Article Source

• GEOMETRIC DEEP LEARNING

Geometric Deep Learning Paper & Code

• M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, Geometric deep learning: going beyond Euclidean data, IEEE Signal Processing Magazine 2017 (Review paper)
• H. Maron, M. Galun, N. Aigerman, M. Trope, N. Dym, E. Yumer, V. G. Kim, Y. Lipman, Convolutional Neural Networks on Surfaces via Seamless Toric Covers, 2017
• K. T. Schütt, P. Kindermans, H. E. Sauceda, S. Chmiela, A. Tkatchenko, K. Müller, SchNet: A continuous-filter convolutional neural network for modeling quantum interactions, NIPS 2017
• T. Lei, W. Jin, R. Barzilay, T. Jaakkola, Deriving Neural Architectures from Sequence and Graph Kernels, ICML 2017
• R. Levie*, F. Monti*, X. Bresson, M. M. Bronstein, CayleyNets: Graph convolutional neural networks with complex rational spectral filters, 2017 (CayleyNet framework)
• O. Litany, T. Remez, E. Rodolà, A. M. Bronstein, M. M. Bronstein, Deep Functional Maps: Structured Prediction for Dense Shape Correspondence, 2017 (FMNet framework) [CODE]
• F. Monti, X. Bresson, M. M. Bronstein, Geometric matrix completion with recurrent multi-graph neural networks, NIPS 2017 (CNNs on multiple graphs) [CODE]
• J. Gilmer, S. S. Schoenholz, P. F. Riley, O. Vinyals, G. E. Dahl, Neural Message Passing for Quantum Chemistry, ICML 2017
• Z. Huang, C. Wan, T. Probst, L. Van Gool, Deep Learning on Lie Groups for Skeleton-based Action Recognition, CVPR 2017 [CODE]
• Y. Seo, M. Defferrard , P. Vandergheynst, X. Bresson, Structured Sequence Modeling with Graph Convolutional Recurrent Networks , 2016 (recurrent single graph CNN) [CODE]
• L. Yi, H. Su, X. Guo, L. Guibas, SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation , CVPR 2017 (spectral transformer networks)
• F. Monti*, D. Boscaini*, J. Masci, E. Rodolà, J. Svoboda, M. M. Bronstein, Geometric deep learning on graphs and manifolds using mixture model CNNs, CVPR 2017 (MoNet framework) [CODE] [VIDEO]
• T. Kipf, M. Welling, Semi-supervised Classification with Graph Convolutional Networks, ICLR 2017 (simplification of ChebNet) [CODE]
• M. Defferrard, X. Bresson, P. Vandergheynst, Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering, NIPS 2016 (ChebNet framework) [TF CODE] [PyTorch CODE]
• M. Niepert, M. Ahmed, K. Kutzkov, Learning Convolutional Neural Networks for Graphs, ICML 2016
• H. Dai, B. Dai, L. Song, Discriminative embeddings of latent variable models for structured data, ICML 2016
• D. Boscaini, J. Masci, E. Rodolà, M. M. Bronstein, Learning shape correspondence with anisotropic convolutional neural networks, NIPS 2016 (Anisotropic CNN framework)
• J. Masci, D. Boscaini, M. M. Bronstein, P. Vandergheynst, Geodesic convolutional neural networks on Riemannian manifolds, 3dRR 2015 (Geodesic CNN framework)
• D. Duvenaud, D. Maclaurin, J. Aguilera-Iparraguirre, R. Gomez-Bombarelli, T. Hirzel, A. Aspuru-Guzik, R. P. Adams, Convolutional Networks on Graphs for Learning Molecular Fingerprints, NIPS 2015 (molecular fingerprints using graph CNNs)
• J. Atwood, D. Towsley, Diffusion-Convolutional Neural Networks, 2015
• M. Henaff, J. Bruna, Y. LeCun: Deep Convolutional Networks on Graph-Structured Data, 2015
• J. Bruna, W. Zaremba, A. Szlam, Y. LeCun, Spectral Networks and Deep Locally Connected Networks on Graphs, ICLR 2014 (spectral CNN on graphs)
• F. Scarselli, M. Gori, A. C. Tsoi, M. Hagenbuchner, G. Monfardini, The graph neural network model, Trans. Neural Networks 20(1):61-80, 2009 (first neural networks on graphs)