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Latest

 

  • Acosta, F., Sanborn, S., Dao Duc, K., Madhav, M., Miolane, N. "Quantifying Local External Curvatures of Neural Manifolds." (2022 - in preparation).

 

  • Huq, F., Dey, A., Yusuf, S., Bazazian, D., Birdal, T., Miolane, N. "Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks." (2022 - in preparation). [Paper].

 

  • Hajij, M., Zamzmi, G., Papamarkou, T., Miolane, N., Guzman-Saenz, A., Ramamurthy, K., N. Higher-Order Attention Networks. (2022 - in preparation). [Paper] [Code (coming soon)].

 

  • Le Brigant, A., Deschamps, J., Collas, A., Miolane, N. "Parametric information geometry with the package Geomstats." || Transactions of Mathematical Software (TOMS). (2022 - in review). [Paper] [Code].

 

  • Utpala, S., Vepakomma, P., Miolane, N. "Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices." || Transactions of Machine Learning Research. (2022 - in review). [Paper] [Code (coming soon)].

 

  • Gabbert, A., Mondo, J., Campanale, J., Mitchell, N., Myers, A., Streichan, S., Miolane, N., Montell, D. "Septins Regulate Border Cell Shape and Surface Geometry ownstream of Rho." || Journal Developmental Cell (2022 - in review).

 

2022

 

  • Donnat, C., Levy, A., Poitevin, F., Miolane, N. "Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy." || Journal of Structural Biology (2022). [Paper] [Code].

 

  • Niederhauser, T., Lester, A., Miolane, N., Dao Duc, K., Madhav, M. "Testing Geometric Representation Hypotheses from Simulated Place Cells Recordings." || NeurIPS Workshop for Symmetry and Geometry in Neural Representations (2022). [Paper] [Code].

 

  • Guigui, N., Miolane, N., Pennec, X.. "Introduction to Riemannian Geometry and Geometric Statistics: from theory to implementation with Geomstats." || Journal of Foundations and Trends in Machine Learning. (2022). [Paper] [Code].

 

  • Myers, A., Utpala, ., Talbar, S., Sanborn, S., Shewmake, C., Donnat, C., Mathe, J., Lupo, U., Sonthalia, R., Cui, X., Szwagier, T., Pignet, A., Bergsson, A., Hauberg, S., Nielsen, D., Sommer, S., Klindt, D., Hermansen, E., Vaupel, M., Dunn, B., Xiong, J., Aharony, N., Pe'er, I., Ambellan, F., Hanik, M., Nava-Yazdani, E., von Tycowicz, C., Miolane, N. "ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results." || Proceedings of Machine Learning Research (2022). [Paper] [Code].

 

  • Papillon, M., Pettee, M., Miolane, N. "Intentional Choreography with Semi-Supervised Recurrent Variational Autoencoders." || NeurIPS Workshop of Creativity and Design (2022). [Paper] [Code].

 

  • Myers, A., Miolane, N. "Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves." || NeurIPS Workshop on Learning Meaningful Representations of Life. (2022). [Paper] [Code].

 

  • Nashed, Y., Peck, A., Martel, J., Levy, A., Koo, B., Wetzstein, G., Miolane, N., Ratner, D., Poitevin, F. "Heterogeneous Reconstructions of Deformable Models in Cryo-Electron Microscopy." || NeurIPS Workshop of Machine Learning for Structural Biology (2022). [Paper] [Code (coming soon)].

 

  • Papillon M., Pettee M., Miolane N. "PirouNet: Creating Dance through Artist-Centric Deep Learning." || EAI ArtsIT Conference (2022). Best Paper Award (Oral). [Paper][Code].

 

  • Levy, A., Poitevin, F., Martel, J., Nashed, Y., Peck, A., Miolane, N., Ratner, D., Dunne, M., Wetzstein, G. "CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images." Best Poster Award at the 4th International Symposium on Cryo-3D Image Analysis. || ECCV European Conference on Computer Vision (2022). [Paper] [Code].

 

  • Utpala, S., Miolane, N. "Biological Shape Analysis with Geometric Statistics and Learning." || Oberwolfach Snapshots of Modern Mathematics (2022).

 

  • Legendre, N, Dao Duc, K., Miolane, N. "Defining an Action of SO(d)-Rotations on Images Generated by Projections of d-Dimensional Objects: Applications to Pose Inference with Geometric VAEs." || GRETSI Conference (2022). [Paper].

 

Previous

 

  • Miolane, N., Caorsi, M., Lupo, U., Guerard, M., Guigui, N., Mathe, J., Cabanes, Y., Reise, W., Davies, T., Leitão, A., Mohapatra, S., Utpala, S., Shailja, S., Corso, G., Liu, G., Iuricich, F., Manolache, A., Nistor, M., Bejan, M., Mihai Nicolicioiu, A., Luchian, B.-A., Stupariu, M.-S., Michel, F., Dao Duc, K., Abdulrahman, B., Beketov, M., Maignant, E., Liu, Z., Černý, M., Bauw, M., Velasco-Forero, S., Angulo, J., Long Y. "ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results." || ICLR Workshop on Geometrical and Topologic Representation Learning (2021). [Paper] [Code].

 

  • Miolane, N., Guigui, N., Zaatiti, H., Shewmake, C., Hajri, H., Brooks, D., Le Brigant, A., Mathe, J. Hou, B., Thanwerdas, Y., Heyder, S., Peltre, O., Koep, N., Cabanes, Y., Gerald, T. Chauchat, P., Kainz, B., Donnat, C., Holmes, S., Pennec, X. "Introduction to Geometric Learning in Python with Geomstats." || SciPy Conference on Scientific Computing in Python (2020). [Paper] [Code].

 

  • Miolane, N., Guigui, N., Le Brigant, A., Mathe, J., Hou, B., Thanwerdas, Y., Heyder, S., Peltre, O., Koep, N., Cabanes, Y., Chauchat, P., Zaatiti, H., Hajri, H., Gerald, T. , Shewmake, C., Brooks, D., Kainz, B., Donnat, C., Holmes, S., Pennec, X. "Geomstats: A Python Package for Riemannian Geometry in Machine Learning." || Journal of Machine Learning Research (JMLR) (2020). [Paper] [Code].

 

  • Miolane, N., Holmes, S.: "Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders." || CVPR Conference of Computer Vision and Pattern Recognition (2020). [Paper].

 

  • Miolane, N., Poitevin, F., Li, Y.-T., Holmes, S. "Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks." || CVPR Workshop on Computer Vision for Microscopy Imaging (2020). [Paper].

 

  • Donnat, C., Miolane, N., Bunbury, F., Kreindler, J. "A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses." || NeurIPS Workshop on Machine Learning for Health. (2020). [Paper].

 

  • Miolane, N., Devilliers, L., Pennec, X. "Riemannian Geometric Statistics in Medical Imaging. Statistics on Shape Spaces". Chapter: “Bias on Estimation in Quotient Space and Correction Methods“. || Elsevier. (2020). [Book].

 

  • Chang, A. et al. (including Miolane, N.). Intelligence-based Medicine. || Elsevier. (2020). [Book].

 

  • Miolane, N., Poitevin, F., Holmes, S. "Exploring Cryo-EM Latent Space with Variational Autoencoders." || Stanford Bio-X Workshop on Cryo-Electron Microscopy. (2019).

 

  • Poitevin, F., Li, Y.T., Miolane, N., Gati, C., Levitt, M. "Convenience Tools to Explore Variability in Cryo-EM Data." || Stanford Bio-X Workshop on Cryo-Electron Microscopy (2019).

 

  • Koepsell, K., Cadieu, D., Poilvert, N., Hong, H., Cannon, M., Bilenko, N., Romano, N., Mathe, J., Cheng, C., Miolane, N.. "Video Clip Selector for Medical Imaging and Diagnosis." Caption Health || United States Patent and Trademark Office. (2019). [Patent].

 

  • Mathe, J., Miolane, N., Sebastien, N., Lequeux, J. "PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic Power Forecasting from Numerical Weather Prediction." || ICML Workshop on AI for Climate Change. (2019). [Paper].

 

  • Hou, B., Miolane N., Khanal B., Lee M., Alansary A., McDonagh S., Hajnal J., Rueckert D., Glocker B., Kainz B. "Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry." || MICCAI Conference on Medical Image Computing and Computer Assisted Intervention. (2018). [Paper].

 

  • Miolane, N., Holmes, S., Pennec, X.. "Topologically Constrained Template Estimation via Morse-Smale Complices Controls its Statistical Consistency." || SIAM Journal on Applied Algebra and Geometry. (2018). [Paper].

 

  • Miolane, N., Holmes, S., Pennec, X.. "Template Shape Estimation in Computational Anatomy: Correcting an Asymptotic Bias." || SIAM Journal of Imaging Science. (2017). [Paper].

 

  • Miolane, N., Pennec, X., Holmes, S. "Toward a Unified Geometric Bayesian Framework for Template Estimation in Computational Anatomy." || ISBA World Meeting of the International Society for Bayesian Analysis. (2016). (Young Researcher Travel Award).

 

  • Miolane, N., Pennec, X. "Biased Estimators on Quotient spaces." || GSI Conference on Geometric Sciences of Information (2015). (Oral). [Paper] [Video].

 

  • Miolane, N., Pennec, X. "A Survey of Mathematical Structures for Extending 2D Neurogeometry to 3D Image Processing." || MICCAI Workshop of Medical Computer Vision (2015). [Paper] [Code].

 

  • Miolane, N., Pennec, X. "Computing Bi-Invariant Pseudo-Metrics on Lie Groups for Consistent Statistics." || Journal Entropy. (2015). [Paper] [Code].

 

  • Miolane, N.: "Statistics on Lie Groups: Can We Obtain a Consistent Framework with Pseudo-Riemannian Metrics?" || Institut Henri Poincaré Workshop on Geometrical Models in Vision. (2014).

 

  • Miolane, N., Pennec, X. "Statistics on Lie Groups : A Need to Go Beyond the Pseudo-Riemannian Framework." || MaxEnt Workshop on Bayesian Inference and Maximum Entropy Methods (2014). (Oral). [Paper] [Code].

 

  • Miolane, N., Khanal, B.: "Statistics on Lie Groups for Computational Anatomy." Video for the 2014 Educational Challenge at MIT || MICCAI Conference on Medical Image Computing and Computer Assisted Intervention. (2014). (1st Prize). [Video].

 

  • Darmante, H., Bugnas, B., Dompsure, R.B.D., Barresi, L., Miolane, N., Pennec, X., de Peretti, F., Bronsard, N.. "Analyse Biométrique de l'Anneau Pelvien en 3 Dimensions – A Propos de 100 Scanners." || Journal Revue de Chirurgie Orthopédique et Traumatologique. (2014). [Paper].