Nina Miolane
11 papers · 2020–2025 · 5 venues · across top CS/AI venues
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Research topics
Machine Learning
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Deep Learning
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Core Methods
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Artificial Intelligence
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Core AI
(4)
Optimization & Theory
(2)
Healthcare & Medicine
(1)
Interdisciplinary
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Mathematics & Optimization
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Application Areas
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Bayesian & Probabilistic
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Clinical
(1)
Cognitive Science
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(1)
Keywords
path integration
(2)
spatial navigation
(2)
grid cell
(2)
recurrent neural network
(2)
relational learning
(1)
interpretability
(1)
image classification
(1)
graph representation
(1)
manifold learning
(1)
group theory
(1)
dimension reduction
(1)
signal processing
(1)
message passing
(1)
graph embedding
(1)
riemannian geometry
(1)
geometric deep learning
(1)
functional magnetic resonance imaging
(1)
riemannian manifold
(1)
learning rate
(1)
short-term memory
(1)
Papers
Dynamical phases of short-term memory mechanisms in RNNs
ICML 2025
TopoTune: A Framework for Generalized Combinatorial Complex Neural Networks
ICML 2025
Global Distortions from Local Rewards: Neural Coding Strategies in Path-Integrating Neural Systems
NeurIPS 2024
Not so griddy: Internal representations of RNNs path integrating more than one agent
NeurIPS 2024
Position: Topological Deep Learning is the New Frontier for Relational Learning
ICML 2024
The Selective G-Bispectrum and its Inversion: Applications to G-Invariant Networks
NeurIPS 2024
TopoX: A Suite of Python Packages for Machine Learning on Topological Domains
JMLR 2024
A General Framework for Robust G-Invariance in G-Equivariant Networks
NeurIPS 2023
CryoAI: Amortized Inference of Poses for Ab Initio Reconstruction of 3D Molecular Volumes from Real Cryo-EM Images
ECCV 2022
Geomstats: A Python Package for Riemannian Geometry in Machine Learning
JMLR 2020
Learning Weighted Submanifolds With Variational Autoencoders and Riemannian Variational Autoencoders
CVPR 2020