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Convolutional Networks on Graphs for Learning Molecular Fingerprints.

D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gómez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, and R. Adams. NIPS, page 2224-2232. (2015)

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Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming.A. Paszke, D. Johnson, D. Duvenaud, D. Vytiniotis, A. Radul, M. Johnson, J. Ragan-Kelley, and D. Maclaurin. Proc. ACM Program. Lang., 5 (ICFP): 1-29 (2021)Firefly Monte Carlo: Exact MCMC with Subsets of Data.D. Maclaurin, and R. Adams. UAI, page 543-552. AUAI Press, (2014)Parallelism-preserving automatic differentiation for second-order array languages.A. Paszke, M. Johnson, R. Frostig, and D. Maclaurin. FHPNC@ICFP, page 13-23. ACM, (2021)Automatically batching control-intensive programs for modern accelerators.A. Radul, B. Patton, D. Maclaurin, M. Hoffman, and R. Saurous. MLSys, mlsys.org, (2020)Early Stopping is Nonparametric Variational Inference.D. Maclaurin, D. Duvenaud, and R. Adams. CoRR, (2015)Gradient-based Hyperparameter Optimization through Reversible LearningD. Maclaurin, D. Duvenaud, and R. Adams. (2015)cite arxiv:1502.03492Comment: 10 figures. Submitted to ICML.Convolutional Networks on Graphs for Learning Molecular FingerprintsD. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. Adams. Advances in Neural Information Processing Systems 28, Curran Associates, Inc., (2015)Early Stopping as Nonparametric Variational Inference.D. Duvenaud, D. Maclaurin, and R. Adams. AISTATS, volume 51 of JMLR Workshop and Conference Proceedings, page 1070-1077. JMLR.org, (2016)Firefly Monte Carlo: Exact MCMC with Subsets of Data.D. Maclaurin, and R. Adams. IJCAI, page 4289-4295. AAAI Press, (2015)Decomposing reverse-mode automatic differentiation.R. Frostig, M. Johnson, D. Maclaurin, A. Paszke, and A. Radul. CoRR, (2021)

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Convolutional Networks on Graphs for Learning Molecular Fingerprints.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Convolutional Networks on Graphs for Learning Molecular Fingerprints.

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Convolutional Networks on Graphs for Learning Molecular Fingerprints.