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UAI
1998
11 years 1 months ago
Learning From What You Don't Observe
The process of diagnosis involves learning about the state of a system from various observations of symptoms or findings about the system. Sophisticated Bayesian (and other) algor...
Mark A. Peot, Ross D. Shachter
TRECVID
2008
11 years 1 months ago
Beyond Semantic Search: What You Observe May Not Be What You Think
This paper presents our approaches and results of the four TRECVID 2008 tasks we participated in: high-level feature extraction, automatic video search, video copy detection, and ...
Chong-Wah Ngo, Yu-Gang Jiang, Xiao-Yong Wei, Wanle...
ICRA
2003
IEEE
141views Robotics» more  ICRA 2003»
11 years 5 months ago
Visual transformations in gesture imitation: what you see is what you do
We propose an approach for a robot to imitate the gestures of a human demonstrator. Our framework consists solely of two components: a Sensory-Motor Map (SMM) and a View-Point Tra...
Manuel Cabido-Lopes, José Santos-Victor
CVPR
2011
IEEE
10 years 7 months ago
What You Saw is Not What You Get: Domain Adaptation Using Asymmetric Kernel Transforms
In real-world applications, “what you saw” during training is often not “what you get” during deployment: the distribution and even the type and dimensionality of features...
Brian Kulis, Kate Saenko, Trevor Darrell
AIED
2009
Springer
11 years 6 months ago
I learn from you, you learn from me: How to make iList learn from students
We developed a new model for iList, our system that helps students learn linked list. The model is automatically extracted from past student data, and allows iList to track student...
Davide Fossati, Barbara Di Eugenio, Stellan Ohlsso...
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