Fetching the paper…
Reading the bibliography…
Graph neural networks are currently leading the performance charts in learning-based molecule property prediction and classification.
R. C. Bose and D. K. Ray-Chaudhuri, “On a class of error correcting binary group codes,” Information and control , vol. 3, no. 1, pp. 68–79, 1960
1960
Earlier work this paper cites.
R. Gallager, “Low-density parity-check codes,” IRE Transactions on information theory , vol. 8, no. 1, pp. 21–28, 1962
1962
Earlier work this paper cites.
C. Goller and A. Kuchler, “Learning task-dependent distributed representations by backpropagation through structure,” in Proceedings of International Conference on Neural Networks (ICNN’96) , vol. 1. IEEE, 1996, pp. 347–352
1996
Earlier work this paper cites.
J. Atwood and D. Towsley, “Diffusion-convolutional neural networks,” in Advances in Neural Information Processing Systems , 2016, pp. 1993–2001
2001
Earlier work this paper cites.
M. Gori, G. Monfardini, and F. Scarselli, “A new model for learning in graph domains,” in Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005. , vol. 2. IEEE, 2005, pp. 729–734
2005
Earlier work this paper cites.
F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, “The graph neural network model,” IEEE Transactions on Neural Networks , vol. 20, no. 1, pp. 61–80, 2008
2008
Earlier work this paper cites.
E. Arikan, “Channel polarization: A method for constructing capacity-achieving codes,” in 2008 IEEE International Symposium on Information Theory . IEEE, 2008, pp. 1173–1177
2008
Earlier work this paper cites.
D. Rogers and M. Hahn, “Extended-connectivity fingerprints,” Journal of chemical information and modeling , vol. 50, no. 5, pp. 742–754, 2010
2010
Earlier work this paper cites.
N. Shervashidze, P. Schweitzer, E. J. v. Leeuwen, K. Mehlhorn, and K. M. Borgwardt, “Weisfeiler-lehman graph kernels,” Journal of Machine Learning Research , vol. 12, no. Sep, pp. 2539–2561, 2011
2011
Earlier work this paper cites.
M. Rupp, A. Tkatchenko, K.-R. Müller, and O. A. Von Lilienfeld, “Fast and accurate modeling of molecular atomization energies with machine learning,” Physical review letters , vol. 108, no. 5, p. 058301, 2012
2012
Earlier work this paper cites.
G. Montavon, K. Hansen et al. , “Learning invariant representations of molecules for atomization energy prediction,” in Advances in Neural Information Processing Systems , 2012, pp. 440–448
2012
Earlier work this paper cites.
L. Ruddigkeit, R. Van Deursen, L. C. Blum, and J.-L. Reymond, “Enumeration of 166 billion organic small molecules in the chemical universe database gdb-17,” Journal of chemical information and modeling , vol. 52, no. 11, pp. 2864–2875, 2012
2012
Earlier work this paper cites.
R. Socher, A. Perelygin, J. Wu, J. Chuang, C. D. Manning, A. Ng, and C. Potts, “Recursive deep models for semantic compositionality over a sentiment treebank,” in Proceedings of the 2013 conference on empirical methods in natural language processing , 2013, pp. 1631–1642
2013
Earlier work this paper cites.
2013
Earlier work this paper cites.
J. E. Saal, S. Kirklin, M. Aykol, B. Meredig, and C. Wolverton, “Materials design and discovery with high-throughput density functional theory: the open quantum materials database (oqmd),” Jom , vol. 65, no. 11, pp. 1501–1509, 2013
2013
Earlier work this paper cites.
R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Von Lilienfeld, “Quantum chemistry structures and properties of 134 kilo molecules,” Scientific data , vol. 1, p. 140022, 2014
2014
Earlier work this paper cites.
D. K. Duvenaud, D. Maclaurin, J. Iparraguirre, R. Bombarell, T. Hirzel, A. Aspuru-Guzik, and R. P. Adams, “Convolutional networks on graphs for learning molecular fingerprints,” in Advances in neural information processing systems , 2015, pp. 2224–2232
2015
Earlier work this paper cites.
K. Hansen, F. Biegler et al. , “Machine learning predictions of molecular properties: Accurate many-body potentials and nonlocality in chemical space,” The journal of physical chemistry letters , vol. 6, no. 12, pp. 2326–2331, 2015
2015
Earlier work this paper cites.
B. Klein, L. Wolf, and Y. Afek, “A dynamic convolutional layer for short range weather prediction,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2015, pp. 4840–4848
2015
Earlier work this paper cites.
G. Riegler, S. Schulter, M. Rüther, and H. Bischof, “Conditioned regression models for non-blind single image super-resolution,” in 2015 IEEE International Conference on Computer Vision (ICCV) , Dec 2015, pp. 522–530
2015
Earlier work this paper cites.
S. Kirklin, J. E. Saal, B. Meredig, A. Thompson, J. W. Doak, M. Aykol, S. Rühl, and C. Wolverton, “The open quantum materials database (oqmd): assessing the accuracy of dft formation energies,” npj Computational Materials , vol. 1, p. 15010, 2015
2015
Earlier work this paper cites.
P. Yanardag and S. Vishwanathan, “Deep graph kernels,” in Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining . ACM, 2015, pp. 1365–1374
2015
Cited alongside, same era.
S. Kearnes, K. McCloskey, M. Berndl, V. Pande, and P. Riley, “Molecular graph convolutions: moving beyond fingerprints,” Journal of computer-aided molecular design , vol. 30, no. 8, pp. 595–608, 2016
2016
Cited alongside, same era.
Y. Li, D. Tarlow, M. Brockschmidt, and R. Zemel, “Gated graph sequence neural networks,” in ICLR , 2016
2016
Cited alongside, same era.
M. Defferrard, X. Bresson, and P. Vandergheynst, “Convolutional neural networks on graphs with fast localized spectral filtering,” in Advances in neural information processing systems , 2016, pp. 3844–3852
2016
Cited alongside, same era.
2018
Later among the works it cites.
2018
Later among the works it cites.
A. Brock, T. Lim, J. Ritchie, and N. Weston, “SMASH: One-shot model architecture search through hypernetworks,” in International Conference on Learning Representations , 2018. [Online]. Available: https://openreview.net/forum?id=rydeCEhs-
2018
Later among the works it cites.
H. Kim, Y. Jiang, S. Kannan, S. Oh, and P. Viswanath, “Deepcode: Feedback codes via deep learning,” in Advances in Neural Information Processing Systems (NIPS) , 2018, pp. 9436–9446
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2016
Cited alongside, same era.
P. Battaglia, R. Pascanu, M. Lai, D. J. Rezende et al. , “Interaction networks for learning about objects, relations and physics,” in Advances in neural information processing systems , 2016, pp. 4502–4510
2016
Cited alongside, same era.
B. Huang and O. A. Von Lilienfeld, “Communication: Understanding molecular representations in machine learning: The role of uniqueness and target similarity,” 2016
2016
Cited alongside, same era.
X. Jia, B. De Brabandere, T. Tuytelaars, and L. V. Gool, “Dynamic filter networks,” in Advances in Neural Information Processing Systems , 2016, pp. 667–675
2016
Cited alongside, same era.
D. Ha, A. Dai, and Q. V. Le, “Hypernetworks,” arXiv preprint arXiv:1609.09106 , 2016
2016
Cited alongside, same era.
L. Bertinetto, J. F. Henriques, J. Valmadre, P. Torr, and A. Vedaldi, “Learning feed-forward one-shot learners,” in Advances in Neural Information Processing Systems , 2016, pp. 523–531
2016
Cited alongside, same era.
E. Nachmani, Y. Be’ery, and D. Burshtein, “Learning to decode linear codes using deep learning,” in 2016 54th Annual Allerton Conference on Communication, Control, and Computing (Allerton) . IEEE, 2016, pp. 341–346
2016
Cited alongside, same era.
K. Schütt, P.-J. Kindermans, H. E. S. Felix, S. Chmiela, A. Tkatchenko, and K.-R. Müller, “SchNet: A continuous-filter convolutional neural network for modeling quantum interactions,” in Advances in Neural Information Processing Systems , 2017, pp. 991–1001
2017
Cited alongside, same era.
C.-F. Teng, C.-C. Liao, C.-H. Chen, and A.-Y. A. Wu, “Polar feature based deep architectures for automatic modulation classification considering channel fading,” in 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP) . IEEE, 2018, pp. 554–558
2018
Later among the works it cites.
B. Vasić, X. Xiao, and S. Lin, “Learning to decode ldpc codes with finite-alphabet message passing,” in 2018 Information Theory and Applications Workshop (ITA) . IEEE, 2018, pp. 1–9
2018
Later among the works it cites.
Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse, A. S. Pappu, K. Leswing, and V. Pande, “Moleculenet: a benchmark for molecular machine learning,” Chemical science , vol. 9, no. 2, pp. 513–530, 2018
2018
Later among the works it cites.
M. Zhang, Z. Cui, M. Neumann, and Y. Chen, “An end-to-end deep learning architecture for graph classification,” in Thirty-Second AAAI Conference on Artificial Intelligence , 2018
2018
Later among the works it cites.
S. Ivanov and E. Burnaev, “Anonymous walk embeddings,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, J. Dy and A. Krause, Eds., vol. 80. Stockholmsmässan, Stockholm Sweden: PMLR, 10–15 Jul 2018, pp. 2186–2195. [Online]. Available: http://proceedings.mlr.press/v80/ivanov18a.html
2018
Later among the works it cites.
2019
Later among the works it cites.
K. Xu, W. Hu, J. Leskovec, and S. Jegelka, “How powerful are graph neural networks?” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=ryGs6iA5Km
2019
Later among the works it cites.
E. Nachmani and L. Wolf, “Hyper-graph-network decoders for block codes,” in Advances in Neural Information Processing Systems (NeurIPS) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Zhang, M. Ren, and R. Urtasun, “Graph hypernetworks for neural architecture search,” in International Conference on Learning Representations , 2019. [Online]. Available: https://openreview.net/forum?id=rkgW0oA9FX
2019
Later among the works it cites.
H. Maron, E. Fetaya, N. Segol, and Y. Lipman, “On the universality of invariant networks,” in International Conference on Machine Learning , 2019, pp. 4363–4371
2019
Later among the works it cites.
M. Helmling, S. Scholl, F. Gensheimer, T. Dietz, K. Kraft, S. Ruzika, and N. Wehn, “Database of Channel Codes and ML Simulation Results,” www.uni-kl.de/channel-codes , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
2019
Later among the works it cites.
C. Morris, M. Ritzert, M. Fey, W. L. Hamilton, J. E. Lenssen, G. Rattan, and M. Grohe, “Weisfeiler and leman go neural: Higher-order graph neural networks,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, 2019, pp. 4602–4609
2019
Later among the works it cites.
M. Niepert, M. Ahmed, and K. Kutzkov, “Learning convolutional neural networks for graphs,” in International conference on machine learning , 2016, pp. 2014–2023
2023
Closest in time.