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3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.

G. Zhou, N. Gothoskar, L. Wang, J. Tenenbaum, D. Gutfreund, M. Lázaro-Gredilla, D. George, and V. Mansinghka. ICCV, page 21568-21579. IEEE, (2023)

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Ludmilla Gutfreund

Klaus Siegfried Gutfreund

Horst Dan

Dan Radulescu

Dan Tidten

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3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.G. Zhou, N. Gothoskar, L. Wang, J. Tenenbaum, D. Gutfreund, M. Lázaro-Gredilla, D. George, and V. Mansinghka. ICCV, page 21568-21579. IEEE, (2023)Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind.T. Zhi-Xuan, N. Gothoskar, F. Pollok, D. Gutfreund, J. Tenenbaum, and V. Mansinghka. CoRR, (2022)Multi-Moments in Time: Learning and Interpreting Models for Multi-Action Video Understanding.M. Monfort, B. Pan, K. Ramakrishnan, A. Andonian, B. McNamara, A. Lascelles, Q. Fan, D. Gutfreund, R. Feris, and A. Oliva. IEEE Trans. Pattern Anal. Mach. Intell., 44 (12): 9434-9445 (2022)Derandomizing Arthur-Merlin Games and Approximate Counting Implies Exponential-Size Lower Bounds.D. Gutfreund, and A. Kawachi. CCC, page 38-49. IEEE Computer Society, (2010)ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models.A. Barbu, D. Mayo, J. Alverio, W. Luo, C. Wang, D. Gutfreund, J. Tenenbaum, and B. Katz. NeurIPS, page 9448-9458. (2019)LInK: Learning Joint Representations of Design and Performance Spaces through Contrastive Learning for Mechanism Synthesis.A. Nobari, A. Srivastava, D. Gutfreund, K. Xu, and F. Ahmed. CoRR, (2024)Fooling Parity Tests with Parity Gates.D. Gutfreund, and E. Viola. APPROX-RANDOM, volume 3122 of Lecture Notes in Computer Science, page 381-392. Springer, (2004)Automatic Arguments Construction - From Search Engine to Research Engine.D. Gutfreund, Y. Katz, and N. Slonim. AAAI Fall Symposia, AAAI Press, (2016)Identifying Interpretable Action Concepts in Deep Networks.K. Ramakrishnan, M. Monfort, B. McNamara, A. Lascelles, D. Gutfreund, R. Feris, and A. Oliva. CVPR Workshops, page 12-15. Computer Vision Foundation / IEEE, (2019)Claims on demand - an initial demonstration of a system for automatic detection and polarity identification of context dependent claims in massive corpora.N. Slonim, E. Aharoni, C. Alzate, R. Bar-Haim, Y. Bilu, L. Dankin, I. Eiron, D. Hershcovich, S. Hummel, M. Khapra and 10 other author(s). COLING (Demos), page 6-9. ACL, (2014)

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Disambiguation of "Gutfreund, Dan"

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3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.

Please choose a person to relate this publication to

Ludmilla Gutfreund

Klaus Siegfried Gutfreund

Horst Dan

Dan Radulescu

Dan Tidten

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Gutfreund, Dan"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML 3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.

Please choose a person to relate this publication to

Ludmilla Gutfreund

Klaus Siegfried Gutfreund

Horst Dan

Dan Radulescu

Dan Tidten

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
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APA
Chicago
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3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.