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2020 – today
- 2024
[c24]Adele Myers, Nina Miolane:
On Accuracy and Speed of Geodesic Regression: Do Geometric Priors Improve Learning on Small Datasets? CVPR Workshops 2024: 2714-2722- [c23]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Lio, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position: Topological Deep Learning is the New Frontier for Relational Learning. ICML 2024 - [c22]Francisco Acosta, Fatih Dinc, William Redman, Manu S. Madhav, David A. Klindt, Nina Miolane:
Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems. NeurIPS 2024 - [c21]Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane:
The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks. NeurIPS 2024 - [c20]William Redman, Francisco Acosta, Santiago Acosta-Mendoza, Nina Miolane:
Not so griddy: Internal representations of RNNs path integrating more than one agent. NeurIPS 2024 - [i36]Mustafa Hajij, Mathilde Papillon, Florian Frantzen
, Jens Agerberg, Ibrahem AlJabea, Rubén Ballester, Claudio Battiloro, Guillermo Bernárdez, Tolga Birdal, Aiden Brent, Peter Chin, Sergio Escalera, Simone Fiorellino, Odin Hoff Gardaa, Gurusankar Gopalakrishnan, Devendra Govil, Josef Hoppe, Maneel Reddy Karri, Jude Khouja, Manuel Lecha, Neal Livesay, Jan Meißner, Soham Mukherjee, Alexander Nikitin, Theodore Papamarkou, Jaro Prílepok, Karthikeyan Natesan Ramamurthy, Paul Rosen, Aldo Guzmán-Sáenz, Alessandro Salatiello, Shreyas N. Samaga, Simone Scardapane, Michael T. Schaub, Luca Scofano, Indro Spinelli, Lev Telyatnikov, Quang Truong, Robin Walters, Maosheng Yang, Olga Zaghen, Ghada Zamzmi, Ali Zia, Nina Miolane:
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. CoRR abs/2402.02441 (2024) - [i35]Theodore Papamarkou, Tolga Birdal, Michael M. Bronstein, Gunnar E. Carlsson, Justin Curry, Yue Gao, Mustafa Hajij, Roland Kwitt, Pietro Liò, Paolo Di Lorenzo, Vasileios Maroulas, Nina Miolane, Farzana Nasrin, Karthikeyan Natesan Ramamurthy, Bastian Rieck, Simone Scardapane, Michael T. Schaub, Petar Velickovic, Bei Wang, Yusu Wang, Guo-Wei Wei, Ghada Zamzmi:
Position Paper: Challenges and Opportunities in Topological Deep Learning. CoRR abs/2402.08871 (2024) - [i34]Simon Mataigne, Ralf Zimmermann, Nina Miolane:
An efficient algorithm for the Riemannian logarithm on the Stiefel manifold for a family of Riemannian metrics. CoRR abs/2403.11730 (2024) - [i33]Rubén Ballester, Pablo Hernández-García, Mathilde Papillon, Claudio Battiloro, Nina Miolane, Tolga Birdal, Carles Casacuberta, Sergio Escalera, Mustafa Hajij:
Attending to Topological Spaces: The Cellular Transformer. CoRR abs/2405.14094 (2024) - [i32]Lev Telyatnikov, Guillermo Bernárdez, Marco Montagna, Pavlo Vasylenko, Ghada Zamzmi, Mustafa Hajij, Michael T. Schaub, Nina Miolane, Simone Scardapane, Theodore Papamarkou:
TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning. CoRR abs/2406.06642 (2024) - [i31]Luís F. Pereira, Alice Le Brigant, Adele Myers, Emmanuel Hartman, Amil Khan, Malik Tuerkoen, Trey Dold, Mengyang Gu, Pablo Suárez-Serrato, Nina Miolane:
Learning from landmarks, curves, surfaces, and shapes in Geomstats. CoRR abs/2406.10437 (2024) - [i30]Simon Mataigne, Johan Mathe, Sophia Sanborn, Christopher Hillar, Nina Miolane:
The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks. CoRR abs/2407.07655 (2024) - [i29]Sophia Sanborn, Johan Mathe, Mathilde Papillon, Domas Buracas, Hansen Lillemark, Christian Shewmake, Abby Bertics, Xavier Pennec, Nina Miolane:
Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures. CoRR abs/2407.09468 (2024) - [i28]Simon Mataigne, Pierre-Antoine Absil, Nina Miolane:
Bounds on the geodesic distances on the Stiefel manifold for a family of Riemannian metrics. CoRR abs/2408.07072 (2024) - [i27]Guillermo Bernárdez, Lev Telyatnikov, Marco Montagna, Federica Baccini, Mathilde Papillon, Miquel Ferriol Galmés, Mustafa Hajij, Theodore Papamarkou, Maria Sofia Bucarelli, Olga Zaghen, Johan Mathe, Audun Myers, Scott Mahan, Hansen Lillemark, Sharvaree P. Vadgama, Erik J. Bekkers, Tim Doster, Tegan Emerson, Henry Kvinge, Katrina Agate, Nesreen K. Ahmed, Pengfei Bai, Michael Banf, Claudio Battiloro, Maxim Beketov, Paul Bogdan, Martin Carrasco, Andrea Cavallo, Yun Young Choi, George Dasoulas, Matous Elphick, Giordan Escalona, Dominik Filipiak, Halley Fritze, Thomas Gebhart, Manel Gil-Sorribes, Salvish Goomanee, Victor Guallar, Liliya Imasheva, Andrei Irimia, Hongwei Jin, Graham Johnson, Nikos Kanakaris, Boshko Koloski, Veljko Kovac, Manuel Lecha, Minho Lee, Pierrick Leroy, Theodore Long, German Magai, Alvaro Martinez, Marissa Masden, Sebastian Meznar, Bertran Miquel-Oliver, Alexis Molina, Alexander Nikitin, Marco Nurisso, Matt Piekenbrock, Yu Qin, Patryk Rygiel, Alessandro Salatiello, Max Schattauer, Pavel Snopov, Julian Suk, Valentina Sánchez, Mauricio Tec, Francesco Vaccarino, Jonas Verhellen, Frédéric Wantiez, Alexander Weers, Patrik Zajec, Blaz Skrlj, Nina Miolane:
ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. CoRR abs/2409.05211 (2024) - [i26]Mathilde Papillon, Guillermo Bernárdez, Claudio Battiloro, Nina Miolane:
TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks. CoRR abs/2410.06530 (2024) - 2023
- [j7]Wanxin Li, Jules Mirone, Ashok Prasad, Nina Miolane, Carine Legrand, Khanh Dao Duc:
Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets. Frontiers Bioinform. 3 (2023) - [j6]Nicolas Guigui, Nina Miolane, Xavier Pennec:
Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats. Found. Trends Mach. Learn. 16(3): 329-493 (2023) - [j5]Saiteja Utpala, Praneeth Vepakomma, Nina Miolane:
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. Trans. Mach. Learn. Res. 2023 (2023) - [j4]Alice Le Brigant, Jules Deschamps, Antoine Collas, Nina Miolane:
Parametric Information Geometry with the Package Geomstats. ACM Trans. Math. Softw. 49(4): 34:1-34:26 (2023) - [c19]Francisco Acosta, Sophia Sanborn, Khanh Dao Duc, Manu S. Madhav, Nina Miolane:
Quantifying Extrinsic Curvature in Neural Manifolds. CVPR Workshops 2023: 610-619 - [c18]Christian Shewmake, Nina Miolane, Bruno A. Olshausen:
Group Equivariant Sparse Coding. GSI (1) 2023: 91-101 - [c17]Adele Myers, Caitlin M. Taylor, Emily G. Jacobs, Nina Miolane:
Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation. ICCV (Workshops) 2023: 2534-2543 - [c16]Sophia Sanborn, Nina Miolane:
A General Framework for Robust G-Invariance in G-Equivariant Networks. NeurIPS 2023 - [c15]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Preface. TAG-ML 2023: 1-2 - [c14]Mathilde Papillon, Mustafa Hajij, Audun Myers, Florian Frantzen, Ghada Zamzmi, Helen Jenne, Johan Mathe, Josef Hoppe, Michael T. Schaub, Theodore Papamarkou, Aldo Guzmán-Sáenz, Bastian Rieck, Neal Livesay, Tamal K. Dey, Abraham Rabinowitz, Aiden Brent, Alessandro Salatiello, Alexander Nikitin, Ali Zia, Claudio Battiloro, Dmitrii Gavrilev, Georg Bökman, German Magai, Gleb Bazhenov, Guillermo Bernárdez, Indro Spinelli, Jens Agerberg, Kalyan Varma Nadimpalli, Lev Telyatnikov, Luca Scofano, Lucia Testa, Manuel Lecha, Maosheng Yang, Mohammed Hassanin, Odin Hoff Gardaa, Olga Zaghen, Paul Häusner, Paul Snopoff, Pavlo Melnyk, Rubén Ballester, Sadrodin Barikbin, Sergio Escalera, Simone Fiorellino, Henry Kvinge, Jan Meissner, Karthikeyan Natesan Ramamurthy, Michael Scholkemper, Paul Rosen, Robin Walters, Shreyas N. Samaga, Soham Mukherjee, Sophia Sanborn, Tegan Emerson, Timothy Doster, Tolga Birdal, Vincent P. Grande, Abdelwahed Khamis, Simone Scardapane, Suraj Singh, Tatiana Malygina, Yixiao Yue, Nina Miolane:
ICML 2023 Topological Deep Learning Challenge: Design and Results. TAG-ML 2023: 3-8 - [e3]Timothy Doster, Tegan Emerson, Henry Kvinge, Nina Miolane, Mathilde Papillon, Bastian Rieck, Sophia Sanborn:
Topological, Algebraic and Geometric Learning Workshops 2023, 28 July 2023, Honolulu, HI, USA. Proceedings of Machine Learning Research 221, PMLR 2023 [contents] - [i25]Mathilde Papillon, Sophia Sanborn, Mustafa Hajij, Nina Miolane:
Architectures of Topological Deep Learning: A Survey on Topological Neural Networks. CoRR abs/2304.10031 (2023) - [i24]Bongjin Koo, Julien N. P. Martel, Ariana Peck, Axel Levy, Frédéric Poitevin, Nina Miolane:
Reconstructing Heterogeneous Cryo-EM Molecular Structures by Decomposing Them into Polymer Chains. CoRR abs/2306.07274 (2023) - [i23]Adele Myers, Caitlin M. Taylor, Emily G. Jacobs, Nina Miolane:
Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation. CoRR abs/2309.16662 (2023) - [i22]David A. Klindt, Sophia Sanborn, Francisco Acosta, Frédéric Poitevin, Nina Miolane:
Identifying Interpretable Visual Features in Artificial and Biological Neural Systems. CoRR abs/2310.11431 (2023) - [i21]Sophia Sanborn, Nina Miolane:
A General Framework for Robust G-Invariance in G-Equivariant Networks. CoRR abs/2310.18564 (2023) - 2022
- [c13]Mathilde Papillon, Mariel Pettee, Nina Miolane:
PirouNet: Creating Dance Through Artist-Centric Deep Learning. ArtsIT 2022: 447-465 - [c12]Axel Levy, Frédéric Poitevin, Julien N. P. Martel, Youssef S. G. Nashed, Ariana Peck, Nina Miolane, Daniel Ratner, Mike Dunne, Gordon Wetzstein:
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images. ECCV (21) 2022: 540-557 - [c11]Sophia Sanborn, Christian Shewmake, Simone Azeglio, Arianna Di Bernardo, Nina Miolane:
Preface. NeurReps 2022: i-vi - [c10]Alexander Cloninger, Timothy Doster, Tegan Emerson, Manohar Kaul, Ira Ktena, Henry Kvinge, Nina Miolane, Bastian Rice, Sarah Tymochko, Guy Wolf:
Preface. TAG-ML 2022: 1-5 - [c9]Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David A. Klindt, Erik Hermansen, Melvin Vaupel, Benjamin A. Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz, Nina Miolane:
ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results. TAG-ML 2022: 269-276 - [e2]Sophia Sanborn, Christian Shewmake, Simone Azeglio, Arianna Di Bernardo, Nina Miolane:
NeurIPS Workshop on Symmetry and Geometry in Neural Representations, 03 December 2022, New Orleans, Lousiana, USA. Proceedings of Machine Learning Research 197, PMLR 2022 [contents] - [e1]Alexander Cloninger, Timothy Doster, Tegan Emerson, Manohar Kaul, Ira Ktena, Henry Kvinge, Nina Miolane, Bastian Rice, Sarah Tymochko, Guy Wolf:
Topological, Algebraic and Geometric Learning Workshops 2022, 25-22 July 2022, Virtual. Proceedings of Machine Learning Research 196, PMLR 2022 [contents] - [i20]Claire Donnat, Axel Levy, Frédéric Poitevin, Nina Miolane:
Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy. CoRR abs/2201.02867 (2022) - [i19]Axel Levy, Frédéric Poitevin, Julien N. P. Martel, Youssef S. G. Nashed, Ariana Peck, Nina Miolane, Daniel Ratner, Mike Dunne, Gordon Wetzstein:
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images. CoRR abs/2203.08138 (2022) - [i18]Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou, Nina Miolane, Aldo Guzmán-Sáenz, Karthikeyan Natesan Ramamurthy:
Higher-Order Attention Networks. CoRR abs/2206.00606 (2022) - [i17]Adele Myers, Saiteja Utpala, Shubham Talbar, Sophia Sanborn, Christian Shewmake, Claire Donnat, Johan Mathe, Umberto Lupo, Rishi Sonthalia, Xinyue Cui, Tom Szwagier, Arthur Pignet, Andri Bergsson, Søren Hauberg, Dmitriy Nielsen, Stefan Sommer, David A. Klindt, Erik Hermansen, Melvin Vaupel, Benjamin A. Dunn, Jeffrey Xiong, Noga Aharony, Itsik Pe'er, Felix Ambellan, Martin Hanik, Esfandiar Nava-Yazdani, Christoph von Tycowicz, Nina Miolane:
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results. CoRR abs/2206.09048 (2022) - [i16]Nicolas Legendre, Khanh Dao Duc, Nina Miolane:
Defining an action of SO(d)-rotations on images generated by projections of d-dimensional objects: Applications to pose inference with Geometric VAEs. CoRR abs/2207.11582 (2022) - [i15]Mathilde Papillon, Mariel Pettee, Nina Miolane:
PirouNet: Creating Intentional Dance with Semi-Supervised Conditional Recurrent Variational Autoencoders. CoRR abs/2207.12126 (2022) - [i14]Mathilde Papillon, Mariel Pettee, Nina Miolane:
Intentional Choreography with Semi-Supervised Recurrent VAEs. CoRR abs/2209.10010 (2022) - [i13]Youssef S. G. Nashed, Ariana Peck, Julien N. P. Martel, Axel Levy, Bongjin Koo, Gordon Wetzstein, Nina Miolane, Daniel Ratner, Frédéric Poitevin:
Heterogeneous reconstruction of deformable atomic models in Cryo-EM. CoRR abs/2209.15121 (2022) - [i12]Adele Myers, Nina Miolane:
Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves. CoRR abs/2210.01932 (2022) - [i11]Thibault Niederhauser, Adam Lester, Nina Miolane, Khanh Dao Duc, Manu S. Madhav:
Testing geometric representation hypotheses from simulated place cell recordings. CoRR abs/2211.09096 (2022) - [i10]Alice Le Brigant, Jules Deschamps, Antoine Collas, Nina Miolane:
Parametric information geometry with the package Geomstats. CoRR abs/2211.11643 (2022) - 2021
- [c8]Wanxin Li, Ashok Prasad, Nina Miolane, Khanh Dao Duc:
Using a Riemannian Elastic Metric for Statistical Analysis of Tumor Cell Shape Heterogeneity. GSI (2) 2021: 583-592 - [i9]Nina Miolane, Matteo Caorsi, Umberto Lupo, Marius Guerard, Nicolas Guigui, Johan Mathe, Yann Cabanes, Wojciech Reise, Thomas Davies, António Leitão, Somesh Mohapatra, Saiteja Utpala, Shailja Shailja, Gabriele Corso, Guoxi Liu, Federico Iuricich, Andrei Manolache, Mihaela Nistor, Matei Bejan, Armand Mihai Nicolicioiu, Bogdan-Alexandru Luchian, Mihai-Sorin Stupariu, Florent Michel, Khanh Dao Duc, Bilal Abdulrahman, Maxim Beketov, Elodie Maignant, Zhiyuan Liu, Marek Cerný, Martin Bauw, Santiago Velasco-Forero, Jesús Angulo, Yanan Long:
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results. CoRR abs/2108.09810 (2021) - [i8]Faria Huq, Adrish Dey, Sahra Yusuf, Dena Bazazian, Tolga Birdal, Nina Miolane:
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks. CoRR abs/2111.14762 (2021) - 2020
- [c7]Nina Miolane, Frédéric Poitevin, Yee-Ting Li, Susan P. Holmes:
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks. CVPR Workshops 2020: 4174-4183 - [c6]Nina Miolane, Susan P. Holmes:
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders. CVPR 2020: 14491-14499 - [c5]Claire Donnat, Nina Miolane, Freddy Bunbury, Jack Kreindler:
A {B. ML4H@NeurIPS 2020: 53-84 - [c4]Nina Miolane, Nicolas Guigui, Hadi Zaatiti, Christian Shewmake, Hatem Hajri, Daniel Brooks, Alice Le Brigant, Johan Mathe, Benjamin Hou, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Yann Cabanes, Thomas Gerald, Paul Chauchat, Bernhard Kainz, Claire Donnat, Susan P. Holmes, Xavier Pennec:
Introduction to Geometric Learning in Python with Geomstats. SciPy 2020: 48-57 - [i7]Nina Miolane, Alice Le Brigant, Johan Mathe, Benjamin Hou, Nicolas Guigui, Yann Thanwerdas, Stefan Heyder, Olivier Peltre, Niklas Koep, Hadi Zaatiti, Hatem Hajri, Yann Cabanes, Thomas Gerald, Paul Chauchat, Christian Shewmake, Bernhard Kainz, Claire Donnat, Susan P. Holmes, Xavier Pennec:
Geomstats: A Python Package for Riemannian Geometry in Machine Learning. CoRR abs/2004.04667 (2020)
2010 – 2019
- 2019
- [i6]Johan Mathe, Nina Miolane, Nicolas Sébastien, Jeremie Lequeux:
PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction. CoRR abs/1902.01453 (2019) - [i5]Nina Miolane, Frédéric Poitevin, Yee-Ting Li, Susan P. Holmes:
Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks. CoRR abs/1911.08121 (2019) - [i4]Nina Miolane, Susan P. Holmes:
Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders. CoRR abs/1911.08147 (2019) - 2018
- [j3]Nina Miolane, Susan P. Holmes, Xavier Pennec:
Topologically Constrained Template Estimation via Morse-Smale Complexes Controls Its Statistical Consistency. SIAM J. Appl. Algebra Geom. 2(2): 348-375 (2018) - [c3]Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew C. H. Lee, Amir Alansary, Steven G. McDonagh, Joseph V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz:
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry. MICCAI (1) 2018: 756-764 - [i3]Benjamin Hou, Nina Miolane, Bishesh Khanal, Matthew C. H. Lee, Amir Alansary, Steven G. McDonagh, Joseph V. Hajnal, Daniel Rueckert, Ben Glocker, Bernhard Kainz:
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry. CoRR abs/1805.01026 (2018) - [i2]Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec:
geomstats: a Python Package for Riemannian Geometry in Machine Learning. CoRR abs/1805.08308 (2018) - 2017
- [j2]Nina Miolane, Susan P. Holmes, Xavier Pennec:
Template Shape Estimation: Correcting an Asymptotic Bias. SIAM J. Imaging Sci. 10(2): 808-844 (2017) - 2016
- [b1]Nina Miolane:
Geometric Statistics for Computational Anatomy. (Les statistiques géométriques pour l'anatomie numérique). University of Côte d'Azur, France, 2016 - [i1]Nina Miolane, Susan P. Holmes, Xavier Pennec:
Template shape estimation: correcting an asymptotic bias. CoRR abs/1610.01502 (2016) - 2015
- [j1]Nina Miolane, Xavier Pennec:
Computing Bi-Invariant Pseudo-Metrics on Lie Groups for Consistent Statistics. Entropy 17(4): 1850-1881 (2015) - [c2]Nina Miolane, Xavier Pennec:
Biased Estimators on Quotient Spaces. GSI 2015: 130-139 - [c1]Nina Miolane, Xavier Pennec:
A Survey of Mathematical Structures for Extending 2D Neurogeometry to 3D Image Processing. MCV@MICCAI 2015: 155-167
Coauthor Index
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