Geometric Deep Learning - Michael Bronstein - MLSS 2020, Tübingen

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0:00:00 Speaker Introduction
0:02:04 Geometric Deep Learning - going beyond Euclidean data
0:02:36 Motivation
0:02:48 Perceptron
0:03:03 Multilayer perceptron
0:03:20 Multilayer perceptron = universal approximator
0:03:57 Curse of dimensionality
0:05:31 Computer vision problems
0:06:17 Take advantage of structure!
0:06:54 Convolutional Neural Networks
0:07:35 Inductive biases
0:08:20 energy U = ?
0:10:43 Geometric Deep Learning
0:10:57 Prototypical objects
0:11:16 Images and Graphs
0:12:24 Convolution
0:12:38 Fully connected layer
0:13:06 Sparsely connected layer
0:13:21 Convolutional layer
0:14:24 Shift-equivariance
0:14:43 Joint diagonalization
0:15:03 Eigenvectors of ST = Fourier transform
0:15:15 Convolution in spectral domain
0:15:36 Convolution theorem
0:16:13 Key insights
0:17:12 From grids to graphs
0:17:15 Graphs
0:17:47 Attributes
0:18:12 Adjacently matrix
0:18:22 Weighted adjacently matrix
0:18:39 Graph Laplacian
0:19:23 Dirichlet energy
0:19:45 Laplacian on manifolds and meshes
0:20:28 Convolution on graphs?
0:20:53 Graph Fourier transform
0:21:03 1D grid = ring graph
0:21:30 From Grid to Graph Fourier Transform
0:22:52 Spectral graph convolution
0:24:32 Spectral graph convolution: drawbacks
0:24:54 Basis dependence
0:25:45 Isotropic filters on graphs
0:26:38 Anisotropic filters on meshes
0:27:14 Spectral graph convolution, take 2
0:28:16 Polynomial filter (ChebNet)
0:29:46 Spatial graph convolution
0:30:09 Convolution, revisited
0:31:01 Graph Convolutional Networks (GCN)
0:33:03 Pooling
0:33:10 Graph coarsening
0:34:11 Deep graph neural networks
0:34:25 Challenges of going deep on graphs
0:35:33 Does depth help?
0:36:41 Simplified GCN (SGCN)
0:37:21 SIGN: Scalable Inception-like Graph Neural network
0:38:30 SIGN scalability
0:39:01 Do we need depth for graphs?
0:39:35 For what graphs do we need depth?
0:43:06 Different recipes for graph convolution
0:43:53 MoNet
0:44:24 Graph Attention Networks (GAT)
0:45:14 Message Passing Neural Network (MPNN)
0:46:19 How powerful are graph neural networks?
0:46:55 Graph isomorphism
0:47:47 Weisfeiler-Lehman test
0:49:20 How powerful is WL test?
0:49:57 Message passing and WL tests
0:53:05 How powerful are GSN?
0:54:09 What substructure to use?
0:54:37 Substructures in chemistry
0:55:34 Theory beyond Weisfeiler-Lehman
0:57:08 What's next?
0:58:16 The road ahead
1:00:51 Dynamic graphs
1:01:22 Temporal Graph Networks
1:02:01 Higher-order structures
1:03:58 Robustness and guaranteed performance
1:05:21 ICLR 2020 submissions keyword statistics
1:05:44 Graphs = systems of relations and interactions
1:08:10 Recommender systems and link prediction
1:09:01 Learning the graph
1:10:32 High-energy physics
1:11:30 Neutrino detection
1:12:22 Computational chemistry and drug design
1:14:26 Hyperfoods
1:16:59 Combinatorial drug therapy
1:18:42 Protein science and cancer immunotherapy
1:19:17 De novo protein design
1:20:15 3D vision and graphics
1:20:55 Shape analysis and synthesis
1:21:15 Classical and Geometric (intrinsic) convolution
1:21:46 Deformable shape correspondence
1:22:01 3D generative models
1:22:24 3D hand reconstruction from 2D images
1:23:33 CVPR 2020 = Geometry + Deep Learning
1:24:12 Conclusions
1:25:54 Q&A

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