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ICML 2023 Topological Deep Learning Challenge: Design and Results.

M. Papillon, M. Hajij, A. Myers, F. Frantzen, G. Zamzmi, H. Jenne, J. Mathe, J. Hoppe, M. Schaub, T. Papamarkou, A. Guzmán-Sáenz, B. Rieck, N. Livesay, T. Dey, A. Rabinowitz, A. Brent, A. Salatiello, A. Nikitin, A. Zia, C. Battiloro, D. Gavrilev, G. Bökman, G. Magai, G. Bazhenov, G. Bernárdez, I. Spinelli, J. Agerberg, K. Nadimpalli, L. Telyatnikov, L. Scofano, L. Testa, M. Lecha, M. Yang, M. Hassanin, O. Gardaa, O. Zaghen, P. Häusner, P. Snopoff, P. Melnyk, R. Ballester, S. Barikbin, S. Escalera, S. Fiorellino, H. Kvinge, J. Meissner, K. Ramamurthy, M. Scholkemper, P. Rosen, R. Walters, S. Samaga, S. Mukherjee, S. Sanborn, T. Emerson, T. Doster, T. Birdal, V. Grande, A. Khamis, S. Scardapane, S. Singh, T. Malygina, Y. Yue, and N. Miolane. TAG-ML, volume 221 of Proceedings of Machine Learning Research, page 3-8. PMLR, (2023)

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David Georg

Jens Georg

Inna Georgieva

Maria Georgiou-Smith

Hans-Walter Georgii

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Azimuthal Rotational Equivariance in Spherical Convolutional Neural Networks.C. Toft, G. Bökman, and F. Kahl. ICPR, page 3808-3814. IEEE, (2022)DeDoDe: Detect, Don't Describe - Describe, Don't Detect for Local Feature Matching.J. Edstedt, G. Bökman, M. Wadenbäck, and M. Felsberg. 3DV, page 148-157. IEEE, (2024)DeDoDe v2: Analyzing and Improving the DeDoDe Keypoint Detector.J. Edstedt, G. Bökman, and Z. Zhao. CoRR, (2024)In Search of Projectively Equivariant Neural Networks.G. Bökman, A. Flinth, and F. Kahl. CoRR, (2022)RoMa: Revisiting Robust Losses for Dense Feature Matching.J. Edstedt, Q. Sun, G. Bökman, M. Wadenbäck, and M. Felsberg. CoRR, (2023)ICML 2023 Topological Deep Learning Challenge: Design and Results.M. Papillon, M. Hajij, A. Myers, F. Frantzen, G. Zamzmi, H. Jenne, J. Mathe, J. Hoppe, M. Schaub, T. Papamarkou and 52 other author(s). TAG-ML, volume 221 of Proceedings of Machine Learning Research, page 3-8. PMLR, (2023)Rigidity Preserving Image Transformations and Equivariance in Perspective.L. Brynte, G. Bökman, A. Flinth, and F. Kahl. SCIA (2), volume 13886 of Lecture Notes in Computer Science, page 59-76. Springer, (2023)Leveraging Cutting Edge Deep Learning Based Image Matching for Reconstructing a Large Scene from Sparse Images.G. Bökman, and J. Edstedt. CoRR, (2023)A case for using rotation invariant features in state of the art feature matchers.G. Bökman, and F. Kahl. CVPR Workshops, page 5106-5115. IEEE, (2022)ZZ-Net: A Universal Rotation Equivariant Architecture for 2D Point Clouds.G. Bökman, F. Kahl, and A. Flinth. CVPR, page 10966-10975. IEEE, (2022)

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Disambiguation of "Bökman, Georg"

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ICML 2023 Topological Deep Learning Challenge: Design and Results.

Please choose a person to relate this publication to

David Georg

Jens Georg

Inna Georgieva

Maria Georgiou-Smith

Hans-Walter Georgii

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Bökman, Georg"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML ICML 2023 Topological Deep Learning Challenge: Design and Results.

Please choose a person to relate this publication to

David Georg

Jens Georg

Inna Georgieva

Maria Georgiou-Smith

Hans-Walter Georgii

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
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history of this post
URL
DOI
BibTeX
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APA
Chicago
DIN 1505
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ICML 2023 Topological Deep Learning Challenge: Design and Results.