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Boecking, Benedikt
19  Ergebnisse:
Personensuche X
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1

Learning to Exploit Temporal Structure for Biomedical Visio..:

, In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR),
Bannur, Shruthi ; Hyland, Stephanie ; Liu, Qianchu... - p. 15016-15027 , 2023
Link: https://doi.org/10.1109/..
 
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2

Constrained clustering and multiple kernel learning without..:

Boecking, Benedikt ; Jeanselme, Vincent ; Dubrawski, Artur
Advances in Data Analysis and Classification.  18 (2022)  2 - p. 309-324 , 2022
Link: https://doi.org/10.1007/..
 
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3

Making the Most of Text Semantics to Improve Biomedical Vis..:

, In: Lecture Notes in Computer Science; Computer Vision – ECCV 2022,
Boecking, Benedikt ; Usuyama, Naoto ; Bannur, Shruthi... - p. 1-21 , 2022
Link: https://doi.org/10.1007/..
 
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4

Always Lurking : Understanding and Mitigating Bias in On..:

, In: Proceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society,
Hundman, Kyle ; Gowda, Thamme ; Kejriwal, Mayank. - p. 137-143 , 2018
Link: https://dl.acm.org/doi/1..
 
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5

Event Prediction With Learning Algorithms—A Study of Events..:

Boecking, Benedikt ; Hall, Margeret ; Schneider, Jeff
Policy & Internet.  7 (2015)  2 - p. 159-184 , 2015
Link: https://doi.org/10.1002/..
 
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6

Support vector clustering of time series data with alignmen..:

Boecking, Benedikt ; Chalup, Stephan K. ; Seese, Detlef.
Pattern Recognition Letters.  45 (2014)  - p. 129-135 , 2014
Link: https://doi.org/10.1016/..
 
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7

Learning to Exploit Temporal Structure for Biomedical Visio..:

Bannur, Shruthi ; Hyland, Stephanie ; Liu, Qianchu...
http://arxiv.org/abs/2301.04558.  , 2023
Link: http://arxiv.org/abs/230..
 
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8

Weak Supervision for Affordable Modeling of Electrocardiogr..:

Goswami, Mononito ; Boecking, Benedikt ; Dubrawski, Artur
http://arxiv.org/abs/2201.02936.  , 2022
Link: http://arxiv.org/abs/220..
 
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9

Constrained Clustering and Multiple Kernel Learning without..:

Boecking, Benedikt ; Jeanselme, Vincent ; Dubrawski, Artur
http://arxiv.org/abs/2203.12546.  , 2022
Link: http://arxiv.org/abs/220..
 
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10

Generative Modeling Helps Weak Supervision (and Vice Versa):

Boecking, Benedikt ; Roberts, Nicholas ; Neiswanger, Willie...
http://arxiv.org/abs/2203.12023.  , 2022
Link: http://arxiv.org/abs/220..
 
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11

Making the Most of Text Semantics to Improve Biomedical Vis..:

Boecking, Benedikt ; Usuyama, Naoto ; Bannur, Shruthi...
http://arxiv.org/abs/2204.09817.  , 2022
Link: http://arxiv.org/abs/220..
 
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12

Dependency Structure Misspecification in Multi-Source Weak ..:

Cachay, Salva Rühling ; Boecking, Benedikt ; Dubrawski, Artur
http://arxiv.org/abs/2106.10302.  , 2021
Link: http://arxiv.org/abs/210..
 
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13

End-to-End Weak Supervision:

Cachay, Salva Rühling ; Boecking, Benedikt ; Dubrawski, Artur
http://arxiv.org/abs/2107.02233.  , 2021
Link: http://arxiv.org/abs/210..
 
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14

Interactive Weak Supervision: Learning Useful Heuristics fo..:

Boecking, Benedikt ; Neiswanger, Willie ; Xing, Eric.
http://arxiv.org/abs/2012.06046.  , 2020
Link: http://arxiv.org/abs/201..
 
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15

Pairwise Feedback for Data Programming:

Boecking, Benedikt ; Dubrawski, Artur
http://arxiv.org/abs/1912.07685.  , 2019
Link: http://arxiv.org/abs/191..
 
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