Martius, Georg S., et al. “Self-Organized Control of an Tendon Driven Arm by Differential Extrinsic Plasticity.”10.7551/978-0-262-33936-0-ch029__Proceedings of the Artificial Life Conference 2016, vol. 28, MIT Press, 2016, pp. 142–43, doi:10.7551/978-0-262-33936-0-ch029.
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

Pentina, Anastasia. Theoretical Foundations of Multi-Task Lifelong Learning. Institute of Science and Technology Austria, 2016, doi:10.15479/AT:ISTA:TH_776.
LibreCat: https://research-explorer.ista.ac.at/record/1126
DOI: 10.15479/AT:ISTA:TH_776

Pentina, Anastasia, and Christoph Lampert. Lifelong Learning with Non-i.i.d. Tasks. Vol. 2015, Neural Information Processing Systems, 2015, pp. 1540–48.
LibreCat: https://research-explorer.ista.ac.at/record/1425

Xia, Wei, et al. “Segmentation over Detection via Optimal Sparse Reconstructions.”10.1109/TCSVT.2014.2379972__IEEE Transactions on Circuits and Systems for Video Technology, vol. 25, no. 8, IEEE, 2015, pp. 1295–308, doi:10.1109/TCSVT.2014.2379972.
LibreCat: https://research-explorer.ista.ac.at/record/1533
DOI: 10.1109/TCSVT.2014.2379972

Der, Ralf, and Georg S. Martius. “Novel Plasticity Rule Can Explain the Development of Sensorimotor Intelligence.”10.1073/pnas.1508400112__PNAS, vol. 112, no. 45, National Academy of Sciences, 2015, pp. 24–32, doi:10.1073/pnas.1508400112.
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Pentina, Anastasia, and Shai Ben David. Multi-Task and Lifelong Learning of Kernels. Vol. 9355, Springer, 2015, pp. 194–208, doi:10.1007/978-3-319-24486-0_13.
LibreCat: https://research-explorer.ista.ac.at/record/1706
DOI: 10.1007/978-3-319-24486-0_13

Shah, Neel, et al. A Multi-Plane Block-Coordinate Frank-Wolfe Algorithm for Training Structural SVMs with a Costly Max-Oracle. IEEE, 2015, pp. 2737–45, doi:10.1109/CVPR.2015.7298890.
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Royer, Amélie, and Christoph Lampert. Classifier Adaptation at Prediction Time. IEEE, 2015, pp. 1401–09, doi:10.1109/CVPR.2015.7298746.
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

Lampert, Christoph. Predicting the Future Behavior of a Time-Varying Probability Distribution. IEEE, 2015, pp. 942–50, doi:10.1109/CVPR.2015.7298696.
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

Pentina, Anastasia, et al. Curriculum Learning of Multiple Tasks. IEEE, 2015, pp. 5492–500, doi:10.1109/CVPR.2015.7299188.
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

Martius, Georg S., and Eckehard Olbrich. “Quantifying Self-Organizing Behavior of Autonomous Robots.”10.7551/978-0-262-33027-5-ch018__Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78, doi:10.7551/978-0-262-33027-5-ch018.
LibreCat: https://research-explorer.ista.ac.at/record/12881
DOI: 10.7551/978-0-262-33027-5-ch018

Sharmanska, Viktoriia. Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views. Institute of Science and Technology Austria, 2015, doi:10.15479/at:ista:1401.
LibreCat: https://research-explorer.ista.ac.at/record/1401
DOI: 10.15479/at:ista:1401

Martius, Georg S., and Eckehard Olbrich. “Quantifying Emergent Behavior of Autonomous Robots.”10.3390/e17107266__Entropy, vol. 17, no. 10, MDPI, 2015, pp. 7266–97, doi:10.3390/e17107266.
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 10.3390/e17107266

Muelling, Katharina, et al. “Movement Templates for Learning of Hitting and Batting.”10.1007/978-3-319-03194-1_3__Learning Motor Skills, edited by Jens Kober and Jan Peters, vol. 97, Springer, 2014, pp. 69–82, doi:10.1007/978-3-319-03194-1_3.
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 10.1007/978-3-319-03194-1_3

Hernandez Lobato, Daniel, et al. “Mind the Nuisance: Gaussian Process Classification Using Privileged Noise.” Advances in Neural Information Processing Systems, vol. 1, no. January, Neural Information Processing Systems, 2014, pp. 837–45.
LibreCat: https://research-explorer.ista.ac.at/record/2033

Morvant, Emilie, et al. “Majority Vote of Diverse Classifiers for Late Fusion.”10.1007/978-3-662-44415-3_16__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 8621, Springer, 2014, pp. 153–62, doi:10.1007/978-3-662-44415-3_16.
LibreCat: https://research-explorer.ista.ac.at/record/2057
DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

Kolesnikov, Alexander, et al. “Closed-Form Approximate CRF Training for Scalable Image Segmentation.”10.1007/978-3-319-10578-9_36__Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), edited by David Fleet et al., vol. 8691, no. PART 3, Springer, 2014, pp. 550–65, doi:10.1007/978-3-319-10578-9_36.
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

Khamis, Sameh, and Christoph Lampert. “CoConut: Co-Classification with Output Space Regularization.” Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2173

Sydorov, Vladyslav, et al. “Deep Fisher Kernels – End to End Learning of the Fisher Kernel GMM Parameters.”10.1109/CVPR.2014.182__Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–09, doi:10.1109/CVPR.2014.182.
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182

Bellet, Aurélien, et al. “Learning a Priori Constrained Weighted Majority Votes.”10.1007/s10994-014-5462-z__Machine Learning, vol. 97, no. 1–2, Springer, 2014, pp. 129–54, doi:10.1007/s10994-014-5462-z.
LibreCat: https://research-explorer.ista.ac.at/record/2180
DOI: 10.1007/s10994-014-5462-z