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Bernd Prach

Christoph Lampert

Egor Zverev

Hossein Zakerinia

Max Cairney-Leeming

Nikita Kalinin

Peter Sukenik

Jonathan Scott

Anastasia Pentina

Csaba Domokos

Filip Korc

Viktoriia Sharmanska

Daniel Pucher

Paul Henderson

Mary Phuong

Johannes Dostal

Nikola Konstantinov

Emilie Morvant

Georg Martius

Alexander Kolesnikov

Volhejn, Vaclav

Alexandra Krumes

Alexander Zimin

Amélie Royer



117 Publications

2019 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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 |Published| 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
 
2018 | Research Data | IST-REx-ID: 5584 | OA
Nonlinear decoding of a complex movie from the mammalian retina
S. Deny, O. Marre, V. Botella-Soler, G.S. Martius, G. Tkačik, (2018).
[Published Version] View | Files available | DOI
 
2017 |Published| Conference Paper | IST-REx-ID: 652
Dynamical self consistency leads to behavioral development and emergent social interactions in robots
R. Der, G.S. Martius, in:, IEEE, 2017.
View | DOI
 
2017 |Published| Journal Article | IST-REx-ID: 658 | OA
Self organized behavior generation for musculoskeletal robots
R. Der, G.S. Martius, Frontiers in Neurorobotics 11 (2017).
[Published Version] View | Files available | DOI
 
2017 |Published| Conference Paper | IST-REx-ID: 6841 | OA
Extrapolation and learning equations
G.S. Martius, C. Lampert, in:, 5th International Conference on Learning Representations, ICLR 2017 - Workshop Track Proceedings, International Conference on Learning Representations, 2017.
[Preprint] View | Download Preprint (ext.) | arXiv
 
2017 |Published| Conference Paper | IST-REx-ID: 750
Optimal geospatial volunteer allocation needs realistic distances
J. Pielorz, M. Prandtstetter, M. Straub, C. Lampert, in:, 2017 IEEE International Conference on Big Data, IEEE, 2017, pp. 3760–3763.
View | DOI
 
2017 |Published| Conference Paper | IST-REx-ID: 1000 | OA
PixelCNN models with auxiliary variables for natural image modeling
A. Kolesnikov, C. Lampert, in:, 34th International Conference on Machine Learning, JMLR, 2017, pp. 1905–1914.
[Submitted Version] View | Download Submitted Version (ext.) | WoS | arXiv
 
2017 |Published| Conference Paper | IST-REx-ID: 998 | OA
iCaRL: Incremental classifier and representation learning
S.A. Rebuffi, A. Kolesnikov, G. Sperl, C. Lampert, in:, IEEE, 2017, pp. 5533–5542.
[Submitted Version] View | DOI | Download Submitted Version (ext.) | WoS
 

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