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Richard Kurle
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2020 – today
- 2023
- [b1]Richard Kurle:
Variational Bayes for Continual Learning and Time-Series Forecasting. Technical University of Munich, Germany, 2023 - 2022
- [c9]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. NeurIPS 2022 - [i6]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 abs/2206.14342 (2022) - [i5]Richard Kurle, Ralf Herbrich, Tim Januschowski, Yuyang Wang, Jan Gasthaus:
On the detrimental effect of invariances in the likelihood for variational inference. CoRR abs/2209.07157 (2022) - 2021
- [c8]Alexej Klushyn, Richard Kurle, Maximilian Soelch, Botond Cseke, Patrick van der Smagt:
Latent Matters: Learning Deep State-Space Models. NeurIPS 2021: 10234-10245 - [c7]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: 29949-29961 - [i4]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 abs/2110.13878 (2021) - 2020
- [c6]Richard Kurle, Botond Cseke, Alexej Klushyn, Patrick van der Smagt
, Stephan Günnemann:
Continual Learning with Bayesian Neural Networks for Non-Stationary Data. ICLR 2020 - [c5]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 - [c4]Richard Kurle, Syama Sundar Rangapuram, Emmanuel de Bézenac, Stephan Günnemann, Jan Gasthaus:
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting. NeurIPS 2020
2010 – 2019
- 2019
- [c3]Richard Kurle, Stephan Günnemann, Patrick van der Smagt
Multi-Source Neural Variational Inference. AAAI 2019: 4114-4121 - [c2]Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt:
Learning Hierarchical Priors in VAEs. NeurIPS 2019: 2866-2875 - [i3]Alexej Klushyn, Nutan Chen, Richard Kurle, Botond Cseke, Patrick van der Smagt:
Learning Hierarchical Priors in VAEs. CoRR abs/1905.04982 (2019) - 2018
- [c1]Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt
Metrics for Deep Generative Models. AISTATS 2018: 1540-1550 - [i2]Richard Kurle, Stephan Günnemann, Patrick van der Smagt:
Multi-Source Neural Variational Inference. CoRR abs/1811.04451 (2018) - 2017
- [i1]Nutan Chen, Alexej Klushyn, Richard Kurle, Xueyan Jiang, Justin Bayer, Patrick van der Smagt:
Metrics for Deep Generative Models. CoRR abs/1711.01204 (2017)
Coauthor Index
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