Optimal geospatial allocation of volunteers for crisis management
J. Pielorz, C. Lampert, in:, IEEE, 2016.
LibreCat: https://research-explorer.ista.ac.at/record/1707
DOI: 10.1109/ICT-DM.2015.7402041

Self-organized control of an tendon driven arm by differential extrinsic plasticity
G.S. Martius, R. Hostettler, A. Knoll, R. Der, in:, Proceedings of the Artificial Life Conference 2016, MIT Press, 2016, pp. 142–143.
LibreCat: https://research-explorer.ista.ac.at/record/8094
DOI: 10.7551/978-0-262-33936-0-ch029

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

Lifelong learning with non-i.i.d. tasks
A. Pentina, C. Lampert, in:, Neural Information Processing Systems, 2015, pp. 1540–1548.
LibreCat: https://research-explorer.ista.ac.at/record/1425

Segmentation over detection via optimal sparse reconstructions
W. Xia, C. Domokos, J. Xiong, L. Cheong, S. Yan, IEEE Transactions on Circuits and Systems for Video Technology 25 (2015) 1295–1308.
LibreCat: https://research-explorer.ista.ac.at/record/1533
DOI: 10.1109/TCSVT.2014.2379972

Novel plasticity rule can explain the development of sensorimotor intelligence
R. Der, G.S. Martius, PNAS 112 (2015) 24–32.
LibreCat: https://research-explorer.ista.ac.at/record/1570
DOI: 10.1073/pnas.1508400112
PMID: 26504200

Multi-task and lifelong learning of kernels
A. Pentina, S. Ben David, in:, Springer, 2015, pp. 194–208.
LibreCat: https://research-explorer.ista.ac.at/record/1706
DOI: 10.1007/978-3-319-24486-0_13

A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle
N. Shah, V. Kolmogorov, C. Lampert, in:, IEEE, 2015, pp. 2737–2745.
LibreCat: https://research-explorer.ista.ac.at/record/1859
DOI: 10.1109/CVPR.2015.7298890

Classifier adaptation at prediction time
A. Royer, C. Lampert, in:, IEEE, 2015, pp. 1401–1409.
LibreCat: https://research-explorer.ista.ac.at/record/1860
DOI: 10.1109/CVPR.2015.7298746

Predicting the future behavior of a time-varying probability distribution
C. Lampert, in:, IEEE, 2015, pp. 942–950.
LibreCat: https://research-explorer.ista.ac.at/record/1858
DOI: 10.1109/CVPR.2015.7298696
arXiv: 1406.5362

Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, C. Lampert, in:, IEEE, 2015, pp. 5492–5500.
LibreCat: https://research-explorer.ista.ac.at/record/1857
DOI: 10.1109/CVPR.2015.7299188

Quantifying self-organizing behavior of autonomous robots
G.S. Martius, E. Olbrich, in:, Proceedings of the 13th European Conference on Artificial Life, MIT Press, 2015, p. 78.
LibreCat: https://research-explorer.ista.ac.at/record/12881
DOI: 10.7551/978-0-262-33027-5-ch018

Learning with attributes for object recognition: Parametric and non-parametrics views
V. Sharmanska, Learning with Attributes for Object Recognition: Parametric and Non-Parametrics Views, Institute of Science and Technology Austria, 2015.
LibreCat: https://research-explorer.ista.ac.at/record/1401
DOI: 10.15479/at:ista:1401

Quantifying emergent behavior of autonomous robots
G.S. Martius, E. Olbrich, Entropy 17 (2015) 7266–7297.
LibreCat: https://research-explorer.ista.ac.at/record/1655
DOI: 10.3390/e17107266

Movement templates for learning of hitting and batting
K. Muelling, O. Kroemer, C. Lampert, B. Schölkopf, in:, J. Kober, J. Peters (Eds.), Learning Motor Skills, Springer, 2014, pp. 69–82.
LibreCat: https://research-explorer.ista.ac.at/record/1829
DOI: 10.1007/978-3-319-03194-1_3

Mind the nuisance: Gaussian process classification using privileged noise
D. Hernandez Lobato, V. Sharmanska, K. Kersting, C. Lampert, N. Quadrianto, in:, Advances in Neural Information Processing Systems, Neural Information Processing Systems, 2014, pp. 837–845.
LibreCat: https://research-explorer.ista.ac.at/record/2033

Majority vote of diverse classifiers for late fusion
E. Morvant, A. Habrard, S. Ayache, in:, Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 153–162.
LibreCat: https://research-explorer.ista.ac.at/record/2057
DOI: 10.1007/978-3-662-44415-3_16
arXiv: 1404.7796

Closed-form approximate CRF training for scalable image segmentation
A. Kolesnikov, M. Guillaumin, V. Ferrari, C. Lampert, in:, D. Fleet, T. Pajdla, B. Schiele, T. Tuytelaars (Eds.), Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer, 2014, pp. 550–565.
LibreCat: https://research-explorer.ista.ac.at/record/2171
DOI: 10.1007/978-3-319-10578-9_36

CoConut: Co-classification with output space regularization
S. Khamis, C. Lampert, in:, Proceedings of the British Machine Vision Conference 2014, BMVA Press, 2014.
LibreCat: https://research-explorer.ista.ac.at/record/2173

Deep Fisher Kernels – End to end learning of the Fisher Kernel GMM parameters
V. Sydorov, M. Sakurada, C. Lampert, in:, Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 2014, pp. 1402–1409.
LibreCat: https://research-explorer.ista.ac.at/record/2172
DOI: 10.1109/CVPR.2014.182