8 Publications

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[8]
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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[7]
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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[6]
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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[5]
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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[4]
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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[3]
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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[2]
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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[1]
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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8 Publications

Mark all

[8]
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
View | Files available | DOI | Download (ext.)
 
[7]
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
View | Files available | DOI
 
[6]
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.
View | Download (ext.)
 
[5]
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.
View | Download (ext.)
 
[4]
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
View | DOI | Download (ext.)
 
[3]
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
View | DOI | Download (ext.)
 
[2]
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
View | DOI | Download (ext.)
 
[1]
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
View | DOI | Download (ext.)
 

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