Karsten Roth

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Dr. Marzyeh GhassemiAssistant Professor, EECS.CSAIL/IMES, MITVerified email at mit.edu
Joseph Paul CohenAmazon, AIMI (Stanford University), Mila (Quebec AI Institute)Verified email at stanford.edu
Björn OmmerProfessor, Computer Vision & Learning Group (CompVis), University of MunichVerified email at uni-heidelberg.de
Timo MilbichAignostics GmbHVerified email at aignostics.com
Samarth SinhaUniversity of TorontoVerified email at mail.utoronto.ca
Zeynep AkataProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Yoshua BengioProfessor of computer science, University of Montreal, Mila, IVADO, CIFARVerified email at umontreal.ca
Oriol VinyalsResearch Scientist at Google DeepMindVerified email at google.com
Thomas BroxUniversity of Freiburg and AmazonVerified email at cs.uni-freiburg.de
Peter GehlerZalandoVerified email at zalando.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Biagio BrattoliResearch scientist at Lunit, previously AWS. PhD at Heidelberg UniversityVerified email at lunit.io
Natalie DullerudPh.D. Student, Stanford UniversityVerified email at stanford.edu
Prateek GuptaDPhil, University of Oxford, The Alan Turing Institute, Montréal Institute of Learning AlgorithmsVerified email at columbia.edu
Juergen HesserHeidelberg UniversityVerified email at medma.uni-heidelberg.de
Haoran ZhangPhD Student, Massachusetts Institute of TechnologyVerified email at mit.edu
Winfried K. HensingerUniversity of SussexVerified email at sussex.ac.uk
Homanga BharadhwajCarnegie Mellon University | Meta AIVerified email at cs.cmu.edu
Hugo LarochelleGoogle DeepMind & MilaVerified email at google.com
Ludwig SchmidtUniversity of Washington and Allen Institute for AIVerified email at cs.washington.edu

Karsten Roth

ELLIS, IMPRS-IS, University of Tübingen

Verified email at uni-tuebingen.de - Homepage

Continual Learning Generalization Foundation Models Contrastive Learning Deep Metric Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Covid-19 image data collection: Prospective predictions are the future JP Cohen, P Morrison, L Dao, K Roth, TQ Doung, M Ghassemi Journal of Machine Learning for Biomedical Imaging (MELBA), 2020	826*	2020
The liver tumor segmentation benchmark (lits) P Bilic, P Christ, HB Li, E Vorontsov, A Ben-Cohen, G Kaissis, A Szeskin, ... Medical Image Analysis 84, 102680, 2023	800	2023
Towards total recall in industrial anomaly detection K Roth, L Pemula, J Zepeda, B Schölkopf, T Brox, P Gehler Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022	357	2022
Predicting covid-19 pneumonia severity on chest x-ray with deep learning JP Cohen, L Dao, K Roth, P Morrison, Y Bengio, AF Abbasi, B Shen, ... Cureus 12 (7), 2020	237	2020
Revisiting Training Strategies and Generalization Performance in Deep Metric Learning K Roth, T Milbich, S Sinha, P Gupta, B Ommer, JP Cohen ICML 2020, 2020	154	2020
MIC: Mining Interclass Characteristics for Improved Metric Learning K Roth, B Brattoli, B Ommer Proceedings of the IEEE International Conference on Computer Vision, 8000-8009, 2019	99	2019
Efficient preparation and detection of microwave dressed-state qubits and qutrits with trapped ions J Randall, S Weidt, ED Standing, K Lake, SC Webster, DF Murgia, ... Physical Review A 91 (1), 012322, 2015	67	2015
DiVA: Diverse Visual Feature Aggregation for Deep Metric Learning T Milbich, K Roth, H Bharadhwaj, S Sinha, Y Bengio, B Ommer, JP Cohen ECCV 2020, 2020	59	2020
Pads: Policy-adapted sampling for visual similarity learning K Roth, T Milbich, B Ommer Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020	43	2020
Simultaneous similarity-based self-distillation for deep metric learning K Roth, T Milbich, B Ommer, JP Cohen, M Ghassemi International Conference on Machine Learning, 9095-9106, 2021	39*	2021
Improving the fairness of chest x-ray classifiers H Zhang, N Dullerud, K Roth, L Oakden-Rayner, S Pfohl, M Ghassemi Conference on Health, Inference, and Learning, 204-233, 2022	31	2022
Non-isotropy regularization for proxy-based deep metric learning K Roth, O Vinyals, Z Akata Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022	24	2022
Integrating language guidance into vision-based deep metric learning K Roth, O Vinyals, Z Akata Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2022	24	2022
Sharing Matters for Generalization in Deep Metric Learning T Milbich, K Roth, B Brattoli, B Ommer TPAMI 2020, 2020	24	2020
Characterizing generalization under out-of-distribution shifts in deep metric learning T Milbich, K Roth, S Sinha, L Schmidt, M Ghassemi, B Ommer Advances in Neural Information Processing Systems 34, 25006-25018, 2021	19	2021
Is fairness only metric deep? evaluating and addressing subgroup gaps in deep metric learning N Dullerud, K Roth, K Hamidieh, N Papernot, M Ghassemi The Tenth International Conference on Learning Representations (ICLR), 2022, 2022	11	2022
Liver lesion segmentation with slice-wise 2d tiramisu and tversky loss function K Roth, T Konopczyński, J Hesser arXiv preprint arXiv:1905.03639, 2019	11	2019
Disentanglement of Correlated Factors via Hausdorff Factorized Support K Roth, M Ibrahim, Z Akata, P Vincent, D Bouchacourt The Eleventh International Conference on Learning Representations (ICLR), 2023, 2023	10	2023
Temporal control of the integrated stress response by a stochastic molecular switch P Klein, SM Kallenberger, H Roth, K Roth, TBN Ly-Hartig, V Magg, J Aleš, ... Science Advances 8 (12), eabk2022, 2022	7	2022
Boosting Liver and Lesion Segmentation from CT Scans by Mask Mining K Roth, J Hesser, T Konopczyński Medical Imaging Workshop at NeurIPS 2019, 2019	6*	2019

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