Darrell T, Lampert C, Sebe N, Wu Y, Yan Y. Guest editors’ introduction to the special section on learning with Shared information for computer vision and multimedia analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2018;40(5):1029-1031. doi:10.1109/TPAMI.2018.2804998
LibreCat: https://research-explorer.ista.ac.at/record/321
DOI: 10.1109/TPAMI.2018.2804998
WoS: 000428901200001

Uijlings J, Konyushkova K, Lampert C, Ferrari V. Learning intelligent dialogs for bounding box annotation. In: 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. IEEE; 2018:9175-9184. doi:10.1109/cvpr.2018.00956
LibreCat: https://research-explorer.ista.ac.at/record/10882
DOI: 10.1109/cvpr.2018.00956
WoS: 000457843609036
arXiv: 1712.08087

Sahoo S, Lampert C, Martius GS. Learning equations for extrapolation and control. In: Proceedings of the 35th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:4442-4450.
LibreCat: https://research-explorer.ista.ac.at/record/6012
WoS: 000683379204058
arXiv: 1806.07259

Kuzborskij I, Lampert C. Data-dependent stability of stochastic gradient descent. In: Proceedings of the 35 Th International Conference on Machine Learning. Vol 80. ML Research Press; 2018:2815-2824.
LibreCat: https://research-explorer.ista.ac.at/record/6011
WoS: 000683379202095
arXiv: 1703.01678

Alistarh D-A, Hoefler T, Johansson M, Konstantinov NH, Khirirat S, Renggli C. The convergence of sparsified gradient methods. In: Advances in Neural Information Processing Systems 31. Vol Volume 2018. Neural Information Processing Systems Foundation; 2018:5973-5983.
LibreCat: https://research-explorer.ista.ac.at/record/6589
WoS: 000461852000047
arXiv: 1809.10505

Deny S, Marre O, Botella-Soler V, Martius GS, Tkadik G. Nonlinear decoding of a complex movie from the mammalian retina. 2018. doi:10.15479/AT:ISTA:98
LibreCat: https://research-explorer.ista.ac.at/record/5584
DOI: 10.15479/AT:ISTA:98

Der R, Martius GS. Dynamical self consistency leads to behavioral development and emergent social interactions in robots. In: IEEE; 2017. doi:10.1109/DEVLRN.2016.7846789
LibreCat: https://research-explorer.ista.ac.at/record/652
DOI: 10.1109/DEVLRN.2016.7846789

Der R, Martius GS. Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics. 2017;11(MAR). doi:10.3389/fnbot.2017.00008
LibreCat: https://research-explorer.ista.ac.at/record/658
DOI: 10.3389/fnbot.2017.00008

Martius GS, Lampert C. Extrapolation and learning equations. In: 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. International Conference on Learning Representations; 2017.
LibreCat: https://research-explorer.ista.ac.at/record/6841
arXiv: 1610.02995

Pielorz J, Prandtstetter M, Straub M, Lampert C. Optimal geospatial volunteer allocation needs realistic distances. In: 2017 IEEE International Conference on Big Data. IEEE; 2017:3760-3763. doi:10.1109/BigData.2017.8258375
LibreCat: https://research-explorer.ista.ac.at/record/750
DOI: 10.1109/BigData.2017.8258375

Kolesnikov A, Lampert C. PixelCNN models with auxiliary variables for natural image modeling. In: 34th International Conference on Machine Learning. Vol 70. JMLR; 2017:1905-1914.
LibreCat: https://research-explorer.ista.ac.at/record/1000
WoS: 000683309501102
arXiv: 1612.08185

Rebuffi SA, Kolesnikov A, Sperl G, Lampert C. iCaRL: Incremental classifier and representation learning. In: Vol 2017. IEEE; 2017:5533-5542. doi:10.1109/CVPR.2017.587
LibreCat: https://research-explorer.ista.ac.at/record/998
DOI: 10.1109/CVPR.2017.587
WoS: 000418371405066

Royer A, Kolesnikov A, Lampert C. Probabilistic image colorization. In: BMVA Press; 2017:85.1-85.12. doi:10.5244/c.31.85
LibreCat: https://research-explorer.ista.ac.at/record/911
DOI: 10.5244/c.31.85
arXiv: 1705.04258

Zimin A, Lampert C. Learning theory for conditional risk minimization. In: Vol 54. ML Research Press; 2017:213-222.
LibreCat: https://research-explorer.ista.ac.at/record/1108
WoS: 000509368500024

Pentina A, Lampert C. Multi-task learning with labeled and unlabeled tasks. In: Vol 70. ML Research Press; 2017:2807-2816.
LibreCat: https://research-explorer.ista.ac.at/record/999
WoS: 000683309502093

Pentina A, Urner R. Lifelong learning with weighted majority votes. In: Vol 29. Neural Information Processing Systems; 2016:3619-3627.
LibreCat: https://research-explorer.ista.ac.at/record/1098

Kolesnikov A, Lampert C. Improving weakly-supervised object localization by micro-annotation. In: Proceedings of the British Machine Vision Conference 2016. Vol 2016-September. BMVA Press; 2016:92.1-92.12. doi:10.5244/C.30.92
LibreCat: https://research-explorer.ista.ac.at/record/1102
DOI: 10.5244/C.30.92

Martius GS, Hostettler R, Knoll A, Der R. Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm. In: Vol 2016-November. IEEE; 2016. doi:10.1109/IROS.2016.7759138
LibreCat: https://research-explorer.ista.ac.at/record/1214
DOI: 10.1109/IROS.2016.7759138

Kolesnikov A, Lampert C. Seed, expand and constrain: Three principles for weakly-supervised image segmentation. In: Vol 9908. Springer; 2016:695-711. doi:10.1007/978-3-319-46493-0_42
LibreCat: https://research-explorer.ista.ac.at/record/1369
DOI: 10.1007/978-3-319-46493-0_42

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