Metrics for Deep Generative Models. - Nutan Chen; Alexej Klushyn; Richard Kurle; Xueyan Jiang; Justin Bayer; Patrick van der Smagt (AISTATS, 2018)

Active Learning based on Data Uncertainty and Model Sensitivity. - Nutan Chen; Alexej Klushyn; Alexandros Paraschos; Djalel Benbouzid; Patrick van der Smagt (IROS, 2018)

Fast Approximate Geodesics for Deep Generative Models. - Nutan Chen; Francesco Ferroni; Alexej Klushyn; Alexandros Paraschos; Justin Bayer; Patrick van der Smagt (ICANN (2), 2019)

Increasing the Generalisaton Capacity of Conditional VAEs. - Alexej Klushyn; Nutan Chen; Botond Cseke; Justin Bayer; Patrick van der Smagt (ICANN (2), 2019)

Learning Hierarchical Priors in VAEs. - Alexej Klushyn; Nutan Chen; Richard Kurle; Botond Cseke; Patrick van der Smagt (NeurIPS, 2019)

Continual Learning with Bayesian Neural Networks for Non-Stationary Data. - Richard Kurle; Botond Cseke; Alexej Klushyn; Patrick van der Smagt; Stephan Günnemann (ICLR, 2020)

Learning Flat Latent Manifolds with VAEs. - Nutan Chen; Alexej Klushyn; Francesco Ferroni; Justin Bayer; Patrick van der Smagt (ICML, 2020)

Latent Matters: Learning Deep State-Space Models. - Alexej Klushyn; Richard Kurle; Maximilian Soelch 001; Botond Cseke; Patrick van der Smagt (NeurIPS, 2021)

Latent Matters: Amortised Variational Inference With Constrained Optimisation and Learnable Priors. - Alexej Klushyn (2021)

BALI: Learning Neural Networks via Bayesian Layerwise Inference. - Richard Kurle; Alexej Klushyn; Ralf Herbrich (CoRR, 2024)