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114 Publications
2016 | Conference Paper | IST-REx-ID: 8094 | 
G. S. Martius, R. Hostettler, A. Knoll, and R. Der, “Self-organized control of an tendon driven arm by differential extrinsic plasticity,” in Proceedings of the Artificial Life Conference 2016, Cancun, Mexico, 2016, vol. 28, pp. 142–143.
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2016 | Thesis | IST-REx-ID: 1126 |
A. Pentina, “Theoretical foundations of multi-task lifelong learning,” Institute of Science and Technology Austria, 2016.
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2015 | Conference Paper | IST-REx-ID: 1425 |
A. Pentina and C. Lampert, “Lifelong learning with non-i.i.d. tasks,” presented at the NIPS: Neural Information Processing Systems, Montreal, Canada, 2015, vol. 2015, pp. 1540–1548.
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2015 | Journal Article | IST-REx-ID: 1570 |
R. Der and G. S. Martius, “Novel plasticity rule can explain the development of sensorimotor intelligence,” PNAS, vol. 112, no. 45. National Academy of Sciences, pp. E6224–E6232, 2015.
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| PubMed | Europe PMC
2015 | Conference Paper | IST-REx-ID: 1706 |
A. Pentina and S. Ben David, “Multi-task and lifelong learning of kernels,” presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada, 2015, vol. 9355, pp. 194–208.
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2015 | Conference Paper | IST-REx-ID: 1859 |
N. Shah, V. Kolmogorov, and C. Lampert, “A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA, 2015, pp. 2737–2745.
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2015 | Conference Paper | IST-REx-ID: 1860 |
A. Royer and C. Lampert, “Classifier adaptation at prediction time,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 1401–1409.
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2015 | Conference Paper | IST-REx-ID: 1858 |
C. Lampert, “Predicting the future behavior of a time-varying probability distribution,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 942–950.
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| arXiv
2015 | Conference Paper | IST-REx-ID: 1857 |
A. Pentina, V. Sharmanska, and C. Lampert, “Curriculum learning of multiple tasks,” presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States, 2015, pp. 5492–5500.
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2015 | Conference Paper | IST-REx-ID: 12881 |
G. S. Martius and E. Olbrich, “Quantifying self-organizing behavior of autonomous robots,” in Proceedings of the 13th European Conference on Artificial Life, York, United Kingdom, 2015, p. 78.
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2015 | Thesis | IST-REx-ID: 1401 |
V. Sharmanska, “Learning with attributes for object recognition: Parametric and non-parametrics views,” Institute of Science and Technology Austria, 2015.
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2015 | Journal Article | IST-REx-ID: 1655 |
G. S. Martius and E. Olbrich, “Quantifying emergent behavior of autonomous robots,” Entropy, vol. 17, no. 10. MDPI, pp. 7266–7297, 2015.
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2014 | Conference Paper | IST-REx-ID: 2033 |
D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, and N. Quadrianto, “Mind the nuisance: Gaussian process classification using privileged noise,” in Advances in Neural Information Processing Systems, Montreal, Canada, 2014, vol. 1, no. January, pp. 837–845.
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2014 | Conference Paper | IST-REx-ID: 2057 |
E. Morvant, A. Habrard, and S. Ayache, “Majority vote of diverse classifiers for late fusion,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Joensuu, Finland, 2014, vol. 8621, pp. 153–162.
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| arXiv
2014 | Conference Paper | IST-REx-ID: 2171 |
A. Kolesnikov, M. Guillaumin, V. Ferrari, and C. Lampert, “Closed-form approximate CRF training for scalable image segmentation,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Zurich, Switzerland, 2014, vol. 8691, no. PART 3, pp. 550–565.
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2014 | Conference Paper | IST-REx-ID: 2173 |
S. Khamis and C. Lampert, “CoConut: Co-classification with output space regularization,” in Proceedings of the British Machine Vision Conference 2014, Nottingham, UK, 2014.
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2014 | Conference Paper | IST-REx-ID: 2172
V. Sydorov, M. Sakurada, and C. Lampert, “Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters,” in Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Columbus, USA, 2014, pp. 1402–1409.
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2014 | Journal Article | IST-REx-ID: 2180 |
A. Bellet, A. Habrard, E. Morvant, and M. Sebban, “Learning a priori constrained weighted majority votes,” Machine Learning, vol. 97, no. 1–2. Springer, pp. 129–154, 2014.
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