Graph kernels between point clouds, International Conference on Machine Learning (ICML), 2008. ,
URL : https://hal.archives-ouvertes.fr/hal-00200109
Protein function prediction via graph kernels, Bioinformatics, vol.21, pp.47-56, 2005. ,
Biological sequence modeling with convolutional kernel networks, vol.35, pp.3294-3302, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-01632912
Recurrent kernel networks, Adv. Neural Information Processing Systems (NeurIPS), 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02151135
Graph neural tangent kernel: Fusing graph neural networks with graph kernels, Adv. Neural Information Processing Systems (NeurIPS), 2019. ,
On graph kernels: Hardness results and efficient alternatives, Learning theory and kernel machines, pp.129-143, 2003. ,
Anonymous walk embeddings, International Conference on Machine Learning, pp.2186-2195, 2018. ,
Marginalized kernels between labeled graphs, International Conference on Machine Learning (ICML), 2003. ,
Benchmark data sets for graph kernels, 2016. ,
Adam: A method for stochastic optimization, International Conference on Learning Representations (ICLR), 2015. ,
Semi-supervised classification with graph convolutional networks, International Conference on Learning Representations (ICLR, 2017. ,
A property testing framework for the theoretical expressivity of graph kernels, International Joint Conferences on Artificial Intelligence (IJCAI), 2018. ,
A survey on graph kernels, Applied Network Science, vol.5, issue.1, pp.1-42, 2020. ,
Deriving neural architectures from sequence and graph kernels, International Conference on Machine Learning (ICML), 2017. ,
The spectrum kernel: A string kernel for svm protein classification, Biocomputing, pp.564-575, 2001. ,
Mismatch string kernels for discriminative protein classification, Bioinformatics, vol.20, issue.4, pp.467-476, 2004. ,
End-to-end kernel learning with supervised convolutional kernel networks, Adv. Neural Information Processing Systems (NIPS), 2016. ,
Weisfeiler and Leman go neural: Higher-order graph neural networks, AAAI Conference on Artificial Intelligence, 2019. ,
Generalized max pooling, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2014. ,
Pre-training graph neural networks with kernels, 2018. ,
Learning convolutional neural networks for graphs, International Conference on Machine Learning (ICML), 2016. ,
Kernel graph convolutional neural networks, International Conference on Artificial Neural Networks (ICANN), 2018. ,
Protein homology detection using string alignment kernels, Bioinformatics, vol.20, issue.11, pp.1682-1689, 2004. ,
URL : https://hal.archives-ouvertes.fr/hal-00433587
Integral transforms, reproducing kernels and their applications, vol.369, 1997. ,
Efficient graphlet kernels for large graph comparison, International Conference on Artificial Intelligence and Statistics (AISTATS), 2009. ,
Weisfeiler-Lehman graph kernels, Journal of Machine Learning Research (JMLR), vol.12, pp.2539-2561, 2011. ,
Regression shrinkage and selection via the lasso, Journal of the Royal Statistical Society: Series B (Methodological), vol.58, issue.1, pp.267-288, 1996. ,
Wasserstein Weisfeiler-Lehman graph kernels, Adv. Neural Information Processing Systems (NeurIPS), 2019. ,
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
Graph kernels, Journal of Machine Learning Research, vol.11, pp.1201-1242, 2010. ,
Using the Nyström method to speed up kernel machines, Adv. Neural Information Processing Systems (NIPS), 2001. ,
How powerful are graph neural networks?, International Conference on Learning Representations (ICLR), 2019. ,
Gnnexplainer: Generating explanations for graph neural networks, Adv. Neural Information Processing Systems (NeurIPS), 2019. ,
Improved Nyström low-rank approximation and error analysis, International Conference on Machine Learning (ICML), 2008. ,
An end-to-end deep learning architecture for graph classification, AAAI Conference on Artificial Intelligence, 2018. ,
Retgk: Graph kernels based on return probabilities of random walks, Adv. Neural Information Processing Systems (NeurIPS), 2018. ,