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178
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ICCV
2011
IEEE
14 years 3 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille
ECCV
2008
Springer
16 years 5 months ago
Training Hierarchical Feed-Forward Visual Recognition Models Using Transfer Learning from Pseudo-Tasks
Abstract. Building visual recognition models that adapt across different domains is a challenging task for computer vision. While feature-learning machines in the form of hierarchi...
Amr Ahmed, Kai Yu, Wei Xu, Yihong Gong, Eric P. Xi...
119
Voted
CVPR
2009
IEEE
15 years 10 months ago
Automatic fetal face detection from ultrasound volumes via learning 3D and 2D information
3D ultrasound imaging has been increasingly used in clinics for fetal examination. However, manually searching for the optimal view of the fetal face in 3D ultrasound volumes is c...
Shaolei Feng, Shaohua Kevin Zhou, Sara Good, Dorin...
AAAI
1998
15 years 4 months ago
Learning to Classify Text from Labeled and Unlabeled Documents
In many important text classification problems, acquiring class labels for training documents is costly, while gathering large quantities of unlabeled data is cheap. This paper sh...
Kamal Nigam, Andrew McCallum, Sebastian Thrun, Tom...
COLING
2002
15 years 3 months ago
Learning Verb Argument Structure from Minimally Annotated Corpora
In this paper we investigate the task of automatically identifying the correct argument structure for a set of verbs. The argument structure of a verb allows us to predict the rel...
Anoop Sarkar, Woottiporn Tripasai