Adrien Poulenard

padrien@stanford.edu
personal page

Research interests

I do research in shape analysis and geometric deep learning. My work focuses on equivariance properties of neural networks, but I have a broad interest in artificial intelligence and machine learning.

Recent publications

Poulenard, A., & Guibas, L. J. (2021). A Functional Approach to Rotation Equivariant Non-Linearities for Tensor Field Networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13174-13183).   
R.Sajnani, A.Poulenard, J.Jain, R.Dua, L.Guibas, and S.Sridhar, ConDor: Self-Supervised Canonicalization of 3D Pose for Partial Shapes, arXiv preprint arXiv:2201.07788, (2022).