Biased Estimators on Quotient Spaces. - Nina Miolane; Xavier Pennec (GSI, 2015)

A Survey of Mathematical Structures for Extending 2D Neurogeometry to 3D Image Processing. - Nina Miolane; Xavier Pennec (MCV@MICCAI, 2015)

Computing Bi-Invariant Pseudo-Metrics on Lie Groups for Consistent Statistics. - Nina Miolane; Xavier Pennec (Entropy, 2015)

Geometric Statistics for Computational Anatomy. (Les statistiques géométriques pour l'anatomie numérique). - Nina Miolane (2016)

Template Shape Estimation: Correcting an Asymptotic Bias. - Nina Miolane; Susan P. Holmes; Xavier Pennec (SIAM J. Imaging Sci., 2017)

Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry. - Benjamin Hou; Nina Miolane; Bishesh Khanal; Matthew C. H. Lee; Amir Alansary; Steven McDonagh 001; Joseph V. Hajnal; Daniel Rueckert; Ben Glocker; Bernhard Kainz (MICCAI (1), 2018)

geomstats: a Python Package for Riemannian Geometry in Machine Learning. - Nina Miolane; Johan Mathe; Claire Donnat; Mikael Jorda; Xavier Pennec (CoRR, 2018)

Topologically Constrained Template Estimation via Morse-Smale Complexes Controls Its Statistical Consistency. - Nina Miolane; Susan P. Holmes; Xavier Pennec (SIAM J. Appl. Algebra Geom., 2018)

PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction. - Johan Mathe; Nina Miolane; Nicolas Sébastien; Jeremie Lequeux (CoRR, 2019)

Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders. - Nina Miolane; Susan P. Holmes (CVPR, 2020)

Estimation of Orientation and Camera Parameters from Cryo-Electron Microscopy Images with Variational Autoencoders and Generative Adversarial Networks. - Nina Miolane; Frédéric Poitevin; Yee-Ting Li; Susan P. Holmes (CVPR Workshops, 2020)

A {B. - Claire Donnat; Nina Miolane; Freddy Bunbury; Jack Kreindler (ML4H@NeurIPS, 2020)

Introduction to Geometric Learning in Python with Geomstats. - 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; et al. (SciPy, 2020)

Geomstats: A Python Package for Riemannian Geometry in Machine Learning. - 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; et al. (CoRR, 2020)

Using a Riemannian Elastic Metric for Statistical Analysis of Tumor Cell Shape Heterogeneity. - Wanxin Li; Ashok Prasad; Nina Miolane; Khanh Dao Duc (GSI (2), 2021)

ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results. - Nina Miolane; Matteo Caorsi; Umberto Lupo; Marius Guerard; Nicolas Guigui; Johan Mathe; Yann Cabanes; Wojciech Reise; Thomas Davies 001; António Leitão; Somesh Mohapatra; Saiteja Utpala; et al. (CoRR, 2021)

Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks. - Faria Huq; Adrish Dey; Sahra Yusuf; Dena Bazazian; Tolga Birdal; Nina Miolane (CoRR, 2021)

PirouNet: Creating Dance Through Artist-Centric Deep Learning. - Mathilde Papillon; Mariel Pettee; Nina Miolane (ArtsIT, 2022)

CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images. - Axel Levy; Frédéric Poitevin; Julien N. P. Martel; Youssef S. G. Nashed; Ariana Peck; Nina Miolane; Daniel Ratner; Mike Dunne; Gordon Wetzstein (ECCV (21), 2022)

Preface. - Sophia Sanborn; Christian Shewmake; Simone Azeglio; Arianna Di Bernardo; Nina Miolane (NeurReps, 2022)

Preface. - Alexander Cloninger; Timothy Doster; Tegan Emerson; Manohar Kaul; Ira Ktena; Henry Kvinge; Nina Miolane; Bastian Rice; Sarah Tymochko; Guy Wolf (TAG-ML, 2022)

ICLR 2022 Challenge for Computational Geometry & Topology: Design and Results. - Adele Myers; Saiteja Utpala; Shubham Talbar; Sophia Sanborn; Christian Shewmake; Claire Donnat; Johan Mathe; Rishi Sonthalia; Xinyue Cui; Tom Szwagier; Arthur Pignet; Andri Bergsson; et al. (TAG-ML, 2022)

Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy. - Claire Donnat; Axel Levy; Frédéric Poitevin; Nina Miolane (CoRR, 2022)

Higher-Order Attention Networks. - Mustafa Hajij; Ghada Zamzmi; Theodore Papamarkou; Nina Miolane; Aldo Guzmán-Sáenz; Karthikeyan Natesan Ramamurthy (CoRR, 2022)

ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results. - Adele Myers; Saiteja Utpala; Shubham Talbar; Sophia Sanborn; Christian Shewmake; Claire Donnat; Johan Mathe; Umberto Lupo; Rishi Sonthalia; Xinyue Cui; Tom Szwagier; Arthur Pignet; et al. (CoRR, 2022)

Defining an action of SO(d)-rotations on images generated by projections of d-dimensional objects: Applications to pose inference with Geometric VAEs. - Nicolas Legendre; Khanh Dao Duc; Nina Miolane (CoRR, 2022)

PirouNet: Creating Intentional Dance with Semi-Supervised Conditional Recurrent Variational Autoencoders. - Mathilde Papillon; Mariel Pettee; Nina Miolane (CoRR, 2022)

Intentional Choreography with Semi-Supervised Recurrent VAEs. - Mathilde Papillon; Mariel Pettee; Nina Miolane (CoRR, 2022)

Heterogeneous reconstruction of deformable atomic models in Cryo-EM. - Youssef S. G. Nashed; Ariana Peck; Julien N. P. Martel; Axel Levy; Bongjin Koo; Gordon Wetzstein; Nina Miolane; Daniel Ratner; Frédéric Poitevin (CoRR, 2022)

Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves. - Adele Myers; Nina Miolane (CoRR, 2022)

Testing geometric representation hypotheses from simulated place cell recordings. - Thibault Niederhauser; Adam Lester; Nina Miolane; Khanh Dao Duc; Manu S. Madhav (CoRR, 2022)

Quantifying Extrinsic Curvature in Neural Manifolds. - Francisco Acosta; Sophia Sanborn; Khanh Dao Duc; Manu S. Madhav; Nina Miolane (CVPR Workshops, 2023)

Group Equivariant Sparse Coding. - Christian Shewmake; Nina Miolane; Bruno A. Olshausen (GSI (1), 2023)

Geodesic Regression Characterizes 3D Shape Changes in the Female Brain During Menstruation. - Adele Myers; Caitlin M. Taylor; Emily G. Jacobs; Nina Miolane (ICCV (Workshops), 2023)

Preface. - Sophia Sanborn; Christian Shewmake; Simone Azeglio; Nina Miolane (NeurReps, 2023)

A General Framework for Robust G-Invariance in G-Equivariant Networks. - Sophia Sanborn; Nina Miolane (NeurIPS, 2023)

Preface. - Timothy Doster; Tegan Emerson; Henry Kvinge; Nina Miolane; Mathilde Papillon; Bastian Rieck; Sophia Sanborn (TAG-ML, 2023)

ICML 2023 Topological Deep Learning Challenge: Design and Results. - 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; et al. (TAG-ML, 2023)

Architectures of Topological Deep Learning: A Survey on Topological Neural Networks. - Mathilde Papillon; Sophia Sanborn; Mustafa Hajij; Nina Miolane (CoRR, 2023)

Reconstructing Heterogeneous Cryo-EM Molecular Structures by Decomposing Them into Polymer Chains. - Bongjin Koo; Julien N. P. Martel; Ariana Peck; Axel Levy; Frédéric Poitevin; Nina Miolane (CoRR, 2023)

Identifying Interpretable Visual Features in Artificial and Biological Neural Systems. - David A. Klindt; Sophia Sanborn; Francisco Acosta; Frédéric Poitevin; Nina Miolane (CoRR, 2023)

Introduction to Riemannian Geometry and Geometric Statistics: From Basic Theory to Implementation with Geomstats. - Nicolas Guigui; Nina Miolane; Xavier Pennec (Found. Trends Mach. Learn., 2023)

Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric. - Saiteja Utpala; Praneeth Vepakomma; Nina Miolane (Trans. Mach. Learn. Res., 2023)

Orthogonal outlier detection and dimension estimation for improved MDS embedding of biological datasets. - Wanxin Li; Jules Mirone; Ashok Prasad; Nina Miolane; Carine Legrand; Khanh Dao Duc (Frontiers Bioinform., 2023)

Parametric Information Geometry with the Package Geomstats. - Alice Le Brigant; Jules Deschamps; Antoine Collas; Nina Miolane (ACM Trans. Math. Softw., 2023)

On Accuracy and Speed of Geodesic Regression: Do Geometric Priors Improve Learning on Small Datasets? - Adele Myers; Nina Miolane (CVPR Workshops, 2024)

Position: Topological Deep Learning is the New Frontier for Relational Learning. - 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; et al. (ICML, 2024)

Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems. - Francisco Acosta; Fatih Dinc; William Redman; Manu S. Madhav; David A. Klindt; Nina Miolane (NeurIPS, 2024)

The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks. - Simon Mataigne; Johan Mathe; Sophia Sanborn; Christopher Hillar; Nina Miolane (NeurIPS, 2024)

Not so griddy: Internal representations of RNNs path integrating more than one agent. - William Redman; Francisco Acosta; Santiago Acosta-Mendoza; Nina Miolane (NeurIPS, 2024)

TopoX: A Suite of Python Packages for Machine Learning on Topological Domains. - Mustafa Hajij; Mathilde Papillon; Florian Frantzen; Jens Agerberg; Ibrahem AlJabea; Rubén Ballester; Claudio Battiloro; Guillermo Bernárdez; Tolga Birdal; Aiden Brent; Peter Chin 001; Sergio Escalera; et al. (CoRR, 2024)

Position Paper: Challenges and Opportunities in Topological Deep Learning. - 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; et al. (CoRR, 2024)

Attending to Topological Spaces: The Cellular Transformer. - Rubén Ballester; Pablo Hernández-García; Mathilde Papillon; Claudio Battiloro; Nina Miolane; Tolga Birdal; Carles Casacuberta; Sergio Escalera; Mustafa Hajij (CoRR, 2024)

TopoBenchmarkX: A Framework for Benchmarking Topological Deep Learning. - Lev Telyatnikov; Guillermo Bernárdez; Marco Montagna; Pavlo Vasylenko; Ghada Zamzmi; Mustafa Hajij; Michael T. Schaub; Nina Miolane; Simone Scardapane; Theodore Papamarkou (CoRR, 2024)

Learning from landmarks, curves, surfaces, and shapes in Geomstats. - Luís F. Pereira; Alice Le Brigant; Adele Myers; Emmanuel Hartman; Amil Khan; Malik Tuerkoen; Trey Dold; Mengyang Gu; Pablo Suárez-Serrato; Nina Miolane (CoRR, 2024)

Beyond Euclid: An Illustrated Guide to Modern Machine Learning with Geometric, Topological, and Algebraic Structures. - Sophia Sanborn; Johan Mathe; Mathilde Papillon; Domas Buracas; Hansen Lillemark; Christian Shewmake; Abby Bertics; Xavier Pennec; Nina Miolane (CoRR, 2024)

Bounds on the geodesic distances on the Stiefel manifold for a family of Riemannian metrics. - Simon Mataigne; Pierre-Antoine Absil; Nina Miolane (CoRR, 2024)

ICML Topological Deep Learning Challenge 2024: Beyond the Graph Domain. - 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; et al. (CoRR, 2024)

TopoTune : A Framework for Generalized Combinatorial Complex Neural Networks. - Mathilde Papillon; Guillermo Bernárdez; Claudio Battiloro; Nina Miolane (CoRR, 2024)

Learning Riemannian Metrics for Interpolating Animations. - Sarah Kushner; Vismay Modi; Nina Miolane (GSI (1), 2025)

On the Approximation of the Riemannian Barycenter. - Simon Mataigne; Pierre-Antoine Absil; Nina Miolane (GSI (2), 2025)

An Efficient Algorithm for the Riemannian Logarithm on the Stiefel Manifold for a Family of Riemannian Metrics. - Simon Mataigne; Ralf Zimmermann 002; Nina Miolane (SIAM J. Matrix Anal. Appl., 2025)

When Machine Learning Gets Personal: Understanding Fairness of Personalized Models. - Louisa Cornelis; Guillermo Bernárdez; Haewon Jeong; Nina Miolane (CoRR, 2025)

From superposition to sparse codes: interpretable representations in neural networks. - David A. Klindt; Charles O'Neill; Patrik Reizinger; Harald Maurer; Nina Miolane (CoRR, 2025)

Ordered Topological Deep Learning: a Network Modeling Case Study. - Guillermo Bernárdez; Miquel Ferriol Galmés; Carlos Güemes-Palau; Mathilde Papillon; Pere Barlet-Ros; Albert Cabellos-Aparicio; Nina Miolane (CoRR, 2025)

HOPSE: Scalable Higher-Order Positional and Structural Encoder for Combinatorial Representations. - Martin Carrasco; Guillermo Bernárdez; Marco Montagna; Nina Miolane; Lev Telyatnikov (CoRR, 2025)

Unsupervised cell segmentation by fast Gaussian Processes. - Laura Baracaldo; Blythe King; Haoran Yan; Yizi Lin; Nina Miolane; Mengyang Gu (CoRR, 2025)

Alternating Gradient Flows: A Theory of Feature Learning in Two-layer Neural Networks. - Daniel Kunin; Giovanni Luca Marchetti; Feng Chen; Dhruva Karkada; James B. Simon; Michael Robert DeWeese; Surya Ganguli; Nina Miolane (CoRR, 2025)

GraphUniverse: Enabling Systematic Evaluation of Inductive Generalization. - Louis Van Langendonck; Guillermo Bernárdez; Nina Miolane; Pere Barlet-Ros (CoRR, 2025)