Search Results for author: Claire Donnat

Found 9 papers, 3 papers with code

A Simplified Framework for Contrastive Learning for Node Representations

no code implementations1 May 2023 Ilgee Hong, Huy Tran, Claire Donnat

Contrastive learning has recently established itself as a powerful self-supervised learning framework for extracting rich and versatile data representations.

Contrastive Learning Data Augmentation +1

Tuning the Geometry of Graph Neural Networks

no code implementations12 Jul 2022 Sowon Jeong, Claire Donnat

By recursively summing node features over entire neighborhoods, spatial graph convolution operators have been heralded as key to the success of Graph Neural Networks (GNNs).

Deep Generative Modeling for Volume Reconstruction in Cryo-Electron Microscopy

no code implementations8 Jan 2022 Claire Donnat, Axel Levy, Frederic Poitevin, Ellen Zhong, Nina Miolane

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 volumes, thereby promising further advances in biology, chemistry, and pharmacological research.

A Bayesian Hierarchical Network for Combining Heterogeneous Data Sources in Medical Diagnoses

no code implementations27 Jul 2020 Claire Donnat, Nina Miolane, Frederick de St Pierre Bunbury, Jack Kreindler

Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc.).

Applications

Geomstats: A Python Package for Riemannian Geometry in Machine Learning

1 code implementation ICLR 2019 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 Holmes, Xavier Pennec

We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more.

BIG-bench Machine Learning Clustering +2

Convex Hierarchical Clustering for Graph-Structured Data

no code implementations8 Nov 2019 Claire Donnat, Susan Holmes

Convex clustering is a recent stable alternative to hierarchical clustering.

Clustering

geomstats: a Python Package for Riemannian Geometry in Machine Learning

2 code implementations ICLR 2019 Nina Miolane, Johan Mathe, Claire Donnat, Mikael Jorda, Xavier Pennec

This paper also presents a review of manifolds in machine learning and an overview of the geomstats package with examples demonstrating its use for efficient and user-friendly Riemannian geometry.

BIG-bench Machine Learning Riemannian optimization

Spectral Graph Wavelets for Structural Role Similarity in Networks

no code implementations ICLR 2018 Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec

Nodes residing in different parts of a graph can have similar structural roles within their local network topology.

Learning Structural Node Embeddings Via Diffusion Wavelets

1 code implementation KDD 2018 Claire Donnat, Marinka Zitnik, David Hallac, Jure Leskovec

Nodes residing in different parts of a graph can have similar structural roles within their local network topology.

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