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Alexej Klushyn
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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
802018
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
632019
Continual Learning With Bayesian Neural Networks for Non-Stationary Data
R Kurle, B Cseke, A Klushyn, P van der Smagt, S GŁnnemann
International Conference on Learning Representations (ICLR), 2020
432020
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
202019
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
152018
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
132020
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
122021
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
22019
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
22017
Latent Matters – Amortised Variational Inference With Constrained Optimisation and Learnable Priors
A Klushyn
Technical University of Munich, 2021
2021
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