M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen et al., TensorFlow: Large-scale machine learning on heterogeneous systems, 2015.

R. Achanta, A. Shaji, K. Smith, A. Lucchi, P. Fua et al., SLIC superpixels compared to state-of-the-art superpixel methods, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.34, issue.11, pp.2274-2282, 2012.

A. Agaskar and Y. M. Lu, A spectral graph uncertainty principle, IEEE Trans. Information Theory, vol.59, issue.7, pp.4338-4356, 2013.

N. S. Alvar, M. Zolfaghari, and T. Brox, Orientation-boosted voxel nets for 3D object recognition, 2016.

A. Anand, H. S. Koppula, T. Joachims, and A. Saxena, Contextually guided semantic labeling and search for three-dimensional point clouds, The International Journal of Robotics Research, vol.32, issue.1, pp.19-34, 2013.

I. Armeni, O. Sener, A. R. Zamir, H. Jiang, I. Brilakis et al., 3D semantic parsing of large-scale indoor spaces, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

J. Atwood and D. Towsley, Diffusion-convolutional neural networks, Advances in Neural Information Processing Systems (NIPS), 2016.

L. J. Ba, R. Kiros, and G. E. Hinton, Layer normalization. CoRR, 2016.

B. Baker, O. Gupta, N. Naik, R. , and R. , Designing neural network architectures using reinforcement learning, International Conference on Learning Representations (ICLR), 2017.

A. Barabási, A. , and R. , Emergence of scaling in random networks, Science, vol.286, issue.5439, pp.509-512, 1999.

P. W. Battaglia, R. Pascanu, M. Lai, D. J. Rezende, and K. Kavukcuoglu, Interaction networks for learning about objects, relations and physics, Advances in Neural Information Processing Systems (NIPS), pp.4502-4510, 2016.

M. Belkin and P. Niyogi, Laplacian eigenmaps and spectral techniques for embedding and clustering, Advances in Neural Information Processing Systems (NIPS), pp.585-591, 2001.

S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer, Scheduled sampling for sequence prediction with recurrent neural networks, Advances in Neural Information Processing Systems (NIPS), pp.1171-1179, 2015.

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE transactions on pattern analysis and machine intelligence, vol.35, pp.1798-1828, 2013.

A. Bojchevski and S. Günnemann, Deep gaussian embedding of attributed graphs: Unsupervised inductive learning via ranking, 2017.

A. Bojchevski, O. Shchur, D. Zügner, G. , and S. , NetGAN: Generating graphs via random walks, 2018.

K. M. Borgwardt and H. Kriegel, Shortest-path kernels on graphs, IEEE International Conference on Data Mining (ICDM), pp.74-81, 2005.

D. Boscaini, J. Masci, E. Rodolà, and M. M. Bronstein, Learning shape correspondence with anisotropic convolutional neural networks, Advances in Neural Information Processing Systems (NIPS), pp.3189-3197, 2016.

A. Boulch, B. L. Saux, A. , and N. , Unstructured point cloud semantic labeling using deep segmentation networks, Eurographics Workshop on 3D Object Retrieval, vol.2, 2017.

S. R. Bowman, L. Vilnis, O. Vinyals, A. M. Dai, R. Józefowicz et al., Generating sentences from a continuous space, CoNLL, pp.10-21, 2016.

Y. Boykov, O. Veksler, and R. Zabih, Fast approximate energy minimization via graph cuts, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.23, issue.11, pp.1222-1239, 2001.

B. D. Brabandere, X. Jia, T. Tuytelaars, and L. V. Gool, Dynamic filter networks, Advances in Neural Information Processing Systems (NIPS), 2016.

M. Brockschmidt, M. Allamanis, A. L. Gaunt, P. , and O. , Generative code modeling with graphs, 2018.

M. M. Bronstein, J. Bruna, Y. Lecun, A. Szlam, and P. Vandergheynst, Geometric deep learning: Going beyond Euclidean data, IEEE Signal Processing Magazine, vol.34, issue.4, pp.18-42, 2017.

J. Bruna, W. Zaremba, A. Szlam, and Y. Lecun, , 2013.

N. D. Cao and T. Kipf, Molgan: An implicit generative model for small molecular graphs, 2018.

S. Chandra and I. Kokkinos, Fast, exact and multi-scale inference for semantic image segmentation with deep Gaussian CRFs, IEEE European Conference on Computer Vision (ECCV), 2016.
URL : https://hal.archives-ouvertes.fr/hal-01410872

M. Chen, J. Pennington, and S. S. Schoenholz, Dynamical isometry and a mean field theory of RNNs: Gating enables signal propagation in recurrent neural networks, 2018.

T. Chen, B. Dai, D. Liu, and J. Song, Performance of global descriptors for velodyne-based urban object recognition, IEEE Intelligent Vehicles Symposium Proceedings, pp.667-673, 2014.

K. Cho, B. Van-merriënboer, Ç. Gülçehre, D. Bahdanau, F. Bougares et al., Learning phrase representations using RNN encoder-decoder for statistical machine translation, Conference on Empirical Methods in Natural Language Processing, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01433235

M. Cho, J. Sun, O. Duchenne, and J. Ponce, Finding matches in a haystack: A max-pooling strategy for graph matching in the presence of outliers, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2091-2098, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01053675

H. Dai, B. Dai, and L. Song, Discriminative embeddings of latent variable models for structured data, International Conference on Machine Learning (ICML), 2016.

H. Dai, E. B. Khalil, Y. Zhang, B. Dilkina, and L. Song, Learning combinatorial optimization algorithms over graphs, Advances in Neural Information Processing Systems (NIPS), 2017.

H. Dai, Y. Tian, B. Dai, S. Skiena, and L. Song, Syntax-directed variational autoencoder for structured data, International Conference on Learning Representations (ICLR), 2018.

K. Date and R. Nagi, GPU-accelerated Hungarian algorithms for the linear assignment problem, Parallel Computing, vol.57, pp.52-72, 2016.

M. De-deuge, A. Quadros, C. Hung, D. , and B. , Unsupervised feature learning for classification of outdoor 3D scans, Australasian Conference on Robotics and Automation, vol.2, 2013.

A. K. Debnath, R. L. Lopez-de-compadre, G. Debnath, A. J. Shusterman, and C. Hansch, Structure-activity relationship of mutagenic aromatic and heteroaromatic nitro compounds. correlation with molecular orbital energies and hydrophobicity, Journal of medicinal chemistry, vol.34, issue.2, pp.786-797, 1991.

M. Defferrard, X. Bresson, and P. Vandergheynst, Convolutional neural networks on graphs with fast localized spectral filtering, Advances in Neural Information Processing Systems (NIPS), 2016.

J. Demantké, C. Mallet, N. David, and B. Vallet, Dimensionality based scale selection in 3D lidar point clouds. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, pp.97-102, 2011.

I. S. Dhillon, Y. Guan, and B. Kulis, Weighted graph cuts without eigenvectors a multilevel approach, IEEE transactions, issue.11, p.29, 2007.

L. Dinh, J. Sohl-dickstein, and S. Bengio, Density estimation using real NVP, 2016.

P. D. Dobson and A. J. Doig, Distinguishing enzyme structures from non-enzymes without alignments, Journal of molecular biology, vol.330, issue.4, pp.771-783, 2003.

F. Dörfler and F. Bullo, Kron reduction of graphs with applications to electrical networks, IEEE Trans. on Circuits and Systems, issue.1, pp.150-163, 2013.

D. K. Duvenaud, D. Maclaurin, J. Aguilera-iparraguirre, R. Bombarell, T. Hirzel et al., Convolutional networks on graphs for learning molecular fingerprints, Advances in Neural Information Processing Systems (NIPS), 2015.

M. Edwards and X. Xie, Graph based convolutional neural network, British Machine Vision Conference (BMVC), 2016.

F. Engelmann, T. Kontogianni, A. Hermans, L. , and B. , Exploring spatial context for 3d semantic segmentation of point clouds, IEEE International Conference on Computer Vision (ICCV), 3DRMS Workshop, 2017.

P. Erdos and A. Rényi, On the evolution of random graphs, Publ. Math. Inst. Hung. Acad. Sci, vol.5, issue.1, pp.17-60, 1960.

H. Fan, H. Su, and L. J. Guibas, A point set generation network for 3D object reconstruction from a single image, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2463-2471, 2017.

R. Gadde, V. Jampani, M. Kiefel, D. Kappler, and P. Gehler, Superpixel convolutional networks using bilateral inceptions, IEEE European Conference on Computer Vision (ECCV), 2016.
URL : https://hal.archives-ouvertes.fr/hal-01801019

J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, and G. E. Dahl, Neural message passing for quantum chemistry, International Conference on Machine Learning (ICML), pp.1263-1272, 2017.

R. Gómez-bombarelli, D. K. Duvenaud, J. M. Hernández-lobato, J. Aguilera-iparraguirre, T. D. Hirzel et al., Automatic chemical design using a data-driven continuous representation of molecules, 2016.

S. Gong and T. Xiang, Recognition of group activities using dynamic probabilistic networks, IEEE International Conference on Computer Vision (ICCV), pp.742-749, 2003.

I. J. Goodfellow, J. Pouget-abadie, M. Mirza, B. Xu, D. Warde-farley et al., , 2014.

B. Graham, Fractional max-pooling, 2014.

B. Graham, Sparse 3d convolutional neural networks, British Machine Vision Conference (BMVC), vol.9, pp.150-151, 2015.

B. Graham, M. Engelcke, and L. Van-der-maaten, 3d semantic segmentation with submanifold sparse convolutional networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

A. Grover and J. Leskovec, node2vec: Scalable feature learning for networks, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.855-864, 2016.

S. Guinard and L. Landrieu, Weakly supervised segmentation-aided classification of urban scenes from 3D LiDAR point clouds, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01497548

T. Hackel, N. Savinov, L. Ladicky, J. D. Wegner, K. Schindler et al., Semantic3d. net: A new large-scale point cloud classification benchmark, 2017.

T. Hackel, J. D. Wegner, and K. Schindler, Fast semantic segmentation of 3D point clouds with strongly varying density. ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences, vol.3, issue.3, 2016.

W. L. Hamilton, R. Ying, and J. Leskovec, Representation learning on graphs: Methods and applications, 2017.

W. L. Hamilton, Z. Ying, and J. Leskovec, Inductive representation learning on large graphs, Advances in Neural Information Processing Systems (NIPS), pp.1025-1035, 2017.

K. He and J. Sun, Convolutional neural networks at constrained time cost, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

K. Hornik, Approximation capabilities of multilayer feedforward networks, Neural Networks, vol.4, issue.2, pp.251-257, 1991.

H. Hu, D. Munoz, J. A. Bagnell, H. , and M. , Efficient 3-d scene analysis from streaming data, IEEE International Conference on Robotics and Automation (ICRA), 2013.

G. Huang, Z. Liu, and K. Q. Weinberger, Densely connected convolutional networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

J. Huang and S. You, Point cloud labeling using 3D convolutional neural network, 2016.

Q. Huang, W. Wang, and U. Neumann, Recurrent slice networks for 3D segmentation on point clouds, 2018.

S. Ioffe and C. Szegedy, Batch normalization: Accelerating deep network training by reducing internal covariate shift, International Conference on Machine Learning (ICML), 2015.

J. J. Irwin, T. Sterling, M. M. Mysinger, E. S. Bolstad, C. et al., ZINC: A free tool to discover chemistry for biology, Journal of Chemical Information and Modeling, vol.52, issue.7, pp.1757-1768, 2012.

P. Isola, J. Zhu, T. Zhou, and A. A. Efros, Image-to-image translation with conditional adversarial networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5967-5976, 2017.

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu, Spatial transformer networks, Advances in Neural Information Processing Systems (NIPS), pp.2017-2025, 2015.

E. Jang, S. Gu, and B. Poole, Categorical reparameterization with gumbelsoftmax, 2016.

J. W. Jaromczyk and G. T. Toussaint, Relative neighborhood graphs and their relatives, Proceedings of the IEEE, vol.80, issue.9, pp.1502-1517, 1992.

W. Jin, R. Barzilay, and T. S. Jaakkola, Junction tree variational autoencoder for molecular graph generation, International Conference on Machine Learning (ICML), pp.2328-2337, 2018.

D. D. Johnson, Learning graphical state transitions, International Conference on Learning Representations (ICLR), 2017.

S. M. Kearnes, K. Mccloskey, M. Berndl, V. S. Pande, R. et al., Molecular graph convolutions: moving beyond fingerprints, Journal of Computer-Aided Molecular Design, vol.30, issue.8, pp.595-608, 2016.

B. Kim, P. Kohli, and S. Savarese, 3D scene understanding by voxel-CRF, IEEE International Conference on Computer Vision (ICCV), 2013.

D. P. Kingma and J. Ba, Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR), 2015.

D. P. Kingma, T. Salimans, and M. Welling, Improving variational inference with inverse autoregressive flow, 2016.

D. P. Kingma and M. Welling, Auto-encoding variational bayes, 2013.

T. N. Kipf and M. Welling, Variational graph auto-encoders, Advances in Neural Information Processing Systems (NIPS), Workshop on Bayesian Deep Learning, 2016.

R. Klokov and V. S. Lempitsky, Escape from cells: Deep Kd-networks for the recognition of 3D point cloud models, 2017.

R. Kondor, N-body networks: a covariant hierarchical neural network architecture for learning atomic potentials, 2018.

R. Kondor, H. T. Son, H. Pan, B. Anderson, and S. Trivedi, Covariant compositional networks for learning graphs, 2018.

H. S. Koppula, A. Anand, T. Joachims, and A. Saxena, Semantic labeling of 3D point clouds for indoor scenes, Advances in Neural Information Processing Systems (NIPS), pp.244-252, 2011.

S. I. Ktena, S. Parisot, E. Ferrante, M. Rajchl, M. Lee et al., Distance metric learning using graph convolutional networks: Application to functional brain networks, In Medical Image Computing and Computer-Assisted Intervention, 2017.

S. Kullback and R. A. Leibler, On information and sufficiency, The Annals of Mathematical Statistics, vol.22, issue.1, pp.79-86, 1951.

M. J. Kusner and J. M. Hernández-lobato, GANS for sequences of discrete elements with the gumbel-softmax distribution, 2016.

M. J. Kusner, B. Paige, and J. M. Hernández-lobato, Grammar variational autoencoder, International Conference on Machine Learning (ICML), pp.1945-1954, 2017.

L. Landrieu and G. Obozinski, Cut pursuit: Fast algorithms to learn piecewise constant functions on general weighted graphs, SIAM Journal on Imaging Sciences, vol.10, issue.4, pp.1724-1766, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01306779

L. Landrieu, H. Raguet, B. Vallet, C. Mallet, and M. Weinmann, A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds, ISPRS Journal of Photogrammetry and Remote Sensing, vol.132, pp.102-118, 2017.
URL : https://hal.archives-ouvertes.fr/hal-01505245

L. Landrieu and M. Simonovsky, Large-scale point cloud semantic segmentation with superpoint graphs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01801186

G. Landrum, RDKit: Open-source cheminformatics, 2011.

M. Larsson, F. Kahl, S. Zheng, A. Arnab, P. H. Torr et al., Learning arbitrary potentials in CRFs with gradient descent, 2017.

F. J. Lawin, M. Danelljan, P. Tosteberg, G. Bhat, F. S. Khan et al., Deep projective 3D semantic segmentation, 2017.

Y. Lecun, Y. Bengio, and G. Hinton, Deep learning, Nature, vol.521, issue.7553, pp.436-444, 2015.

Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner, Gradient-based learning applied to document recognition, Proceedings of the IEEE, vol.86, issue.11, pp.2278-2324, 1998.

C. Ledig, L. Theis, F. Huszar, J. Caballero, A. Cunningham et al., Photo-realistic single image super-resolution using a generative adversarial network, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.105-114, 2017.

T. Lei, W. Jin, R. Barzilay, and T. Jaakkola, Deriving neural architectures from sequence and graph kernels, 2017.

Y. Li, R. Bu, M. Sun, C. , and B. , , 2018.

Y. Li, S. Pirk, H. Su, C. R. Qi, and L. J. Guibas, FPNN: field probing neural networks for 3d data, Advances in Neural Information Processing Systems (NIPS), pp.307-315, 2016.

Y. Li, K. Swersky, and R. S. Zemel, Generative moment matching networks, International Conference on Machine Learning (ICML), pp.1718-1727, 2015.

Y. Li, D. Tarlow, M. Brockschmidt, and R. S. Zemel, Gated graph sequence neural networks, International Conference on Learning Representations (ICLR), 2016.

Y. Li, O. Vinyals, C. Dyer, R. Pascanu, and P. Battaglia, Learning deep generative models of graphs, International Conference on Machine Learning (ICML), 2018.

Y. Li, R. Yu, C. Shahabi, and Y. Liu, Diffusion convolutional recurrent neural network: Data-driven traffic forecasting, International Conference on Learning Representations, 2018.

Y. Li, L. Zhang, and Z. Liu, Multi-objective de novo drug design with conditional graph generative model, J. Cheminformatics, vol.10, issue.1, p.24, 2018.

X. Liang, L. Lin, X. Shen, J. Feng, S. Yan et al., Interpretable structure-evolving LSTM, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2175-2184, 2017.

X. Liang, X. Shen, J. Feng, L. Lin, Y. et al., Semantic object parsing with graph LSTM, IEEE European Conference on Computer Vision (ECCV), pp.125-143, 2016.

G. Lin, C. Shen, A. Van-den-hengel, R. , and I. D. , Efficient piecewise training of deep structured models for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

Q. Liu, M. Allamanis, M. Brockschmidt, and A. L. Gaunt, Constrained graph variational autoencoders for molecule design, 2018.

J. Long, E. Shelhamer, D. , and T. , Fully convolutional networks for semantic segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3431-3440, 2015.

Y. Lu and C. Rasmussen, Simplified Markov random fields for efficient semantic labeling of 3d point clouds, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp.2690-2697, 2012.

A. Makhzani, J. Shlens, N. Jaitly, and I. J. Goodfellow, Adversarial autoencoders, 2015.

A. Martinovic, J. Knopp, H. Riemenschneider, and L. Van-gool, 3D all the way: Semantic segmentation of urban scenes from start to end in 3D, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst, Geodesic convolutional neural networks on Riemannian manifolds, IEEE International Conference on Computer Vision (ICCV), Workshop, pp.37-45, 2015.

M. Masoumi and A. B. Hamza, Shape classification using spectral graph wavelets, Appl. Intell, vol.47, issue.4, pp.1256-1269, 2017.

D. Maturana and S. Scherer, Voxnet: A 3D convolutional neural network for real-time object recognition, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015.

B. D. Mckay and A. Piperno, Practical graph isomorphism, II, Journal of Symbolic Computation, vol.60, issue.0, pp.94-112, 2014.

T. Mikolov, K. Chen, G. Corrado, D. , and J. , Efficient estimation of word representations in vector space, 2013.

D. Mishkin and J. Matas, All you need is a good init, International Conference on Learning Representations (ICLR), 2016.

F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda et al., Geometric deep learning on graphs and manifolds using mixture model CNNs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.5425-5434, 2017.

F. Monti, K. Otness, and M. M. Bronstein, Motifnet: a motif-based graph convolutional network for directed graphs, 2018.

L. Mou, G. Li, L. Zhang, T. Wang, J. et al., Convolutional neural networks over tree structures for programming language processing, AAAI Conference on Artificial Intelligence, pp.1287-1293, 2016.

D. Munoz, J. A. Bagnell, N. Vandapel, H. , and M. , Contextual classification with functional max-margin Markov networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2009.

A. Narayanan, M. Chandramohan, L. Chen, Y. Liu, and S. Saminathan, subgraph2vec: Learning distributed representations of rooted sub-graphs from large graphs, 2016.

J. Niemeyer, F. Rottensteiner, and U. Soergel, Contextual classification of lidar data and building object detection in urban areas, ISPRS Journal of Photogrammetry and Remote Sensing, vol.87, pp.152-165, 2014.

M. Niepert, M. Ahmed, and K. Kutzkov, Learning convolutional neural networks for graphs, International Conference on Machine Learning (ICML), 2016.

M. Olivecrona, T. Blaschke, O. Engkvist, C. , and H. , Molecular de novo design through deep reinforcement, 2017.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang et al., Automatic differentiation in PyTorch, Advances in Neural Information Processing Systems (NIPS), 2017.

J. Pearl, Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference, 1988.

B. Perozzi, R. Al-rfou, and S. Skiena, Deepwalk: online learning of social representations, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp.701-710, 2014.

N. Perraudin, J. Paratte, D. I. Shuman, V. Kalofolias, P. Vandergheynst et al., GSPBOX: A toolbox for signal processing on graphs, 2014.

C. R. Qi, H. Su, K. Mo, and L. J. Guibas, PointNet: Deep learning on point sets for 3D classification and segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

C. R. Qi, H. Su, M. Nießner, A. Dai, M. Yan et al., Volumetric and multi-view CNNs for object classification on 3D data, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.

C. R. Qi, L. Yi, H. Su, and L. J. Guibas, PointNet++: Deep hierarchical feature learning on point sets in a metric space, Advances in Neural Information Processing Systems (NIPS), 2017.

X. Qi, R. Liao, J. Jia, S. Fidler, and R. Urtasun, 3D graph neural networks for RGBD semantic segmentation, IEEE International Conference on Computer Vision (ICCV), pp.5209-5218, 2017.

A. Radford, L. Metz, C. , and S. , Unsupervised representation learning with deep convolutional generative adversarial networks, 2015.

R. Ramakrishnan, P. O. Dral, M. Rupp, and O. A. Lilienfeld, Quantum chemistry structures and properties of 134 kilo molecules, p.1, 2014.

S. E. Reed, Z. Akata, X. Yan, L. Logeswaran, B. Schiele et al., , 2016.

, Generative adversarial text to image synthesis, International Conference on Machine Learning (ICML), pp.1060-1069

G. Riegler, A. O. Ulusoy, H. Bischof, and A. Geiger, OctNetFusion: Learning depth fusion from data, Proceedings of the International Conference on 3D Vision, 2017.

G. Riegler, A. O. Ulusoy, and A. Geiger, OctNet: Learning deep 3D representations at high resolutions, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

R. B. Rusu and S. Cousins, 3D is here: Point cloud library (pcl), IEEE International Conference on Robotics and Automation (ICRA), pp.1-4, 2011.

B. Samanta, A. De, N. Ganguly, and M. Gomez-rodriguez, Designing random graph models using variational autoencoders with applications to chemical design, 2018.

S. Sardellitti, S. Barbarossa, L. , and P. D. , On the graph fourier transform for directed graphs, J. Sel. Topics Signal Processing, vol.11, issue.6, pp.796-811, 2017.

A. M. Saxe, J. L. Mcclelland, and S. Ganguli, Exact solutions to the nonlinear dynamics of learning in deep linear neural networks, International Conference on Learning Representations, 2014.

F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, and G. Monfardini, The graph neural network model, IEEE Trans. Neural Networks, vol.20, issue.1, pp.61-80, 2009.

T. Schlegl, P. Seeböck, S. M. Waldstein, U. Schmidt-erfurth, and G. Langs, Unsupervised anomaly detection with generative adversarial networks to guide marker discovery, Information Processing in Medical Imaging (IPMI), pp.146-157, 2017.

M. Schlichtkrull, T. N. Kipf, P. Bloem, R. V. Berg, I. Titov et al., Modeling relational data with graph convolutional networks, 2017.

B. Schölkopf and A. J. Smola, Learning with kernels: support vector machines, regularization, optimization, and beyond, 2002.

K. T. Schütt, F. Arbabzadah, S. Chmiela, K. R. Müller, and A. Tkatchenko, Quantum-chemical insights from deep tensor neural networks, 2017.

K. T. Schütt, P. Kindermans, H. E. Felix, S. Chmiela, A. Tkatchenko et al., Schnet: A continuous-filter convolutional neural network for modeling quantum interactions, Advances in Neural Information Processing Systems (NIPS), pp.992-1002, 2017.

A. G. Schwing and R. Urtasun, Fully connected deep structured networks, 2015.

M. H. Segler, T. Kogej, C. Tyrchan, and M. P. Waller, Generating focused molecule libraries for drug discovery with recurrent neural networks, ACS Central Science, vol.4, issue.1, pp.120-131, 2018.

R. Shapovalov, D. Vetrov, and P. Kohli, Spatial inference machines, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2013.

D. Shen, G. Wu, S. , and H. , Deep learning in medical image analysis, Annual review of biomedical engineering, vol.19, pp.221-248, 2017.

N. Shervashidze, P. Schweitzer, E. J. Van-leeuwen, K. Mehlhorn, and K. M. Borgwardt, Weisfeiler-lehman graph kernels, Journal of Machine Learning Research, vol.12, pp.2539-2561, 2011.

D. I. Shuman, M. J. Faraji, and P. Vandergheynst, A multiscale pyramid transform for graph signals, IEEE Trans. Signal Processing, vol.64, issue.8, pp.2119-2134, 2016.

D. I. Shuman, S. K. Narang, P. Frossard, A. Ortega, and P. Vandergheynst, The emerging field of signal processing on graphs: Extending high-dimensional data analysis to networks and other irregular domains, IEEE Signal Processing Magazine, vol.30, issue.3, pp.83-98, 2013.

D. I. Shuman, P. Vandergheynst, F. , and P. , Chebyshev polynomial approximation for distributed signal processing, Distributed Computing in Sensor Systems (DCOSS), pp.1-8, 2011.

M. Simonovsky, B. Gutiérrez-becker, D. Mateus, N. Navab, and N. Komodakis, A deep metric for multimodal registration, Medical Image Computing and Computer-Assisted Intervention (MICCAI), pp.10-18, 2016.
URL : https://hal.archives-ouvertes.fr/hal-01576914

M. Simonovsky and N. Komodakis, OnionNet: Sharing features in cascaded deep classifiers, British Machine Vision Conference (BMVC), 2016.

M. Simonovsky and N. Komodakis, Dynamic edge-conditioned filters in convolutional neural networks on graphs, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
URL : https://hal.archives-ouvertes.fr/hal-01576919

M. Simonovsky and N. Komodakis, GraphVAE: Towards generation of small graphs using variational autoencoders, International Conference on Artificial Neural Networks (ICANN), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01990381

M. Simonovsky and N. Komodakis, Towards variational generation of small graphs, International Conference on Learning Representations (ICLR), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01801194

R. Sinkhorn and P. Knopp, Concerning nonnegative matrices and doubly stochastic matrices, Pacific Journal of Mathematics, vol.21, issue.2, pp.343-348, 1967.

T. A. Snijders and K. Nowicki, Estimation and prediction for stochastic blockmodels for graphs with latent block structure, Journal of Classification, vol.14, issue.1, pp.75-100, 1997.

K. Sohn, H. Lee, Y. , and X. , Learning structured output representation using deep conditional generative models, Advances in Neural Information Processing Systems (NIPS), pp.3483-3491, 2015.

D. A. Spielman and N. Srivastava, Graph sparsification by effective resistances, SIAM Journal on Computing, vol.40, issue.6, pp.1913-1926, 2011.

R. Stewart, M. Andriluka, and A. Y. Ng, End-to-end people detection in crowded scenes, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.2325-2333, 2016.

H. Su, S. Maji, E. Kalogerakis, and E. G. Miller, Multi-view convolutional neural networks for 3D shape recognition, IEEE International Conference on Computer Vision (ICCV), 2015.

I. Sutskever, J. Martens, and G. E. Hinton, Generating text with recurrent neural networks, International Conference on Machine Learning (ICML), pp.1017-1024, 2011.

C. Tallec and Y. Ollivier, Can recurrent neural networks warp time, International Conference on Learning Representations (ICLR), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01812064

M. Tatarchenko, A. Dosovitskiy, and T. Brox, Octree generating networks: Efficient convolutional architectures for high-resolution 3d outputs, IEEE International Conference on Computer Vision (ICCV), 2017.

M. Tatarchenko, J. Park, V. Koltun, and Q. Zhou, Tangent convolutions for dense prediction in 3D, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

L. P. Tchapmi, C. B. Choy, I. Armeni, J. Gwak, and S. Savarese, SEGCloud: Semantic segmentation of 3D point clouds, 2017.

L. Theis, A. Van-den-oord, and M. Bethge, A note on the evaluation of generative models, 2015.

N. Thomas, T. Smidt, S. M. Kearnes, L. Yang, L. Li et al., Tensor field networks: Rotation-and translation-equivariant neural networks for 3d point clouds, 2018.

I. O. Tolstikhin, O. Bousquet, S. Gelly, and B. Schölkopf, Wasserstein autoencoders, International Conference on Learning Representations (ICLR), 2018.

W. Tu, M. Liu, V. Jampani, D. Sun, S. Chien et al., Learning superpixels with segmentation-aware affinity loss, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

D. Ulyanov, A. Vedaldi, V. Lempitsky, A. Van-den-oord, N. Kalchbrenner et al., Pixel recurrent neural networks, International Conference on Machine Learning (ICML), pp.1747-1756, 2016.

P. Veli?kovi?, G. Cucurull, A. Casanova, A. Romero, P. Liò et al., Graph Attention Networks. International Conference on Learning Representations (ICLR), 2018.

T. Verelst, M. Berman, and M. B. Blaschko, Generating superpixels with deep representations, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Deep Vision Workshop, 2018.

N. Verma, E. Boyer, and J. Verbeek, FeaStNet: Feature-steered graph convolutions for 3D shape analysis, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.
URL : https://hal.archives-ouvertes.fr/hal-01540389

O. Vinyals, S. Bengio, and M. Kudlur, Order matters: Sequence to sequence for sets, 2015.

N. Wale, I. A. Watson, and G. Karypis, Comparison of descriptor spaces for chemical compound retrieval and classification, Knowledge and Information Systems, vol.14, issue.3, pp.347-375, 2008.

P. Wang, Y. Liu, Y. Guo, C. Sun, and X. Tong, O-CNN: Octree-based convolutional neural networks for 3d shape analysis, ACM Transactions on Graphics, issue.4, p.36, 2017.

X. Wang, R. Girshick, A. Gupta, and K. He, Non-local neural networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018.

Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein et al., Dynamic graph CNN for learning on point clouds, 2018.

D. Weininger, SMILES, a chemical language and information system. 1. Introduction to methodology and encoding rules, Journal of Chemical Information and Computer Sciences, vol.28, issue.1, pp.31-36, 1988.

M. Weinmann, S. Hinz, and M. Weinmann, A hybrid semantic point cloud classification-segmentation framework based on geometric features and semantic rules, PFG-Journal of Photogrammetry, Remote Sensing and Geoinformation Science, vol.85, issue.3, pp.183-194, 2017.

M. Weinmann, A. Schmidt, C. Mallet, S. Hinz, F. Rottensteiner et al., Contextual classification of point cloud data by exploiting individual 3D neighborhoods. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol.4, pp.271-278, 2015.

R. J. Williams and D. Zipser, A learning algorithm for continually running fully recurrent neural networks, Neural Computation, vol.1, issue.2, pp.270-280, 1989.

D. Wolf, J. Prankl, and M. Vincze, Fast semantic segmentation of 3D point clouds using a dense CRF with learned parameters, IEEE International Conference on Robotics and Automation (ICRA), 2015.

S. Wolf, L. Schott, U. Köthe, and F. A. Hamprecht, Learned watershed: End-to-end learning of seeded segmentation, IEEE International Conference on Computer Vision (ICCV), pp.2030-2038, 2017.

Z. Wu, B. Ramsundar, E. N. Feinberg, J. Gomes, C. Geniesse et al., Moleculenet: a benchmark for molecular machine learning, Chemical science, vol.9, issue.2, pp.513-530, 2018.

Z. Wu, S. Song, A. Khosla, X. Tang, X. et al., 3D ShapeNets for 2.5D object recognition and next-best-view prediction, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015.

Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang et al., 3D ShapeNets: A deep representation for volumetric shapes, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.1912-1920, 2015.

D. Xu, Y. Zhu, C. B. Choy, and L. Fei-fei, Scene graph generation by iterative message passing, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.3097-3106, 2017.

P. Yanardag and S. V. Vishwanathan, Deep graph kernels, ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2015.

L. Yi, H. Su, X. Guo, and L. J. Guibas, SyncSpecCNN: Synchronized spectral CNN for 3D shape segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.6584-6592, 2017.

J. You, B. Liu, R. Ying, V. S. Pande, and J. Leskovec, Graph convolutional policy network for goal-directed molecular graph generation, 2018.

J. You, R. Ying, X. Ren, W. L. Hamilton, and J. Leskovec, GraphRNN: A deep generative model for graphs, International Conference on Machine Learning (ICML), 2018.

L. Yu, W. Zhang, J. Wang, Y. , and Y. , SeqGAN: Sequence generative adversarial nets with policy gradient, AAAI Conference on Artificial Intelligence, 2017.

Y. Yuan, X. Liang, X. Wang, D. Yeung, and A. Gupta, Temporal dynamic graph LSTM for action-driven video object detection, IEEE International Conference on Computer Vision (ICCV), pp.1819-1828, 2017.

M. Zhang, Z. Cui, M. Neumann, C. , and Y. , An end-to-end deep learning architecture for graph classification, AAAI Conference on Artificial Intelligence, 2018.

S. Zheng, S. Jayasumana, B. Romera-paredes, V. Vineet, Z. Su et al., Conditional random fields as recurrent neural networks, IEEE International Conference on Computer Vision (ICCV), 2015.