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» Learning Object Representations Using Sequential Patterns
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IJCNN
2006
IEEE
15 years 8 months ago
In-Place Learning for Positional and Scale Invariance
— In-place learning is a biologically inspired concept, meaning that the computational network is responsible for its own learning. With in-place learning, there is no need for a...
Juyang Weng, Hong Lu, Tianyu Luwang, Xiangyang Xue
FLAIRS
2008
15 years 4 months ago
Contrast Pattern Mining with Gap Constraints for Peptide Folding Prediction
1 In this paper, we propose a peptide folding prediction method which discovers contrast patterns to differentiate and predict peptide folding classes. A contrast pattern is defin...
Chinar C. Shah, Xingquan Zhu, Taghi M. Khoshgoftaa...
CVPR
2007
IEEE
16 years 4 months ago
Learning and Matching Line Aspects for Articulated Objects
Traditional aspect graphs are topology-based and are impractical for articulated objects. In this work we learn a small number of aspects, or prototypical views, from video data. ...
Xiaofeng Ren
COLT
2007
Springer
15 years 8 months ago
Property Testing: A Learning Theory Perspective
Property testing deals with tasks where the goal is to distinguish between the case that an object (e.g., function or graph) has a prespecified property (e.g., the function is li...
Dana Ron
ARCS
2005
Springer
15 years 7 months ago
Adaptive Object Acquisition
We propose an active vision system for object acquisition. The core of our approach is a reinforcement learning module which learns a strategy to scan an object. The agent moves a...
Gabriele Peters, Claus-Peter Alberts, Markus Bries...