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» Learning Functions from Imperfect Positive Data
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126
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CVPR
2005
IEEE
16 years 4 months ago
Hybrid Models for Human Motion Recognition
Probabilistic models have been previously shown to be efficient and effective for modeling and recognition of human motion. In particular we focus on methods which represent the h...
Claudio Fanti, Lihi Zelnik-Manor, Pietro Perona
107
Voted
BMCBI
2006
100views more  BMCBI 2006»
15 years 2 months ago
STAR: predicting recombination sites from amino acid sequence
Background: Designing novel proteins with site-directed recombination has enormous prospects. By locating effective recombination sites for swapping sequence parts, the probabilit...
Denis C. Bauer, Mikael Bodén, Ricarda Thier...
100
Voted
PLDI
2010
ACM
16 years 4 days ago
Complete Functional Synthesis
Synthesis of program fragments from specifications can make programs easier to write and easier to reason about. To integrate synthesis into programming languages, synthesis algor...
Viktor Kuncak, Mika l Mayer, Ruzica Piskac, Philip...
164
Voted
ATAL
2010
Springer
15 years 3 months ago
Combining manual feedback with subsequent MDP reward signals for reinforcement learning
As learning agents move from research labs to the real world, it is increasingly important that human users, including those without programming skills, be able to teach agents de...
W. Bradley Knox, Peter Stone
124
Voted
ICDM
2010
IEEE
142views Data Mining» more  ICDM 2010»
15 years 21 days ago
Causal Discovery from Streaming Features
In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available ...
Kui Yu, Xindong Wu, Hao Wang, Wei Ding