| Metrics for Deep Generative Models N Chen*, A Klushyn*, R Kurle*, X Jiang, J Bayer, P van der Smagt International Conference on Artificial Intelligence and Statistics (AISTATS), 2018 | 135 | 2018 |
| Learning Hierarchical Priors in VAEs A Klushyn, N Chen, R Kurle, B Cseke, P van der Smagt Advances in Neural Information Processing Systems (NeurIPS), 2019 | 105 | 2019 |
| Continual Learning With Bayesian Neural Networks for Non-Stationary Data R Kurle, B Cseke, A Klushyn, P van der Smagt, S Günnemann International Conference on Learning Representations (ICLR), 2020 | 75 | 2020 |
| Learning Flat Latent Manifolds With VAEs N Chen, A Klushyn, F Ferroni, J Bayer, P van der Smagt International Conference on Machine Learning (ICML), 2020 | 44 | 2020 |
| Latent Matters: Learning Deep State-Space Models A Klushyn, R Kurle, M Soelch, B Cseke, P van der Smagt Advances in Neural Information Processing Systems (NeurIPS), 2021 | 37 | 2021 |
| Fast Approximate Geodesics for Deep Generative Models N Chen, F Ferroni, A Klushyn, A Paraschos, J Bayer, P van der Smagt International Conference on Artificial Neural Networks (ICANN), 2019 | 27 | 2019 |
| Active Learning Based on Data Uncertainty and Model Sensitivity N Chen, A Klushyn, A Paraschos, D Benbouzid, P Van der Smagt International Conference on Intelligent Robots and Systems (IROS), 2018 | 16 | 2018 |
| Increasing the Generalisation Capacity of Conditional VAEs A Klushyn, N Chen, B Cseke, J Bayer, P van der Smagt International Conference on Artificial Neural Networks (ICANN), 2019 | 2 | 2019 |
| Metrics for Deep Generative Models Based on Learned Skills N Chen*, A Klushyn*, R Kurle*, X Jiang, J Bayer, P van der Smagt Advances in Neural Information Processing Systems (NeurIPS), Workshop on …, 2017 | 2 | 2017 |
| Latent Matters – Amortised Variational Inference With Constrained Optimisation and Learnable Priors A Klushyn Technical University of Munich, 2021 | 1 | 2021 |
Patrick van der SmagtVolkswagen AG; ELTE UniversityVerified email at tum.de
