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Generation of graphs is a major challenge for real-world tasks that require understanding the complex nature of their non-Euclidean structures.
Reverse-time diffusion equation models
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The properties of known drugs. 1. molecular frameworks
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Distinguishing enzyme structures from non-enzymes without alignments
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Stochastic Differential Equations
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Fast neighborhood subgraph pairwise distance kernel
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Zinc: a free tool to discover chemistry for biology
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Properties of the ornstein-uhlenbeck bridge
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Quantum chemistry structures and properties of 134 kilo molecules
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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Molgan: An implicit generative model for small molecular graphs
De Cao, N. and Kipf, T · 2018
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Junction tree variational autoencoder for molecular graph generation
Jin, W., Barzilay, R., and Jaakkola, T. S · 2018
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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., de Vries, H., Dumoulin, V., and Courville, A. C · 2018
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Fréchet chemnet distance: a metric for generative models for molecules in drug discovery
Preuer, K., Renz, P., Unterthiner, T., Hochreiter, S., and Klambauer, G · 2018
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Graphvae: Towards generation of small graphs using variational autoencoders
Simonovsky, M. and Komodakis, N · 2018
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Graphrnn: Generating realistic graphs with deep auto-regressive models
You, J., Ying, R., Ren, X., Hamilton, W., and Leskovec, J · 2018
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Generative code modeling with graphs
Brockschmidt, M., Allamanis, M., Gaunt, A. L., and Polozov, O · 2019
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Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules
Gebauer, N. W. A., Gastegger, M., and Schütt, K · 2019
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Generative models for graph-based protein design
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Efficient graph generation with graph recurrent attention networks
Liao, R., Li, Y., Song, Y., Wang, S., Hamilton, W., Duvenaud, D. K., Urtasun, R., and Zemel, R · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S · 2019
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E(n) equivariant normalizing flows
Satorras, V. G., Hoogeboom, E., Fuchs, F., Posner, I., and Welling, M · 2021
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2021
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Geom, energy-annotated molecular conformations for property prediction and molecular generation
Axelrod, S. and Gomez-Bombarelli, R · 2022
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A continuous time framework for discrete denoising models
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G., and Doucet, A · 2022
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Likelihood training of schrödinger bridge using forward-backward sdes theory
Chen, T., Liu, G., and Theodorou, E. A · 2022
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A generalization of transformer networks to graphs
Dwivedi, V. P. and Bresson, X · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Permutation invariant graph generation via score-based generative modeling
Niu, C., Song, Y., Song, J., Zhao, S., Grover, A., and Ermon, S · 2020
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Graphaf: a flow-based autoregressive model for molecular graph generation
Shi, C., Xu, M., Zhu, Z., Zhang, W., Zhang, M., and Tang, J · 2020
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Improved techniques for training score-based generative models
Song, Y. and Ermon, S · 2020
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Moflow: an invertible flow model for generating molecular graphs
Zang, C. and Wang, F · 2020
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Ho, J., Salimans, T., Gritsenko, A. A., Chan, W., Norouzi, M., and Fleet, D. J · 2022
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Equivariant diffusion for molecule generation in 3d
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Score-based generative modeling of graphs via the system of stochastic differential equations
Jo, J., Lee, S., and Hwang, S. J · 2022
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Let us build bridges: Understanding and extending diffusion generative models
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SPECTRE: spectral conditioning helps to overcome the expressivity limits of one-shot graph generators
Martinkus, K., Loukas, A., Perraudin, N., and Wattenhofer, R · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, S. K. S., Lopes, R. G., Ayan, B. K., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
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Diffusion-based molecule generation with informative prior bridges
Wu, L., Gong, C., Liu, X., Ye, M., and Liu, Q · 2022
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First hitting diffusion models
Ye, M., Wu, L., and Liu, Q · 2022
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Stochastic interpolants: A unifying framework for flows and diffusions
Albergo, M. S., Boffi, N. M., and Vanden-Eijnden, E · 2023
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Generative modeling on manifolds through mixture of riemannian diffusion processes
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