Richard Kurle

Publication Activity (10 Years)

Years Active: 2017-2022
Publications (10 Years): 15

Top Topics

Generative Model

Variational Inference

Top Venues

Publications

Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
On the detrimental effect of invariances in the likelihood for variational inference. CoRR (2022)
Stephan Rabanser, Tim Januschowski, Kashif Rasul, Oliver Borchert, Richard Kurle, Jan Gasthaus, Michael Bohlke-Schneider, Nicolas Papernot, Valentin Flunkert
Intrinsic Anomaly Detection for Multi-Variate Time Series. CoRR (2022)
Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus
On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS (2022)
Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt
Latent Matters: Learning Deep State-Space Models. NeurIPS (2021)
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Yuyang Wang, Tim Januschowski
Deep Explicit Duration Switching Models for Time Series. CoRR (2021)
Abdul Fatir Ansari, Konstantinos Benidis, Richard Kurle, Ali Caner Türkmen, Harold Soh, Alexander J. Smola, Bernie Wang, Tim Januschowski
Deep Explicit Duration Switching Models for Time Series. NeurIPS (2021)
Emmanuel de Bézenac, Syama Sundar Rangapuram, Konstantinos Benidis, Michael Bohlke-Schneider, Richard Kurle, Lorenzo Stella, Hilaf Hasson, Patrick Gallinari, Tim Januschowski
Normalizing Kalman Filters for Multivariate Time Series Analysis. NeurIPS (2020)
Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS (2020)
Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt, Stephan Günnemann
Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR (2020)
Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Multi-Source Neural Variational Inference. AAAI (2019)
Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
Learning Hierarchical Priors in VAEs. NeurIPS (2019)
Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt
Learning Hierarchical Priors in VAEs. CoRR (2019)
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt
Metrics for Deep Generative Models. AISTATS (2018)
Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Multi-Source Neural Variational Inference. CoRR (2018)
Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt
Metrics for Deep Generative Models. CoRR (2017)