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  • 2024

    Accurate Computation of the Logarithm of Modified Bessel Functions on GPUs

    Plesner, A. L., Sørensen, H. H. B. & Hauberg, S., 2024, Proceedings of the 38th ACM International Conference on Supercomputing. Association for Computing Machinery, p. 213-224

    Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

  • Bayesian Metric Learning for Uncertainty Quantification in Image Retrieval

    Warburg, F., Miani, M., Brack, S. & Hauberg, S., 2024, (Accepted/In press) Proceedings of the 37th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, Vol. 36. 26 p.

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  • Learning to Taste: A Multimodal Wine Dataset

    Bender, T., Søresen, S. M., Kashani, A., Hjorleifsson, K. E., Hyldig, G., Hauberg, S., Belongie, S. & Warburg, F., 2024, (Accepted/In press) Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS 2023).. Neural Information Processing Systems Foundation, Vol. 36. 40 p.

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  • On Masked Pre-training and the Marginal Likelihood

    Moreno-Muñoz, P., Recasens, P. G. & Hauberg, S., 2024, (Accepted/In press) Proceedings of the 37th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, Vol. 36. 11 p.

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  • Riemannian Laplace approximations for Bayesian neural networks

    Bergamin, F., Moreno-Muñoz, P., Hauberg, S. & Arvanitidis, G., 2024, (Accepted/In press) Proceedings of the 37th Conference on Neural Information Processing Systems. Neural Information Processing Systems Foundation, Vol. 36. 28 p.

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  • 2023

    Adaptive Cholesky Gaussian Processes

    Bartels, S., Stensbo-Smidt, K., Moreno-Munoz, P., Boomsma, W., Frellsen, J. & Hauberg, S., 2023, Proceedings of the 26th International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, Vol. 206. p. 408-452 (Proceedings of Machine Learning Research).

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  • 2022

    Benchmarking Generative Latent Variable Models for Speech

    Havtorn, J. D., Borgholt, L., Hauberg, S., Frellsen, J. & Maaløe, L., 2022, Proceedings of ICLR Workshop on Deep Generative Models for Highly Structured Data. 23 p.

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  • Laplacian Autoencoders for Learning Stochastic Representations

    Miani, M., Warburg, F., Moreno-Muñoz, P., Detlefsen, N. S. & Hauberg, S., 2022, Proceedings of 36th Conference on Neural Information Processing Systems. 27 p.

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  • Mario Plays on a Manifold: Generating Functional Content in Latent Space through Differential Geometry

    Gonzalez-Duque, M., Palm, R. B., Hauberg, S. & Risi, S., 2022, Proceedings of 2022 IEEE Conference on Games. IEEE, 8 p.

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  • Model-agnostic out-of-distribution detection using combined statistical tests

    Bergamin, F., Mattei, P-A., Havtorn, J. D., Senetaire, H., Schmutz, H., Maaløe, L., Hauberg, S. & Frellsen, J., 2022, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics . International Machine Learning Society (IMLS), p. 10753-10776 (Proceedings of Machine Learning Research, Vol. 151).

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  • Probabilistic Spatial Transformer Networks

    Schwöbel, P. E., Warburg, F. R., Jørgensen, M., Madsen, K. H. & Hauberg, S., 2022, Proceedings of 38th Conference on Uncertainty in Artificial Intelligence . p. 1749-1759 (Proceedings of Machine Learning Research, Vol. 180).

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  • Pulling back information geometry

    Arvanitidis, G., González-Duque, M., Pouplin, A., Kalatzis, D. & Hauberg, S., 2022, Proceedings of the 25th International Conference on Artificial Intelligence and Statistics. 22 p. (Proceedings of Machine Learning Research, Vol. 151).

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  • Revisiting Active Sets for Gaussian Process Decoders

    Moreno-Muñoz, P., Feldager, C. W. & Hauberg, S., 2022, Proceedings of 36th Conference on Neural Information Processing Systems. 16 p.

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  • 2021

    Bayesian Triplet Loss: Uncertainty Quantification in Image Retrieval

    Warburg, F., Jørgensen, M., Civera, J. & Hauberg, S., 2021, Proceedings of 2021 International Conference on Computer Vision. 11 p.

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  • Bounds all around: training energy-based models with bidirectional bounds

    Geng, C., Wang, J., Gao, Z., Frellsen, J. & Hauberg, S., 2021, Proceedings of 35th Conference on Neural Information Processing Systems . International Machine Learning Society (IMLS)

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  • Hierarchical VAEs Know What They Don't Know

    Havtorn, J. D., Frellsen, J., Hauberg, S. & Maaløe, L., 2021, Proceedings of the 38th International Conference on Machine Learning. International Machine Learning Society (IMLS), 12 p. (Proceedings of Machine Learning Research, Vol. 139).

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  • Learning Riemannian Manifolds for Geodesic Motion Skills

    Beik-Mohammadi, H., Hauberg, S., Arvanitidis, G., Neumann, G. & Rozo, L., 2021, Proceedings of Robotics: Science and Systems 2021. 11 p. (Robotics: Science and System Xvii).

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  • Spontaneous Symmetry Breaking in Data Visualization

    Feldager, C. W., Hauberg, S. & Hansen, L. K., 2021, Artificial Neural Networks and Machine Learning. Farkaš, I., Masulli, P., Otte, S. & Wermter, S. (eds.). Springer, p. 435-446 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 12892 LNCS).

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  • 2020

    Can you trust predictive uncertainty under real dataset shifts in digital pathology?

    Thagaard, J., Hauberg, S., van der Vegt, B., Ebstrup, T., Hansen, J. D. & Dahl, A. B., 2020, Proceedings of 23rd International Conference on Medical Image Computing and Computer Assisted Intervention. Springer, p. 824-833 (Lecture Notes in Computer Science, Vol. 12261).

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  • Mapillary Street-Level Sequences: A Dataset for Lifelong Place Recognition

    Warburg, F. R., Hauberg, S., Lopez-Antequera, M., Gargallo, P., Kuang, Y. & Civera, J., 2020, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition 2020. IEEE, 10 p.

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  • Variational Autoencoders with Riemannian Brownian Motion Priors

    Kalatzis, D., Eklund, D., Arvanitidis, G. & Hauberg, S., 2020, Proceedings of the 37th International Conference on Machine Learning. Daume, H. & Singh, A. (eds.). International Machine Learning Society (IMLS), Vol. 119. p. 5020-5033

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  • 2019

    Explicit Disentanglement of Appearance and Perspective in Generative Models

    Detlefsen, N. S. & Hauberg, S., 2019, Proceedings of 33rd Conference on Neural Information Processing Systems. 19 p.

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  • Fast and Robust Shortest Paths on Manifolds Learned from Data

    Arvanitidis, G., Hauberg, S., Hennig, P. & Schober, M., 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. Microtome Publishing, Vol. 89. p. 1506-1515

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  • Probabilistic Riemannian submanifold learning with wrapped Gaussian process latent variable models

    Mallasto, A., Hauberg, S. & Feragen, A., 2019, Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics. Microtome Publishing, Vol. 89. p. 2368-2377

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  • Reliable training and estimation of variance networks

    Detlefsen, N. S., Jørgensen, M. & Hauberg, S., 2019, Proceedings of 33rd Conference on Neural Information Processing Systems. 11 p.

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  • 2018

    Deep Diffeomorphic Transformer Networks

    Detlefsen, N. S., Freifeld, O. & Hauberg, S., 2018, Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE, p. 4403-4412 (I E E E Conference on Computer Vision and Pattern Recognition. Proceedings).

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  • Directional Statistics with the Spherical Normal Distribution

    Hauberg, S., 5 Sept 2018, Proceedings of 2018 21st International Conference on Information Fusion, FUSION 2018. IEEE, p. 704-711

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  • Latent space oddity: On the curvature of deep generative models

    Arvanitidis, G., Hansen, L. K. & Hauberg, S., 1 Jan 2018, Proceedings of International Conference on Learning Representations. 15 p.

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  • 2017

    Intrinsic Grassmann Averages for Online Linear and Robust Subspace Learning

    Chakraborty, R., Hauberg, S. & Vemuri, B. C., 2017, Proceedings of the 2017 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, p. 801-809

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  • 2016

    Open Problem: Kernel methods on manifolds and metric spaces: What is the probability of a positive definite geodesic exponential kernel?

    Feragen, A. & Hauberg, S., 2016, JMLR: Workshop and Conference Proceedings. Vol. 49. p. 1-4 4 p. (JMLR: Workshop and Conference Proceedings).

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  • 2015

    A Random Riemannian Metric for Probabilistic Shortest-Path Tractography

    Hauberg, S., Schober, M., Liptrot, M. G., Hennig, P. & Feragen, A., 2015, Proceedings of the 18th International Conference on Medical Image Computing and Computer-Assisted Intervention – MICCAI 2015: Part 1. Navab, N., Hornegger, J., Wells, W. M. & Frangi, A. F. (eds.). Springer, p. 597-604 (Lecture Notes in Computer Science, Vol. 9349).

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  • Geodesic exponential kernels: When Curvature and Linearity Conflict

    Feragen, A., Lauze, F. & Hauberg, S., 2015, Proceedings of the 28th IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2015). IEEE, p. 3032-3042

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  • 2014

    Grassmann Averages for Scalable Robust PCA

    Hauberg, S., Feragen, A. & Black, M. J., 2014, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014. IEEE, p. 3810-3817 (I E E E Conference on Computer Vision and Pattern Recognition. Proceedings).

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  • Metrics for Probabilistic Geometries

    Tosi, A., Hauberg, S., Vellido, A. & Lawrence, N. D., 2014, Proceedings of 30th Conference on Uncertainty in Artificial Intelligence (UAI 2014). Zhang, N. L. & Tian, J. (eds.). AUAI Press Corvallis, p. 800-809

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  • Model Transport: Towards Scalable Transfer Learning on Manifolds

    Freifeld, O., Hauberg, S. & Black, M. J., 2014, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2014. IEEE, p. 1378-1385 (I E E E Conference on Computer Vision and Pattern Recognition. Proceedings).

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  • Probabilistic shortest path tractography in DTI using Gaussian Process ODE solvers

    Schober, M., Kasenburg, N., Feragen, A., Hennig, P. & Hauberg, S., 2014, Proceedings of the 17th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2014), Part III. Golland, P., Hata, N., Barillot, C., Hornegger, J. & Howe, R. (eds.). Springer, p. 265-272 (Lecture Notes in Computer Science; No. 8675).

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  • Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics

    Hennig, P. & Hauberg, S., 2014, Proceedings of the 17th International Conference on Artifficial Intelligence and Statistics (AISTATS 2014). p. 347-355 (JMLR: Workshop and Conference Proceedings, Vol. 33).

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