Francesco Paolo Casale — accepted AI-conference papers

2024

Mixed Models with Multiple Instance Learning

Jan P. Engelmann, Alessandro Palma, Jakub M. Tomczak, Fabian Theis, Francesco Paolo Casale

AISTATS 2024poster

Predicting patient features from single-cell data can help identify cellular states implicated in health and disease. Linear models and average cell type expressions are typically favored for this task for their efficiency and robustness, but they overlook the rich cell heterogeneity inherent in sin…

Cited by 7 Source ↗PDF ↗code ↗Scholar ↗

2018

Gaussian Process Prior Variational Autoencoders

Francesco Paolo Casale, Adrian Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi

NeurIPS 2018poster

Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many imp…

Cited by 165 Source ↗PDF ↗code ↗Scholar ↗