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72 Publications

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. (n.d.). Robust learning from untrusted sources. In Proceedings of the 36th International Conference on Machine Learning. Long Beach, CA, USA.
View | Download (ext.) | arXiv
 
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
View | DOI | Download (ext.) | arXiv
 
2018 | Journal Article | IST-REx-ID: 321
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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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.
View | Download (ext.) | arXiv
 
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.
View | Download (ext.) | arXiv
 
2018 | Journal Article | IST-REx-ID: 6554   OA
Xian, Y., Lampert, C., Schiele, B., & Akata, Z. (2018). Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1–1. https://doi.org/10.1109/tpami.2018.2857768
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2018 | Conference Paper | IST-REx-ID: 6012   OA
Sahoo, S., Lampert, C., & Martius, G. (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.
View | Files available | Download (ext.) | arXiv
 
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 | Journal Article | IST-REx-ID: 563
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 | Research Data | IST-REx-ID: 5584   OA
Deny, S., Marre, O., Botella-Soler, V., Martius, G. S., & Tkacik, 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 | 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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2017 | Conference Paper | IST-REx-ID: 911   OA
Royer, A., Kolesnikov, A., & Lampert, C. (n.d.). Probabilistic image colorization. Presented at the BMVC: British Machine Vision Conference, London, United Kingdom: BMVA Press.
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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: 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 | 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 | 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
View | Files available | DOI
 
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: 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: 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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