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Alexej Klushyn
Alexej Klushyn
Airbus Group AI Research
Zweryfikowany adres z airbus.com
Tytuł
Cytowane przez
Cytowane przez
Rok
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
1292018
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
972019
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
712020
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
402020
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
322021
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
262019
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
172018
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
12021
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