SESAM at SemEval-2020 Task 8: Investigating the Relationship between Image and Text in Sentiment Analysis of Memes. - Lisa Bonheme; Marek Grzes (SemEval@COLING, 2020)

How do Variational Autoencoders Learn? Insights from Representational Similarity. - Lisa Bonheme; Marek Grzes (CoRR, 2022)

FONDUE: an algorithm to find the optimal dimensionality of the latent representations of variational autoencoders. - Lisa Bonheme; Marek Grzes (CoRR, 2022)

The Polarised Regime of identifiable Variational Autoencoders. - Lisa Bonheme; Marek Grzes (Tiny Papers @ ICLR, 2023)

Deconstructing deep active inference. - Théophile Champion; Marek Grzes; Lisa Bonheme; Howard Bowman (CoRR, 2023)

How good are variational autoencoders at transfer learning? - Lisa Bonheme; Marek Grzes (CoRR, 2023)

aedFaCT: Scientific Fact-Checking Made Easier via Semi-Automatic Discovery of Relevant Expert Opinions. - Enes Altuncu; Jason R. C. Nurse; Meryem Bagriacik; Sophie Kaleba; Haiyue Yuan; Lisa Bonheme; Shujun Li (CoRR, 2023)

Be More Active! Understanding the Differences Between Mean and Sampled Representations of Variational Autoencoders. - Lisa Bonheme; Marek Grzes (J. Mach. Learn. Res., 2023)

Deconstructing Deep Active Inference: A Contrarian Information Gatherer. - Théophile Champion; Marek Grzes; Lisa Bonheme; Howard Bowman (Neural Comput., 2024)