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88 Publications

2020 | Conference Paper | IST-REx-ID: 7481 | OA
Bui Thi Mai, P., & Lampert, C. (n.d.). Functional vs. parametric equivalence of ReLU networks.
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2020 | Preprint | IST-REx-ID: 8063 | OA
Anciukevicius, T., Lampert, C., & Henderson, P. M. (n.d.). Object-centric image generation with factored depths, locations, and appearances. ArXiv. ArXiv.
View | Download Preprint (ext.) | arXiv
 
2020 | Conference Paper | IST-REx-ID: 8186 | OA
Henderson, P. M., Tsiminaki, V., & Lampert, C. (2020). Leveraging 2D data to learn textured 3D mesh generation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 7498–7507). Virtual: CVF.
View | Files available | Download Submitted Version (ext.) | arXiv
 
2020 | Thesis | IST-REx-ID: 8390 | OA
Royer, A. (2020). Leveraging structure in Computer Vision tasks for flexible Deep Learning models. IST Austria. https://doi.org/10.15479/AT:ISTA:8390
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2020 | Preprint | IST-REx-ID: 8188 | OA
Henderson, P. M., & Lampert, C. (n.d.). Unsupervised object-centric video generation and decomposition in 3D. ArXiv.
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2020 | Conference Paper | IST-REx-ID: 7936 | OA
Royer, A., & Lampert, C. (2020). Localizing grouped instances for efficient detection in low-resource scenarios. In IEEE Winter Conference on Applications of Computer Vision (pp. 1716–1725). Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093288
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2020 | Conference Paper | IST-REx-ID: 7937 | OA
Royer, A., & Lampert, C. (2020). A flexible selection scheme for minimum-effort transfer learning. In 2020 IEEE Winter Conference on Applications of Computer Vision. Snowmass Village, CO, United States: IEEE. https://doi.org/10.1109/WACV45572.2020.9093635
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2020 | Book Chapter | IST-REx-ID: 8092 | OA
Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Mosseri, I., Cole, F., & Murphy, K. (2020). XGAN: Unsupervised image-to-image translation for many-to-many mappings. In R. Singh, M. Vatsa, V. M. Patel, & N. Ratha (Eds.), Domain Adaptation for Visual Understanding (pp. 33–49). Springer Nature. https://doi.org/10.1007/978-3-030-30671-7_3
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2019 | Conference Paper | IST-REx-ID: 7479 | OA
Bui Thi Mai, P., & Lampert, C. (2019). Distillation-based training for multi-exit architectures. In IEEE International Conference on Computer Vision (Vol. 2019–October, pp. 1355–1364). Seoul, Korea: IEEE. https://doi.org/10.1109/ICCV.2019.00144
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2019 | Conference Paper | IST-REx-ID: 7640 | OA
Kolesnikov, A., Kuznetsova, A., Lampert, C., & Ferrari, V. (2019). Detecting visual relationships using box attention. In Proceedings of the 2019 International Conference on Computer Vision Workshop (pp. 1749–1753). Seoul, South Korea: IEEE. https://doi.org/10.1109/ICCVW.2019.00217
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2019 | Conference Paper | IST-REx-ID: 6482 | OA
Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a ConvNet operates outside of Its specifications (Vol. 11269, pp. 244–259). Presented at the GCPR: Conference on Pattern Recognition, Stuttgart, Germany: Springer Nature. https://doi.org/10.1007/978-3-030-12939-2_18
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2019 | Journal Article | IST-REx-ID: 6554 | OA
Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2019). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(9), 2251–2265. https://doi.org/10.1109/tpami.2018.2857768
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2019 | Conference Paper | IST-REx-ID: 6569 | OA
Bui Thi Mai, P., & Lampert, C. (2019). Towards understanding knowledge distillation. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 5142–5151). Long Beach, CA, United States: PMLR.
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2019 | Conference Paper | IST-REx-ID: 6590 | OA
Konstantinov, N. H., & Lampert, C. (2019). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning (Vol. 97, pp. 3488–3498). Long Beach, CA, USA: PMLR.
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2019 | Conference Paper | IST-REx-ID: 6942 | OA
Ashok, P., Brázdil, T., Chatterjee, K., Křetínský, J., Lampert, C., & Toman, V. (2019). Strategy representation by decision trees with linear classifiers. In 16th International Conference on Quantitative Evaluation of Systems (Vol. 11785, pp. 109–128). Glasgow, United Kingdom: Springer Nature. https://doi.org/10.1007/978-3-030-30281-8_7
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2019 | Journal Article | IST-REx-ID: 6944 | OA
Sun, R., & Lampert, C. (2019). KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01232-x
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2019 | Journal Article | IST-REx-ID: 6952 | OA
Henderson, P. M., & Ferrari, V. (2019). Learning single-image 3D reconstruction by generative modelling of shape, pose and shading. International Journal of Computer Vision. https://doi.org/10.1007/s11263-019-01219-8
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2019 | Book (Editor) | IST-REx-ID: 7171
Kersting, K., Lampert, C., & Rothkopf, C. (Eds.). (2019). Wie Maschinen lernen. Springer Nature. https://doi.org/10.1007/978-3-658-26763-6
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2018 | Research Data | IST-REx-ID: 5584 | OA
Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkačik, G. (2018). Nonlinear decoding of a complex movie from the mammalian retina. IST Austria. https://doi.org/10.15479/AT:ISTA:98
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2018 | Journal Article | IST-REx-ID: 563 | OA
Ringbauer, H., Kolesnikov, A., Field, D., & Barton, N. H. (2018). Estimating barriers to gene flow from distorted isolation-by-distance patterns. Genetics, 208(3), 1231–1245. https://doi.org/10.1534/genetics.117.300638
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2018 | Conference Paper | IST-REx-ID: 6011 | OA
Kuzborskij, I., & Lampert, C. (2018). Data-dependent stability of stochastic gradient descent. In Proceedings of the 35 th International Conference on Machine Learning (Vol. 80, pp. 2815–2824). Stockholm, Sweden: International Machine Learning Society.
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2018 | Conference Paper | IST-REx-ID: 6012 | OA
Sahoo, S., Lampert, C., & Martius, G. S. (2018). Learning equations for extrapolation and control. In Proceedings of the 35th International Conference on Machine Learning (Vol. 80, pp. 4442–4450). Stockholm, Sweden: International Machine Learning Society.
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2018 | Conference Paper | IST-REx-ID: 6589 | OA
Alistarh, D.-A., Hoefler, T., Johansson, M., Konstantinov, N. H., Khirirat, S., & Renggli, C. (2018). The convergence of sparsified gradient methods. In Advances in Neural Information Processing Systems 31 (Vol. Volume 2018, pp. 5973–5983). Montreal, Canada: Neural information processing systems.
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2018 | Thesis | IST-REx-ID: 68 | OA
Zimin, A. (2018). Learning from dependent data. IST Austria. https://doi.org/10.15479/AT:ISTA:TH1048
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2018 | Thesis | IST-REx-ID: 197 | OA
Kolesnikov, A. (2018). Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images. IST Austria. https://doi.org/10.15479/AT:ISTA:th_1021
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2018 | Journal Article | IST-REx-ID: 321 | OA
Darrell, T., Lampert, C., Sebe, N., Wu, Y., & Yan, Y. (2018). 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, 40(5), 1029–1031. https://doi.org/10.1109/TPAMI.2018.2804998
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2017 | Conference Paper | IST-REx-ID: 750
Pielorz, J., Prandtstetter, M., Straub, M., & Lampert, C. (2017). Optimal geospatial volunteer allocation needs realistic distances. In 2017 IEEE International Conference on Big Data (pp. 3760–3763). Boston, MA, United States: IEEE. https://doi.org/10.1109/BigData.2017.8258375
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2017 | Conference Paper | IST-REx-ID: 652
Der, R., & Martius, G. S. (2017). Dynamical self consistency leads to behavioral development and emergent social interactions in robots. Presented at the ICDL EpiRob: International Conference on Development and Learning and Epigenetic Robotics , Cergy-Pontoise, France: IEEE. https://doi.org/10.1109/DEVLRN.2016.7846789
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2017 | Journal Article | IST-REx-ID: 658 | OA
Der, R., & Martius, G. S. (2017). Self organized behavior generation for musculoskeletal robots. Frontiers in Neurorobotics, 11(MAR). https://doi.org/10.3389/fnbot.2017.00008
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2017 | Conference Paper | IST-REx-ID: 6841 | OA
Martius, G. S., & Lampert, C. (2017). Extrapolation and learning equations. In 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings. Toulon, France: International Conference on Learning Representations.
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2017 | Conference Paper | IST-REx-ID: 998 | OA
Rebuffi, S. A., Kolesnikov, A., Sperl, G., & Lampert, C. (2017). iCaRL: Incremental classifier and representation learning (Vol. 2017, pp. 5533–5542). Presented at the CVPR: Computer Vision and Pattern Recognition, Honolulu, HA, United States: IEEE. https://doi.org/10.1109/CVPR.2017.587
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2017 | Conference Paper | IST-REx-ID: 999 | OA
Pentina, A., & Lampert, C. (2017). Multi-task learning with labeled and unlabeled tasks (Vol. 70, pp. 2807–2816). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: Omnipress.
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2017 | Conference Paper | IST-REx-ID: 1000 | OA
Kolesnikov, A., & Lampert, C. (2017). PixelCNN models with auxiliary variables for natural image modeling (Vol. 70, pp. 1905–1914). Presented at the ICML: International Conference on Machine Learning, Sydney, Australia: Omnipress.
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2017 | Conference Paper | IST-REx-ID: 1108 | OA
Zimin, A., & Lampert, C. (2017). Learning theory for conditional risk minimization (Vol. 54, pp. 213–222). Presented at the AISTATS: Artificial Intelligence and Statistics, Fort Lauderdale, FL, United States: JMLR, Inc. and Microtome Publishing.
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2017 | Conference Paper | IST-REx-ID: 911 | OA
Royer, A., Kolesnikov, A., & Lampert, C. (2017). Probabilistic image colorization (p. 85.1-85.12). Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press. https://doi.org/10.5244/c.31.85
View | Files available | DOI | arXiv
 
2016 | Conference Paper | IST-REx-ID: 1707
Pielorz, J., & Lampert, C. (2016). Optimal geospatial allocation of volunteers for crisis management. Presented at the ICT-DM: Information and Communication Technologies for Disaster Management, Rennes, France: IEEE. https://doi.org/10.1109/ICT-DM.2015.7402041
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2016 | Conference Paper | IST-REx-ID: 8094 | OA
Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Self-organized control of an tendon driven arm by differential extrinsic plasticity. In Proceedings of the Artificial Life Conference 2016 (Vol. 28, pp. 142–143). Cancun, Mexico: MIT Press. https://doi.org/10.7551/978-0-262-33936-0-ch029
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2016 | Conference Paper | IST-REx-ID: 1214
Martius, G. S., Hostettler, R., Knoll, A., & Der, R. (2016). Compliant control for soft robots: Emergent behavior of a tendon driven anthropomorphic arm (Vol. 2016–November). Presented at the IEEE RSJ International Conference on Intelligent Robots and Systems IROS , Daejeon, Korea: IEEE. https://doi.org/10.1109/IROS.2016.7759138
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2016 | Conference Paper | IST-REx-ID: 1369 | OA
Kolesnikov, A., & Lampert, C. (2016). Seed, expand and constrain: Three principles for weakly-supervised image segmentation (Vol. 9908, pp. 695–711). Presented at the ECCV: European Conference on Computer Vision, Amsterdam, The Netherlands: Springer. https://doi.org/10.1007/978-3-319-46493-0_42
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2016 | Conference Paper | IST-REx-ID: 1098 | OA
Pentina, A., & Urner, R. (2016). Lifelong learning with weighted majority votes (Vol. 29, pp. 3619–3627). Presented at the NIPS: Neural Information Processing Systems, Barcelona, Spain: Neural Information Processing Systems.
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2016 | Conference Paper | IST-REx-ID: 1102 | OA
Kolesnikov, A., & Lampert, C. (2016). Improving weakly-supervised object localization by micro-annotation. In Proceedings of the British Machine Vision Conference 2016 (Vol. 2016–September, p. 92.1-92.12). York, United Kingdom: BMVA Press. https://doi.org/10.5244/C.30.92
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2016 | Thesis | IST-REx-ID: 1126 | OA
Pentina, A. (2016). Theoretical foundations of multi-task lifelong learning. IST Austria. https://doi.org/10.15479/AT:ISTA:TH_776
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2015 | Journal Article | IST-REx-ID: 1570 | OA
Der, R., & Martius, G. S. (2015). Novel plasticity rule can explain the development of sensorimotor intelligence. PNAS, 112(45), E6224–E6232. https://doi.org/10.1073/pnas.1508400112
View | DOI | Download Submitted Version (ext.) | PubMed | Europe PMC
 
2015 | Journal Article | IST-REx-ID: 1655 | OA
Martius, G. S., & Olbrich, E. (2015). Quantifying emergent behavior of autonomous robots. Entropy, 17(10), 7266–7297. https://doi.org/10.3390/e17107266
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2015 | Conference Paper | IST-REx-ID: 1706 | OA
Pentina, A., & Ben David, S. (2015). Multi-task and lifelong learning of kernels (Vol. 9355, pp. 194–208). Presented at the ALT: Algorithmic Learning Theory, Banff, AB, Canada: Springer. https://doi.org/10.1007/978-3-319-24486-0_13
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2015 | Conference Paper | IST-REx-ID: 1857 | OA
Pentina, A., Sharmanska, V., & Lampert, C. (2015). Curriculum learning of multiple tasks (pp. 5492–5500). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7299188
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2015 | Conference Paper | IST-REx-ID: 1858 | OA
Lampert, C. (2015). Predicting the future behavior of a time-varying probability distribution (pp. 942–950). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298696
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2015 | Conference Paper | IST-REx-ID: 1859 | OA
Shah, N., Kolmogorov, V., & Lampert, C. (2015). A multi-plane block-coordinate Frank-Wolfe algorithm for training structural SVMs with a costly max-oracle (pp. 2737–2745). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, USA: IEEE. https://doi.org/10.1109/CVPR.2015.7298890
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2015 | Conference Paper | IST-REx-ID: 1860 | OA
Royer, A., & Lampert, C. (2015). Classifier adaptation at prediction time (pp. 1401–1409). Presented at the CVPR: Computer Vision and Pattern Recognition, Boston, MA, United States: IEEE. https://doi.org/10.1109/CVPR.2015.7298746
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2015 | Thesis | IST-REx-ID: 1401
Sharmanska, V. (2015). Learning with attributes for object recognition: Parametric and non-parametrics views. IST Austria.
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2015 | Conference Paper | IST-REx-ID: 1425 | OA
Pentina, A., & Lampert, C. (2015). Lifelong learning with non-i.i.d. tasks (Vol. 2015, pp. 1540–1548). Presented at the NIPS: Neural Information Processing Systems, Montreal, Canada: Neural Information Processing Systems.
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2015 | Journal Article | IST-REx-ID: 1533
Xia, W., Domokos, C., Xiong, J., Cheong, L., & Yan, S. (2015). Segmentation over detection via optimal sparse reconstructions. IEEE Transactions on Circuits and Systems for Video Technology, 25(8), 1295–1308. https://doi.org/10.1109/TCSVT.2014.2379972
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2014 | Conference Paper | IST-REx-ID: 2033 | OA
Hernandez Lobato, D., Sharmanska, V., Kersting, K., Lampert, C., & Quadrianto, N. (2014). Mind the nuisance: Gaussian process classification using privileged noise. In Advances in Neural Information Processing Systems (Vol. 1, pp. 837–845). Montreal, Canada: Neural Information Processing Systems.
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2014 | Conference Paper | IST-REx-ID: 2057 | OA
Morvant, E., Habrard, A., & Ayache, S. (2014). 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) (Vol. 8621, pp. 153–162). Joensuu, Finland: Springer. https://doi.org/10.1007/978-3-662-44415-3_16
View | DOI | Download Preprint (ext.) | arXiv
 
2014 | Conference Paper | IST-REx-ID: 2160 | OA
Pentina, A., & Lampert, C. (2014). A PAC-Bayesian bound for Lifelong Learning. In E. Xing & T. Jebara (Eds.) (Vol. 32, pp. 991–999). Presented at the ICML: International Conference on Machine Learning, Beijing, China: Omnipress.
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2014 | Conference Paper | IST-REx-ID: 2171 | OA
Kolesnikov, A., Guillaumin, M., Ferrari, V., & Lampert, C. (2014). Closed-form approximate CRF training for scalable image segmentation. 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) (Vol. 8691, pp. 550–565). Zurich, Switzerland: Springer. https://doi.org/10.1007/978-3-319-10578-9_36
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2014 | Conference Paper | IST-REx-ID: 2172
Sydorov, V., Sakurada, M., & Lampert, C. (2014). 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 (pp. 1402–1409). Columbus, USA: IEEE. https://doi.org/10.1109/CVPR.2014.182
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2014 | Conference Paper | IST-REx-ID: 2173 | OA
Khamis, S., & Lampert, C. (2014). CoConut: Co-classification with output space regularization. In Proceedings of the British Machine Vision Conference 2014. Nottingham, UK: BMVA Press.
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2014 | Journal Article | IST-REx-ID: 2180 | OA
Bellet, A., Habrard, A., Morvant, E., & Sebban, M. (2014). Learning a priori constrained weighted majority votes. Machine Learning, 97(1–2), 129–154. https://doi.org/10.1007/s10994-014-5462-z
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2014 | Conference Paper | IST-REx-ID: 2189 | OA
Morvant, E. (2014). Adaptation de domaine de vote de majorité par auto-étiquetage non itératif (Vol. 1, pp. 49–58). Presented at the CAP: Conférence Francophone sur l’Apprentissage Automatique (Machine Learning French Conference), Saint-Etienne, France: Elsevier.
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2014 | Book Chapter | IST-REx-ID: 1829
Muelling, K., Kroemer, O., Lampert, C., & Schölkopf, B. (2014). Movement templates for learning of hitting and batting. In J. Kober & J. Peters (Eds.), Learning Motor Skills (Vol. 97, pp. 69–82). Springer. https://doi.org/10.1007/978-3-319-03194-1_3
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2013 | Conference Paper | IST-REx-ID: 2293 | OA
Sharmanska, V., Quadrianto, N., & Lampert, C. (2013). Learning to rank using privileged information (pp. 825–832). Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.107
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2013 | Conference Paper | IST-REx-ID: 2294 | OA
Kazmar, T., Kvon, E., Stark, A., & Lampert, C. (2013). Drosophila Embryo Stage Annotation using Label Propagation. Presented at the ICCV: International Conference on Computer Vision, Sydney, Australia: IEEE. https://doi.org/10.1109/ICCV.2013.139
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2013 | Journal Article | IST-REx-ID: 2516
Lampert, C., Nickisch, H., & Harmeling, S. (2013). Attribute-based classification for zero-shot learning of object categories. IEEE Transactions on Pattern Analysis and Machine Intelligence, 36(3), 453–465. https://doi.org/10.1109/TPAMI.2013.140
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2013 | Conference Paper | IST-REx-ID: 2520 | OA
Quadrianto, N., Sharmanska, V., Knowles, D., & Ghahramani, Z. (2013). The supervised IBP: Neighbourhood preserving infinite latent feature models. In Proceedings of the 29th conference uncertainty in Artificial Intelligence (pp. 527–536). Bellevue, WA, United States: AUAI Press.
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2013 | Conference Paper | IST-REx-ID: 2901 | OA
Chen, C., Kolmogorov, V., Yan, Z., Metaxas, D., & Lampert, C. (2013). Computing the M most probable modes of a graphical model (Vol. 31, pp. 161–169). Presented at the AISTATS: Conference on Uncertainty in Artificial Intelligence, Scottsdale, AZ, United States: JMLR.
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2013 | Conference Paper | IST-REx-ID: 2948 | OA
Tommasi, T., Quadrianto, N., Caputo, B., & Lampert, C. (2013). Beyond dataset bias: Multi-task unaligned shared knowledge transfer. Presented at the ACCV: Asian Conference on Computer Vision, Daejeon, Korea: Springer. https://doi.org/10.1007/978-3-642-37331-2_1
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2013 | Encyclopedia Article | IST-REx-ID: 3321
Quadrianto, N., & Lampert, C. (2013). Kernel based learning. In W. Dubitzky, O. Wolkenhauer, K. Cho, & H. Yokota (Eds.), Encyclopedia of Systems Biology (Vol. 3, pp. 1069–1069). Springer. https://doi.org/10.1007/978-1-4419-9863-7_604
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2012 | Technical Report | IST-REx-ID: 5396 | OA
Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals using discrete energy minimization. IST Austria. https://doi.org/10.15479/AT:IST-2012-0003
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2012 | Conference Paper | IST-REx-ID: 2825
Lampert, C. (2012). Dynamic pruning of factor graphs for maximum marginal prediction (Vol. 1, pp. 82–90). Presented at the NIPS: Neural Information Processing Systems, Lake Tahoe, NV, United States: Neural Information Processing Systems.
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2012 | Conference Paper | IST-REx-ID: 2915
Kroemer, O., Lampert, C., & Peters, J. (2012). Multi-modal learning for dynamic tactile sensing. Deutsches Zentrum für Luft und Raumfahrt.
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2012 | Conference Paper | IST-REx-ID: 3124 | OA
Korc, F., Kolmogorov, V., & Lampert, C. (2012). Approximating marginals using discrete energy minimization. Presented at the ICML: International Conference on Machine Learning, Edinburgh, Scotland: ICML.
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2012 | Conference Paper | IST-REx-ID: 3125 | OA
Sharmanska, V., Quadrianto, N., & Lampert, C. (2012). Augmented attribute representations (Vol. 7576, pp. 242–255). Presented at the ECCV: European Conference on Computer Vision, Florence, Italy: Springer. https://doi.org/10.1007/978-3-642-33715-4_18
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2012 | Conference Paper | IST-REx-ID: 3126
Müller, A., Nowozin, S., & Lampert, C. (2012). Information theoretic clustering using minimal spanning trees (Vol. 7476, pp. 205–215). Presented at the DAGM: German Association For Pattern Recognition, Graz, Austria: Springer. https://doi.org/10.1007/978-3-642-32717-9_21
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2012 | Conference Paper | IST-REx-ID: 3127 | OA
Quadrianto, N., Lampert, C., & Chen, C. (2012). The most persistent soft-clique in a set of sampled graphs. In Proceedings of the 29th International Conference on Machine Learning (pp. 211–218). Edinburgh, United Kingdom: Omnipress.
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2012 | Journal Article | IST-REx-ID: 3164
Blaschko, M., & Lampert, C. (2012). Guest editorial: Special issue on structured prediction and inference. International Journal of Computer Vision, 99(3), 257–258. https://doi.org/10.1007/s11263-012-0530-y
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2012 | Journal Article | IST-REx-ID: 3248 | OA
Lampert, C., & Peters, J. (2012). Real-time detection of colored objects in multiple camera streams with off-the-shelf hardware components. Journal of Real-Time Image Processing, 7(1), 31–41. https://doi.org/10.1007/s11554-010-0168-3
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2011 | Technical Report | IST-REx-ID: 5386 | OA
Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. IST Austria. https://doi.org/10.15479/AT:IST-2011-0002
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2011 | Conference Paper | IST-REx-ID: 3163
Lampert, C. (2011). Maximum margin multi-label structured prediction. Presented at the NIPS: Neural Information Processing Systems, Granada, Spain: Neural Information Processing Systems.
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2011 | Conference Paper | IST-REx-ID: 3319
Quadrianto, N., & Lampert, C. (2011). Learning multi-view neighborhood preserving projections (pp. 425–432). Presented at the ICML: International Conference on Machine Learning, Bellevue, USA: Omnipress.
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2011 | Journal Article | IST-REx-ID: 3320 | OA
Nowozin, S., & Lampert, C. (2011). Structured learning and prediction in computer vision. Foundations and Trends in Computer Graphics and Vision, 6(3–4), 185–365. https://doi.org/10.1561/0600000033
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2011 | Conference Poster | IST-REx-ID: 3322
Lampert, C. (2011). Maximum margin multi label structured prediction. NIPS: Neural Information Processing Systems. Neural Information Processing Systems.
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2011 | Conference Paper | IST-REx-ID: 3336
Chen, C., Freedman, D., & Lampert, C. (2011). Enforcing topological constraints in random field image segmentation. In CVPR: Computer Vision and Pattern Recognition (pp. 2089–2096). Colorado Springs, CO, USA: IEEE. https://doi.org/10.1109/CVPR.2011.5995503
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2011 | Conference Paper | IST-REx-ID: 3337
Wang, Z., Lampert, C., Mülling, K., Schölkopf, B., & Peters, J. (2011). Learning anticipation policies for robot table tennis (pp. 332–337). Presented at the IROS: RSJ International Conference on Intelligent Robots and Systems, San Francisco, USA: IEEE. https://doi.org/10.1109/IROS.2011.6094892
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2011 | Journal Article | IST-REx-ID: 3382
Kroemer, O., Lampert, C., & Peters, J. (2011). Learning dynamic tactile sensing with robust vision based training. IEEE Transactions on Robotics, 27(3), 545–557. https://doi.org/10.1109/TRO.2011.2121130
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2011 | Journal Article | IST-REx-ID: 3389
Blaschko, M., Shelton, J., Bartels, A., Lampert, C., & Gretton, A. (2011). Semi supervised kernel canonical correlation analysis with application to human fMRI. Pattern Recognition Letters, 32(11), 1572–1583. https://doi.org/10.1016/j.patrec.2011.02.011
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2010 | Conference Paper | IST-REx-ID: 3793 | OA
Nowozin, S., Gehler, P., & Lampert, C. (2010). On parameter learning in CRF-based approaches to object class image segmentation (Vol. 6316, pp. 98–111). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15567-3_8
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2010 | Conference Paper | IST-REx-ID: 3794
Lampert, C., & Krömer, O. (2010). Weakly-paired maximum covariance analysis for multimodal dimensionality reduction and transfer learning (Vol. 6312, pp. 566–579). Presented at the ECCV: European Conference on Computer Vision, Heraklion, Crete, Greece: Springer. https://doi.org/10.1007/978-3-642-15552-9_41
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