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Petra Poklukar
Petra Poklukar
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Latent space roadmap for visual action planning of deformable and rigid object manipulation
M Lippi, P Poklukar, MC Welle, A Varava, H Yin, A Marino, D Kragic
2020 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2020
422020
Data-efficient visuomotor policy training using reinforcement learning and generative models
A Ghadirzadeh, P Poklukar, V Kyrki, D Kragic, M Björkman
arXiv preprint arXiv:2007.13134, 2020
102020
Bayesian Meta-Learning for Few-Shot Policy Adaptation Across Robotic Platforms
A Ghadirzadeh, X Chen, P Poklukar, C Finn, M Björkman, D Kragic
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2021
72021
Enabling visual action planning for object manipulation through latent space roadmap
M Lippi, P Poklukar, MC Welle, A Varava, H Yin, A Marino, D Kragic
IEEE Transactions on Robotics, 2022
52022
Delaunay Component Analysis for Evaluation of Data Representations
DK Petra Poklukar, Vladislav Polianskii, Anastasia Varava, Florian Pokorny
International Conference on Learning Representations, 2022
5*2022
GeomCA: Geometric Evaluation of Data Representations
P Poklukar, A Varava, D Kragic
Proceedings of the 38th International Conference on Machine Learning 139 …, 2021
52021
Modeling assumptions and evaluation schemes: On the assessment of deep latent variable models.
J Bütepage, P Poklukar, D Kragic
CVPR Workshops, 9-12, 2019
42019
GraphDCA--a Framework for Node Distribution Comparison in Real and Synthetic Graphs
C Ceylan, P Poklukar, H Hultin, A Kravchenko, A Varava, D Kragic
arXiv preprint arXiv:2202.03884, 2022
22022
GMC--Geometric Multimodal Contrastive Representation Learning
P Poklukar, M Vasco, H Yin, FS Melo, A Paiva, D Kragic
arXiv preprint arXiv:2202.03390, 2022
22022
Batch Curation for Unsupervised Contrastive Representation Learning
MC Welle, P Poklukar, D Kragic
arXiv preprint arXiv:2108.08643, 2021
22021
Training and Evaluation of Deep Policies using Reinforcement Learning and Generative Models
A Ghadirzadeh, P Poklukar, K Arndt, C Finn, V Kyrki, D Kragic, ...
arXiv preprint arXiv:2204.08573, 2022
12022
Few-Shot Learning with Weak Supervision
A Ghadirzadeh, P Poklukar, X Chen, H Yao, H Azizpour, M Björkman, ...
Learning to Learn-Workshop at ICLR 2021, 2021
12021
Augment-Connect-Explore: a Paradigm for Visual Action Planning with Data Scarcity
M Lippi, MC Welle, P Poklukar, A Marino, D Kragic
arXiv preprint arXiv:2203.13034, 2022
2022
Learning and Evaluating the Geometric Structure of Representation Spaces
P Poklukar
KTH Royal Institute of Technology, 2022
2022
GraphDCA - a Framework for Node Distribution Comparison in Real and Synthetic Graphs
P Poklukar, C Ceylan, H Hultin, O Kravchenko, A Varava, D Kragic
2022
Seeing the whole picture instead of a single point: Self-supervised likelihood learning for deep generative models
P Poklukar, J Bütepage, D Kragic
2019
On the real spectrum compactification of Teichmüller space: master thesis
P Poklukar
Univerza v Ljubljani, Fakulteta za matematiko in fiziko, 2016
2016
Operacije na gladkih mnogoterostih: delo diplomskega seminarja
P Poklukar
Univerza v Ljubljani, Fakulteta za matematiko in fiziko, 2014
2014
Latent Space Roadmap for Visual Action Planning
M Lippi, P Poklukar, MC Welle, A Varava, H Yin, A Marino, D Kragic
Analyzing Representations through Interventions
P Poklukar, MC Welle, A Varava, D Kragic
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Articles 1–20