Metric learning on manifolds, 2018. ,
Riemannian L p center of mass : existence, uniqueness and convexity, Proc. Amer. Math. Soc, vol.139, issue.2, pp.655-6673, 2011. ,
Riemannian medians and means with applications to radar signal processing, J. Sel. Topics Signal Processing, vol.7, issue.4, pp.595-604, 2013. ,
Stochastic algorithms for computing means of probability measures, Stoch. Proc. Appl, vol.58, issue.9, pp.1473-1455, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00540623
, Means in complete manifolds : uniqueness and approximation. ESAIM Probability and statistics, vol.18, pp.185-206, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00717677
Stochastic algorithms for computing p-means of probability measures, geometry of Radar Toeplitz covariance matrices and applications to HR Doppler processing, International Radar Symposium (IRS), pp.651-656, 2011. ,
Multiclass brain-computer interface classification by Riemannian geometry, IEEE Trans. Biomed. Engineering, vol.59, issue.4, pp.920-928, 2012. ,
URL : https://hal.archives-ouvertes.fr/hal-00681328
Stochastic gradient descent on Riemannian manifolds, IEEE Trans. Autom. Control, vol.122, issue.4, pp.2217-2229, 2013. ,
Learning community embedding with community detection and node embedding on graphs, Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp.377-386, 2017. ,
Neural embeddings of graphs in hyperbolic space. 13th international workshop on mining and learning with graphs, 2017. ,
Largemargin classification in hyperbolic space, CoRR, 2018. ,
, node2vec : Scalable feature learning for networks. KDD : proceedings. International Conference on Knowledge Discovery & Data Mining, pp.855-864, 2016.
Learning graph-structured data using poincaré embeddings and riemannian k-means algorithms, 2019. ,
URL : https://hal.archives-ouvertes.fr/hal-02339208
Differential geometry, Lie groups, and symmetric spaces, 2001. ,
Distributed representations of words and phrases and their compositionality, Advances in Neural Information Processing Systems, vol.26, pp.3111-3119, 2013. ,
, Learning distributed representations of graphs. CoRR, abs/1707.05005, vol.2, 2017.
Poincaré embeddings for learning hierarchical representations, Advances in Neural Information Processing Systems, vol.30, pp.6338-6347, 2017. ,
Glove : Global vectors for word representation, EMNLP, 2014. ,
Deepwalk : Online learning of social representations, Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '14, pp.701-710, 2014. ,
The network data repository with interactive graph analytics and visualization, Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, 2015. ,
Manton. Riemannian gaussian distributions on the space of symmetric positive definite matrices, IEEE Trans. Information Theory, vol.63, issue.4, pp.2153-2170, 2017. ,
Vemuri. Gaussian distributions on Riemannian symmetric spaces : Statistical learning with structured covariance matrices, IEEE Trans. Information Theory, vol.64, issue.2, pp.752-772, 2018. ,
Representation tradeoffs for hyperbolic embeddings, Proceedings of the 35th International Conference on Machine Learning, pp.4457-4466, 2018. ,
, Poincaré glove : Hyperbolic word embeddings, 2018.
Community-enhanced network representation learning for network analysis, 2016. ,
Structural deep network embedding, Proceedings of the 22Nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '16, pp.1225-1234, 2016. ,
Learning community embedding with Riemannian expectation maximisation algorithms, 2019. ,
From node embedding to community embedding, 2016. ,