27 citations · 81 across the 22 of their papers we have counts for
24 papers
Architectures of Topological Deep Learning: A Survey of Message-Passing Topological Neural Networks
Mathilde Papillon, Sophia Sanborn, Mustafa Hajij +1
The natural world is full of complex systems characterized by intricate relations between their components: from social interactions between individuals in a social network to elec…
Equivariant Representation Learning in the Presence of Stabilizers
Luis Armando Pérez Rey, Giovanni Luca Marchetti, Danica Kragic +2
We introduce Equivariant Isomorphic Networks (EquIN) -- a method for learning representations that are equivariant with respect to general group actions over data. Differently from…
Quantifying Extrinsic Curvature in Neural Manifolds
Francisco Acosta, Sophia Sanborn, Khanh Dao Duc +2
The neural manifold hypothesis postulates that the activity of a neural population forms a low-dimensional manifold whose structure reflects that of the encoded task variables. In…
Regression-Based Elastic Metric Learning on Shape Spaces of Elastic Curves
Adele Myers, Nina Miolane
We propose a metric learning paradigm, Regression-based Elastic Metric Learning (REML), which optimizes the elastic metric for geodesic regression on the manifold of discrete curve…
Parametric information geometry with the package Geomstats
Alice Le Brigant, Jules Deschamps, Antoine Collas +1
We introduce the information geometry module of the Python package Geomstats. The module first implements Fisher-Rao Riemannian manifolds of widely used parametric families of prob…
Testing geometric representation hypotheses from simulated place cell recordings
Thibault Niederhauser, Adam Lester, Nina Miolane +2
Hippocampal place cells can encode spatial locations of an animal in physical or task-relevant spaces. We simulated place cell populations that encoded either Euclidean- or graph-b…
Heterogeneous reconstruction of deformable atomic models in Cryo-EM
Youssef Nashed, Ariana Peck, Julien Martel +6
Cryogenic electron microscopy (cryo-EM) provides a unique opportunity to study the structural heterogeneity of biomolecules. Being able to explain this heterogeneity with atomic mo…
Intentional Choreography with Semi-Supervised Recurrent VAEs
Mathilde Papillon, Mariel Pettee, Nina Miolane
We summarize the model and results of PirouNet, a semi-supervised recurrent variational autoencoder. Given a small amount of dance sequences labeled with qualitative choreographic…
Differentially Private Fréchet Mean on the Manifold of Symmetric Positive Definite (SPD) Matrices with log-Euclidean Metric
Saiteja Utpala, Praneeth Vepakomma, Nina Miolane
Differential privacy has become crucial in the real-world deployment of statistical and machine learning algorithms with rigorous privacy guarantees. The earliest statistical queri…
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
Recent advances in variational autoencoders (VAEs) have enabled learning latent manifolds as compact Lie groups, such as . Since this approach assumes that data lies on a su…
PirouNet: Creating Dance through Artist-Centric Deep Learning
Mathilde Papillon, Mariel Pettee, Nina Miolane
Using Artificial Intelligence (AI) to create dance choreography with intention is still at an early stage. Methods that conditionally generate dance sequences remain limited in the…
ICLR 2022 Challenge for Computational Geometry and Topology: Design and Results
Adele Myers, Saiteja Utpala, Shubham Talbar +25
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learn…
Topological Deep Learning: Going Beyond Graph Data
Mustafa Hajij, Ghada Zamzmi, Theodore Papamarkou +11
Topological deep learning is a rapidly growing field that pertains to the development of deep learning models for data supported on topological domains such as simplicial complexes…
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images
Axel Levy, Frédéric Poitevin, Julien Martel +6
Cryo-electron microscopy (cryo-EM) has become a tool of fundamental importance in structural biology, helping us understand the basic building blocks of life. The algorithmic chall…
Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy
Claire Donnat, Axel Levy, Frederic Poitevin +2
Recent breakthroughs in high-resolution imaging of biomolecules in solution with cryo-electron microscopy (cryo-EM) have unlocked new doors for the reconstruction of molecular volu…
Riemannian Functional Map Synchronization for Probabilistic Partial Correspondence in Shape Networks
Faria Huq, Adrish Dey, Sahra Yusuf +3
We consider the problem of graph-matching on a network of 3D shapes with uncertainty quantification. We assume that the pairwise shape correspondences are efficiently represented a…
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
Nina Miolane, Matteo Caorsi, Umberto Lupo +30
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning…
A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses
Claire Donnat, Nina Miolane, Freddy Bunbury +1
Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc…
Learning Weighted Submanifolds with Variational Autoencoders and Riemannian Variational Autoencoders
Nina Miolane, Susan Holmes
Manifold-valued data naturally arises in medical imaging. In cognitive neuroscience, for instance, brain connectomes base the analysis of coactivation patterns between different br…
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 +1
Cryo-electron microscopy (cryo-EM) is capable of producing reconstructed 3D images of biomolecules at near-atomic resolution. As such, it represents one of the most promising imagi…
PVNet: A LRCN Architecture for Spatio-Temporal Photovoltaic PowerForecasting from Numerical Weather Prediction
Johan Mathe, Nina Miolane, Nicolas Sebastien +1
Photovoltaic (PV) power generation has emerged as one of the lead renewable energy sources. Yet, its production is characterized by high uncertainty, being dependent on weather con…
geomstats: a Python Package for Riemannian Geometry in Machine Learning
Nina Miolane, Johan Mathe, Claire Donnat +2
We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie gro…
Computing CNN Loss and Gradients for Pose Estimation with Riemannian Geometry
Benjamin Hou, Nina Miolane, Bishesh Khanal +7
Pose estimation, i.e. predicting a 3D rigid transformation with respect to a fixed co-ordinate frame in, SE(3), is an omnipresent problem in medical image analysis with application…
Template shape estimation: correcting an asymptotic bias
Nina Miolane, Susan Holmes, Xavier Pennec
We use tools from geometric statistics to analyze the usual estimation procedure of a template shape. This applies to shapes from landmarks, curves, surfaces, images etc. We demons…