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76 Publications

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 | 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
View | DOI | Download (ext.) | arXiv
 
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 | 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 | Conference Paper | IST-REx-ID: 6482
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 | Files available | 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
View | DOI | Download (ext.) | arXiv
 

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