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

Multi-Source Neural Variational Inference. - Richard Kurle; Stephan Günnemann; Patrick van der Smagt (AAAI, 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)

Normalizing Kalman Filters for Multivariate Time Series Analysis. - Emmanuel de Bézenac; Syama Sundar Rangapuram; Konstantinos Benidis; Michael Bohlke-Schneider; Richard Kurle; Lorenzo Stella; Hilaf Hasson; Patrick Gallinari; Tim Januschowski (NeurIPS, 2020)

Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. - Richard Kurle; Syama Sundar Rangapuram; Emmanuel de Bézenac; Stephan Günnemann; Jan Gasthaus (NeurIPS, 2020)

Deep Explicit Duration Switching Models for Time Series. - Abdul Fatir Ansari; Konstantinos Benidis; Richard Kurle; Ali Caner Türkmen; Harold Soh; Alexander J. Smola; Bernie Wang 001; Tim Januschowski (NeurIPS, 2021)

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

On the detrimental effect of invariances in the likelihood for variational inference. - Richard Kurle; Ralf Herbrich; Tim Januschowski; Yuyang Wang 001; Jan Gasthaus (NeurIPS, 2022)

Intrinsic Anomaly Detection for Multi-Variate Time Series. - Stephan Rabanser; Tim Januschowski; Kashif Rasul; Oliver Borchert; Richard Kurle; Jan Gasthaus; Michael Bohlke-Schneider; Nicolas Papernot; Valentin Flunkert (CoRR, 2022)

Variational Bayes for Continual Learning and Time-Series Forecasting. - Richard Kurle (2023)

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

xLSTM 7B: A Recurrent LLM for Fast and Efficient Inference. - Maximilian Beck; Korbinian Pöppel; Phillip Lippe; Richard Kurle; Patrick M. Blies; Günter Klambauer; Sebastian Böck; Sepp Hochreiter (CoRR, 2025)