Search Results for author: Merja Heinäniemi

Found 2 papers, 1 papers with code

The Transitive Information Theory and its Application to Deep Generative Models

no code implementations • 9 Mar 2022 • Trung Ngo, Najwa Laabid, Ville Hautamäki, Merja Heinäniemi

Paradoxically, a Variational Autoencoder (VAE) could be pushed in two opposite directions, utilizing powerful decoder model for generating realistic images but collapsing the learned representation, or increasing regularization coefficient for disentangling representation but ultimately generating blurry examples.

Inductive Bias Variational Inference

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SISUA: Semi-Supervised Generative Autoencoder for Single Cell Data

1 code implementation • ICML Workshop on Computational Biology 2019 2019 • Trung Ngo Trong, Roger Kramer, Juha Mehtonen, Gerardo González, Ville Hautamäki, Merja Heinäniemi

In this study, we propose models based on the Bayesian generative approach, where protein quantification available as CITE-seq counts from the same cells are used to constrain the learning process, thus forming a semi-supervised model.

Single-cell modeling

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