3 citations · 3 across the 3 of their papers we have counts for
4 papers
Mitigating the reconstruction-detection trade-off in VAE-based unsupervised anomaly detection
Agathe Senellart, Maëlys Solal, Stéphanie Allassonnière +1
Variational autoencoders are widely used for unsupervised anomaly detection. Model selection however remains an open-question: to remain fully unsupervised, hyperparameters are oft…
Establishing Baselines for Photonic Quantum Machine Learning: Insights from an Open, Collaborative Initiative
Cassandre Notton, Vassilis Apostolou, Agathe Senellart +28
The Perceval Challenge is an open, reproducible benchmark designed to assess the potential of photonic quantum computing for machine learning. Focusing on a reduced and hardware-fe…
Bridging the inference gap in Mutimodal Variational Autoencoders
Agathe Senellart, Stéphanie Allassonnière
From medical diagnosis to autonomous vehicles, critical applications rely on the integration of multiple heterogeneous data modalities. Multimodal Variational Autoencoders offer ve…
Improving Multimodal Joint Variational Autoencoders through Normalizing Flows and Correlation Analysis
Agathe Senellart, Clément Chadebec, Stéphanie Allassonnière
We propose a new multimodal variational autoencoder that enables to generate from the joint distribution and conditionally to any number of complex modalities. The unimodal posteri…