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EMNLP
2008
13 years 10 months ago
Modeling Annotators: A Generative Approach to Learning from Annotator Rationales
A human annotator can provide hints to a machine learner by highlighting contextual "rationales" for each of his or her annotations (Zaidan et al., 2007). How can one ex...
Omar Zaidan, Jason Eisner
ICCV
2009
IEEE
15 years 1 months ago
Learning Deformable Action Templates from Crowded Videos
In this paper, we present a Deformable Action Template (DAT) model that is learnable from cluttered real-world videos with weak supervisions. In our generative model, an action ...
Benjamin Yao, Song-Chun Zhu
HICSS
2003
IEEE
118views Biometrics» more  HICSS 2003»
14 years 1 months ago
Lessons Learned from Real DSL Experiments
Over the years, our group, led by Bob Balzer, designed and implemented three domain-specific languages for use by outside people in real situations. The first language described t...
David S. Wile
GEOS
2009
Springer
14 years 1 months ago
Bottom-Up Gazetteers: Learning from the Implicit Semantics of Geotags
As directories of named places, gazetteers link the names to geographic footprints and place types. Most existing gazetteers are managed strictly top-down: entries can only be adde...
Carsten Keßler, Patrick Maué, Jan Tor...
ICCV
2009
IEEE
1019views Computer Vision» more  ICCV 2009»
15 years 1 months ago
Similarity Functions for Categorization: from Monolithic to Category Specific
Similarity metrics that are learned from labeled training data can be advantageous in terms of performance and/or efficiency. These learned metrics can then be used in conjuncti...
Boris Babenko, Steve Branson, Serge Belongie