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Discrete-state denoising diffusion models led to state-of-the-art performance in graph generation, especially in the molecular domain.
Approximation by superpositions of a sigmoidal function
G. Cybenko · 1992
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Multilayer feedforward networks with a nonpolynomial activation function can approximate any function
M. Leshno, V. Y. Lin, A. Pinkus, and S. Schocken · 1993
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Deep unsupervised learning using nonequilibrium thermodynamics
J. Sohl-Dickstein, E. Weiss, N. Maheswaranathan, and S. Ganguli · 2015
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On the combinatorial power of the Weisfeiler-Lehman algorithm
M. Fürer · 2017
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
K. Preuer, P. Renz, T. Unterthiner, S. Hochreiter, and G. Klambauer · 2018
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Moleculenet: a benchmark for molecular machine learning
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande · 2018
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GraphRNN: Generating realistic graphs with deep auto-regressive models
J. You, R. Ying, X. Ren, W. Hamilton, and J. Leskovec · 2018
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Guacamol: benchmarking models for de novo molecular design
N. Brown, M. Fiscato, M. H. Segler, and A. C. Vaucher · 2019
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Efficient graph generation with graph recurrent attention networks
R. Liao, Y. Li, Y. Song, S. Wang, W. Hamilton, D. K. Duvenaud, R. Urtasun, and R. Zemel · 2019
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GraphAF: a flow-based autoregressive model for molecular graph generation
C. Shi, M. Xu, Z. Zhu, W. Zhang, M. Zhang, and J. Tang · 2019
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Denoising diffusion probabilistic models
J. Ho, A. Jain, and P. Abbeel · 2020
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Permutation invariant graph generation via score-based generative modeling
C. Niu, Y. Song, J. Song, S. Zhao, A. Grover, and S. Ermon · 2020
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Molecular sets (moses): a benchmarking platform for molecular generation models
D. Polykovskiy, A. Zhebrak, B. Sanchez-Lengeling, S. Golovanov, O. Tatanov, S. Belyaev, R. Kurbanov, A. Artamonov, V. Aladinskiy, M. Veselov, et al · 2020
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Psi4 1.4: Open-source software for high-throughput quantum chemistry
D. G. Smith, L. A. Burns, A. C. Simmonett, R. M. Parrish, M. C. Schieber, R. Galvelis, P. Kraus, H. Kruse, R. Di Remigio, A. Alenaizan, et al · 2020
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Score-based generative modeling through stochastic differential equations
Y. Song, J. Sohl-Dickstein, D. P. Kingma, A. Kumar, S. Ermon, and B. Poole · 2020
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Structured denoising diffusion models in discrete state-spaces
J. Austin, D. D. Johnson, J. Ho, D. Tarlow, and R. Van Den Berg · 2021
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Introduction to Continuous-Time Stochastic Processes
V. Capasso and D. Bakstein · 2021
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Graph neural networks with learnable structural and positional representations
V. P. Dwivedi, A. T. Luu, T. Laurent, Y. Bengio, and X. Bresson · 2021
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Classifier-free diffusion guidance
J. Ho and T. Salimans · 2021
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Rethinking graph transformers with spectral attention
D. Kreuzer, D. Beaini, W. Hamilton, V. Létourneau, and P. Tossou · 2021
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Improved denoising diffusion probabilistic models
A. Q. Nichol and P. Dhariwal · 2021
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A continuous time framework for discrete denoising models
A. Campbell, J. Benton, V. De Bortoli, T. Rainforth, G. Deligiannidis, and A. Doucet · 2022
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Diffusion models for graphs benefit from discrete state spaces
K. K. Haefeli, K. Martinkus, N. Perraudin, and R. Wattenhofer · 2022
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Autoregressive diffusion model for graph generation
L. Kong, J. Cui, H. Sun, Y. Zhuang, B. A. Prakash, and C. Zhang · 2023
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Navigating the design space of equivariant diffusion-based generative models for de novo 3d molecule generation
T. Le, J. Cremer, F. Noe, D.-A. Clevert, and K. T. Schütt · 2023
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Sign and basis invariant networks for spectral graph representation learning
D. Lim, J. D. Robinson, L. Zhao, T. E. Smidt, S. Sra, H. Maron, and S. Jegelka · 2023
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Discrete diffusion modeling by estimating the ratios of the data distribution
A. Lou, C. Meng, and S. Ermon · 2023
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Fast graph generation via spectral diffusion
T. Luo, Z. Mo, and S. J. Pan · 2023
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Graph inductive biases in transformers without message passing
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Score-based generative modeling of graphs via the system of stochastic differential equations
J. Jo, S. Lee, and S. J. Hwang · 2022
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Mgcvae: multi-objective inverse design via molecular graph conditional variational autoencoder
M. Lee and K. Min · 2022
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Spectre: Spectral conditioning helps to overcome the expressivity limits of one-shot graph generators
K. Martinkus, A. Loukas, N. Perraudin, and R. Wattenhofer · 2022
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Score-based continuous-time discrete diffusion models
H. Sun, L. Yu, B. Dai, D. Schuurmans, and H. Dai · 2022
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Improved vector quantized diffusion models
Z. Tang, S. Gu, J. Bao, D. Chen, and F. Wen · 2022
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DiGress: Discrete denoising diffusion for graph generation
C. Vignac, I. Krawczuk, A. Siraudin, B. Wang, V. Cevher, and P. Frossard · 2022
Cited alongside, same era.
L. Ma, C. Lin, D. Lim, A. Romero-Soriano, P. K. Dokania, M. Coates, P. Torr, and S.-N. Lim · 2023
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Classifier-free graph diffusion for molecular property targeting
M. Ninniri, M. Podda, and D. Bacciu · 2023
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Sparse training of discrete diffusion models for graph generation
Y. Qin, C. Vignac, and P. Frossard · 2023
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Weisfeiler–leman and graph spectra
G. Rattan and T. Seppelt · 2023
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Housediffusion: Vector floorplan generation via a diffusion model with discrete and continuous denoising
M. A. Shabani, S. Hosseini, and Y. Furukawa · 2023
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Midi: Mixed graph and 3d denoising diffusion for molecule generation
C. Vignac, N. Osman, L. Toni, and P. Frossard · 2023
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Diffusion models: A comprehensive survey of methods and applications
L. Yang, Z. Zhang, Y. Song, S. Hong, R. Xu, Y. Zhao, W. Zhang, B. Cui, and M.-H. Yang · 2023
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xarxiv preprint arxiv:2305.17589generation
J. Yim, B. L. Trippe, V. De Bortoli, E. Mathieu, A. Doucet, R. Barzilay, and T. Jaakkola · 2023
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Mudiff: Unified diffusion for complete molecule generation
C. Hua, S. Luan, M. Xu, Z. Ying, J. Fu, S. Ermon, and D. Precup · 2024
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Graph generation with diffusion mixture
J. Jo, D. Kim, and S. J. Hwang · 2024
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Pard: Permutation-invariant autoregressive diffusion for graph generation
L. Zhao, X. Ding, and L. Akoglu · 2024
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