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We consider the problem of representation learning for graph data.
Distinguishing enzyme structures from non-enzymes without alignments
Dobson, P. D. and Doig, A. J · 2003
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Protein function prediction via graph kernels
Borgwardt, K. M., Ong, C. S., Schönauer, S., Vishwanathan, S., Smola, A. J., and Kriegel, H.-P · 2005
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A new model for learning in graph domains
Gori, M., Monfardini, G., and Scarselli, F · 2005
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Collective classification in network data
Sen, P., Namata, G., Bilgic, M., Getoor, L., Galligher, B., and Eliassi-Rad, T · 2008
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The graph neural network model
Scarselli, F., Gori, M., Tsoi, A. C., Hagenbuchner, M., and Monfardini, G · 2009
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Efficient backprop
LeCun, Y., Bottou, L., Orr, G. B., and Müller, K.-R · 2012
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Spectral networks and locally connected networks on graphs
Bruna, J., Zaremba, W., Szlam, A., and Lecun, Y · 2014
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Deep convolutional ranking for multilabel image annotation
Gong, Y., Jia, Y., Leung, T., Toshev, A., and Ioffe, S · 2014
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A convolutional neural network for modelling sentences
Kalchbrenner, N., Grefenstette, E., and Blunsom, P · 2014
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Deepwalk: Online learning of social representations
Perozzi, B., Al-Rfou, R., and Skiena, S · 2014
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Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., and Salakhutdinov, R · 2014
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Neural machine translation by jointly learning to align and translate
Bahdanau, D., Cho, K., and Bengio, Y · 2015
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Convolutional networks on graphs for learning molecular fingerprints
Duvenaud, D. K., Maclaurin, D., Iparraguirre, J., Bombarell, R., Hirzel, T., Aspuru-Guzik, A., and Adams, R. P · 2015
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Deep convolutional networks on graph-structured data
Henaff, M., Bruna, J., and LeCun, Y · 2015
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Fully convolutional networks for semantic segmentation
Long, J., Shelhamer, E., and Darrell, T · 2015
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Learning deconvolution network for semantic segmentation
Noh, H., Hong, S., and Han, B · 2015
Cited alongside, same era.
U-net: Convolutional networks for biomedical image segmentation
Ronneberger, O., Fischer, P., and Brox, T · 2015
Cited alongside, same era.
A structural smoothing framework for robust graph comparison
Yanardag, P. and Vishwanathan, S · 2015
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Subsampling for graph power spectrum estimation
Chepuri, S. P. and Leus, G · 2016
Cited alongside, same era.
3D U-Net: learning dense volumetric segmentation from sparse annotation
Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., and Ronneberger, O · 2016
Cited alongside, same era.
Discriminative embeddings of latent variable models for structured data
Dai, H., Dai, B., and Song, L · 2016
Cited alongside, same era.
Mask r-cnn
He, K., Gkioxari, G., Dollár, P., and Girshick, R · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Weinberger, K. Q., and van der Maaten, L · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
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The one hundred layers tiramisu: Fully convolutional densenets for semantic segmentation
Jégou, S., Drozdzal, M., Vazquez, D., Romero, A., and Bengio, Y · 2017
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Semi-supervised classification with graph convolutional networks
Kipf, T. N. and Welling, M · 2017
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Geometric deep learning on graphs and manifolds using mixture model cnns
Monti, F., Boscaini, D., Masci, J., Rodola, E., Svoboda, J., and Bronstein, M. M · 2017
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Convolutional neural networks on graphs with fast localized spectral filtering
Defferrard, M., Bresson, X., and Vandergheynst, P · 2016
Cited alongside, same era.
Learning convolutional neural networks for graphs
Niepert, M., Ahmed, M., and Kutzkov, K · 2016
Cited alongside, same era.
Revisiting semi-supervised learning with graph embeddings
Yang, Z., Cohen, W., and Salakhudinov, R · 2016
Cited alongside, same era.
Multi-scale context aggregation by dilated convolutions
Yu, F. and Koltun, V · 2016
Cited alongside, same era.
Segnet: A deep convolutional encoder-decoder architecture for image segmentation
Badrinarayanan, V., Kendall, A., and Cipolla, R · 2017
Cited alongside, same era.
Geometric deep learning: going beyond euclidean data
Bronstein, M. M., Bruna, J., LeCun, Y., Szlam, A., and Vandergheynst, P · 2017
Cited alongside, same era.
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Dynamic edge-conditioned filters in convolutional neural networks on graphs
Simonovsky, M. and Komodakis, N · 2017
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Graph attention networks
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Liò, P., and Bengio, Y · 2017
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Predicting multicellular function through multi-layer tissue networks
Zitnik, M. and Leskovec, J · 2017
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SplineCNN: Fast geometric deep learning with continuous B-spline kernels
Fey, M., Eric Lenssen, J., Weichert, F., and Müller, H · 2018
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Large-scale learnable graph convolutional networks
Gao, H., Wang, Z., and Ji, S · 2018
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Modeling relational data with graph convolutional networks
Schlichtkrull, M., Kipf, T. N., Bloem, P., van den Berg, R., Titov, I., and Welling, M · 2018
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Hierarchical graph representation learning with differentiable pooling
Ying, Z., You, J., Morris, C., Ren, X., Hamilton, W., and Leskovec, J · 2018
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An end-to-end deep learning architecture for graph classification
Zhang, M., Cui, Z., Neumann, M., and Chen, Y · 2018
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Convolutional neural network architectures for signals supported on graphs
Gama, F., Marques, A. G., Leus, G., and Ribeiro, A · 2019
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