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» Learning Object Representations Using Sequential Patterns
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COGSCI
2010
125views more  COGSCI 2010»
13 years 10 months ago
A Probabilistic Computational Model of Cross-Situational Word Learning
Words are the essence of communication: they are the building blocks of any language. Learning the meaning of words is thus one of the most important aspects of language acquisiti...
Afsaneh Fazly, Afra Alishahi, Suzanne Stevenson
ICRA
2010
IEEE
170views Robotics» more  ICRA 2010»
13 years 8 months ago
Categorizing object-action relations from semantic scene graphs
— In this work we introduce a novel approach for detecting spatiotemporal object-action relations, leading to both, action recognition and object categorization. Semantic scene g...
Eren Erdal Aksoy, Alexey Abramov, Florentin Wö...
UAI
1996
13 years 11 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
IROS
2009
IEEE
138views Robotics» more  IROS 2009»
14 years 4 months ago
Using eigenposes for lossless periodic human motion imitation
— Programming a humanoid robot to perform an action that takes the robot’s complex dynamics into account is a challenging problem. Traditional approaches typically require high...
Rawichote Chalodhorn, Rajesh P. N. Rao
KDD
2008
ACM
119views Data Mining» more  KDD 2008»
14 years 10 months ago
SAIL: summation-based incremental learning for information-theoretic clustering
Information-theoretic clustering aims to exploit information theoretic measures as the clustering criteria. A common practice on this topic is so-called INFO-K-means, which perfor...
Junjie Wu, Hui Xiong, Jian Chen