Networks Mohit Goyal Kedar Tatwawadi Shubham Chandak Idoia Ochoa AI4CE 43 79 0 20 Nov 2018
A Convergence Theory for Deep Learning via Over-Parameterization Zeyuan Allen-Zhu Yuanzhi Li Zhao Song AI4CE ODL 253 1,463 0 09 Nov 2018
Weisfeiler and Leman Go Neural: Higher-order Graph Neural Networks Christopher Morris Martin Ritzert Matthias Fey William L. Hamilton J. E. Lenssen Gaurav Rattan Martin Grohe GNN 192 1,636 0 04 Oct 2018
Gradient Descent Provably Optimizes Over-parameterized Neural Networks S. Du Xiyu Zhai Barnabás Póczós Aarti Singh MLT ODL 219 1,272 0 04 Oct 2018
How Powerful are Graph Neural Networks? Keyulu Xu Weihua Hu J. Leskovec Stefanie Jegelka GNN 240 7,653 0 01 Oct 2018
Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters Marton Havasi Robert Peharz José Miguel Hernández-Lobato 63 82 0 30 Sep 2018
MolGAN: An implicit generative model for small molecular graphs Nicola De Cao Thomas Kipf GNN GAN 177 926 0 30 May 2018
Constrained Graph Variational Autoencoders for Molecule Design Qi Liu Miltiadis Allamanis Marc Brockschmidt Alexander L. Gaunt BDL 75 456 0 23 May 2018
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks Jonathan Frankle Michael Carbin 233 3,473 0 09 Mar 2018
Learning Deep Generative Models of Graphs Yujia Li Oriol Vinyals Chris Dyer Razvan Pascanu Peter W. Battaglia GNN AI4CE 194 662 0 08 Mar 2018
NetGAN: Generating Graphs via Random Walks Aleksandar Bojchevski Oleksandr Shchur Daniel Zügner Stephan Günnemann GAN GNN 177 361 0 02 Mar 2018
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models Jiaxuan You Rex Ying Xiang Ren William L. Hamilton J. Leskovec GNN BDL 116 849 0 24 Feb 2018
Spectrally approximating large graphs with smaller graphs Andreas Loukas P. Vandergheynst 58 105 0 21 Feb 2018
On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization Sanjeev Arora Nadav Cohen Elad Hazan 99 485 0 19 Feb 2018
Junction Tree Variational Autoencoder for Molecular Graph Generation Wengong Jin Regina Barzilay Tommi Jaakkola 352 1,368 0 12 Feb 2018