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114 Publications

2020 | Conference Paper | IST-REx-ID: 8724 | OA
On the sample complexity of adversarial multi-source PAC learning
N.H. Konstantinov, E. Frantar, D.-A. Alistarh, C. Lampert, in:, Proceedings of the 37th International Conference on Machine Learning, ML Research Press, 2020, pp. 5416–5425.
[Published Version] View | Files available | arXiv
 
2020 | Thesis | IST-REx-ID: 8390 | OA
Leveraging structure in Computer Vision tasks for flexible Deep Learning models
A. Royer, Leveraging Structure in Computer Vision Tasks for Flexible Deep Learning Models, Institute of Science and Technology Austria, 2020.
[Published Version] View | Files available | DOI
 
2020 | Conference Paper | IST-REx-ID: 8186 | OA
Leveraging 2D data to learn textured 3D mesh generation
P.M. Henderson, V. Tsiminaki, C. Lampert, in:, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2020, pp. 7498–7507.
[Submitted Version] View | Files available | DOI | Download Submitted Version (ext.) | arXiv
 
2020 | Journal Article | IST-REx-ID: 6944 | OA
KS(conf): A light-weight test if a multiclass classifier operates outside of its specifications
R. Sun, C. Lampert, International Journal of Computer Vision 128 (2020) 970–995.
[Published Version] View | Files available | DOI | WoS
 
2019 | Book (Editor) | IST-REx-ID: 7171
Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt
K. Kersting, C. Lampert, C. Rothkopf, eds., Wie Maschinen Lernen: Künstliche Intelligenz Verständlich Erklärt, 1st ed., Springer Nature, Wiesbaden, 2019.
View | Files available | DOI
 
2019 | Conference Paper | IST-REx-ID: 6942 | OA
Strategy representation by decision trees with linear classifiers
P. Ashok, T. Brázdil, K. Chatterjee, J. Křetínský, C. Lampert, V. Toman, in:, 16th International Conference on Quantitative Evaluation of Systems, Springer Nature, 2019, pp. 109–128.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2019 | Journal Article | IST-REx-ID: 6554 | OA
Zero-shot learning - A comprehensive evaluation of the good, the bad and the ugly
Y. Xian, C. Lampert, B. Schiele, Z. Akata, IEEE Transactions on Pattern Analysis and Machine Intelligence 41 (2019) 2251–2265.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2019 | Conference Paper | IST-REx-ID: 7479 | OA
Distillation-based training for multi-exit architectures
M. Phuong, C. Lampert, in:, IEEE International Conference on Computer Vision, IEEE, 2019, pp. 1355–1364.
[Submitted Version] View | Files available | DOI | WoS
 
2019 | Conference Paper | IST-REx-ID: 7640 | OA
Detecting visual relationships using box attention
A. Kolesnikov, A. Kuznetsova, C. Lampert, V. Ferrari, in:, Proceedings of the 2019 International Conference on Computer Vision Workshop, IEEE, 2019.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2019 | Conference Paper | IST-REx-ID: 6569 | OA
Towards understanding knowledge distillation
M. Phuong, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 5142–5151.
[Published Version] View | Files available
 
2019 | Conference Paper | IST-REx-ID: 6590 | OA
Robust learning from untrusted sources
N.H. Konstantinov, C. Lampert, in:, Proceedings of the 36th International Conference on Machine Learning, ML Research Press, 2019, pp. 3488–3498.
[Preprint] View | Files available | Download Preprint (ext.) | arXiv
 
2019 | Conference Paper | IST-REx-ID: 6482 | OA
KS(conf): A light-weight test if a ConvNet operates outside of Its specifications
R. Sun, C. Lampert, in:, Springer Nature, 2019, pp. 244–259.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | arXiv
 
2018 | Thesis | IST-REx-ID: 68 | OA
Learning from dependent data
A. Zimin, Learning from Dependent Data, Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
2018 | Thesis | IST-REx-ID: 197 | OA
Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images
A. Kolesnikov, Weakly-Supervised Segmentation and Unsupervised Modeling of Natural Images, Institute of Science and Technology Austria, 2018.
[Published Version] View | Files available | DOI
 
2018 | Journal Article | IST-REx-ID: 563 | OA
Estimating barriers to gene flow from distorted isolation-by-distance patterns
H. Ringbauer, A. Kolesnikov, D. Field, N.H. Barton, Genetics 208 (2018) 1231–1245.
[Preprint] View | Files available | DOI | Download Preprint (ext.) | WoS
 
2018 | Journal Article | IST-REx-ID: 321 | OA
Guest editors' introduction to the special section on learning with Shared information for computer vision and multimedia analysis
T. Darrell, C. Lampert, N. Sebe, Y. Wu, Y. Yan, IEEE Transactions on Pattern Analysis and Machine Intelligence 40 (2018) 1029–1031.
[Published Version] View | Files available | DOI | WoS
 
2018 | Conference Paper | IST-REx-ID: 10882 | OA
Learning intelligent dialogs for bounding box annotation
J. Uijlings, K. Konyushkova, C. Lampert, V. Ferrari, in:, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, IEEE, 2018, pp. 9175–9184.
[Preprint] View | DOI | Download Preprint (ext.) | WoS | arXiv
 
2018 | Conference Paper | IST-REx-ID: 6012 | OA
Learning equations for extrapolation and control
S. Sahoo, C. Lampert, G.S. Martius, in:, Proceedings of the 35th International Conference on Machine Learning, ML Research Press, 2018, pp. 4442–4450.
[Preprint] View | Files available | Download Preprint (ext.) | WoS | arXiv
 
2018 | Conference Paper | IST-REx-ID: 6011 | OA
Data-dependent stability of stochastic gradient descent
I. Kuzborskij, C. Lampert, in:, Proceedings of the 35 Th International Conference on Machine Learning, ML Research Press, 2018, pp. 2815–2824.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 
2018 | Conference Paper | IST-REx-ID: 6589 | OA
The convergence of sparsified gradient methods
D.-A. Alistarh, T. Hoefler, M. Johansson, N.H. Konstantinov, S. Khirirat, C. Renggli, in:, Advances in Neural Information Processing Systems 31, Neural Information Processing Systems Foundation, 2018, pp. 5973–5983.
[Preprint] View | Download Preprint (ext.) | WoS | arXiv
 

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