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» Predictive State Temporal Difference Learning
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AAAI
2012
13 years 2 months ago
Relative Attributes for Enhanced Human-Machine Communication
We propose to model relative attributes1 that capture the relationships between images and objects in terms of human-nameable visual properties. For example, the models can captur...
Devi Parikh, Adriana Kovashka, Amar Parkash, Krist...
107
Voted
WOA
2007
15 years 1 months ago
Expectations driven approach for Situated, Goal-directed Agents
Abstract— Situated agents engaged in open systems continually face with external events requiring adequate services and behavioral responses. In these conditions agents should be...
Michele Piunti, Cristiano Castelfranchi, Rino Falc...
95
Voted
ICONIP
2007
15 years 1 months ago
Dynamical Nonstationarity Analysis of Resting EEGs in Alzheimer's Disease
The understanding of nonstationarity, from both a dynamical and a statistical point of view, has turned from a constraint on application of a specific type of analysis (e.g. spectr...
Charles-Francois Vincent Latchoumane, Emmanuel C. ...
129
Voted
CVPR
2012
IEEE
13 years 2 months ago
Understanding collective crowd behaviors: Learning a Mixture model of Dynamic pedestrian-Agents
In this paper, a new Mixture model of Dynamic pedestrian-Agents (MDA) is proposed to learn the collective behavior patterns of pedestrians in crowded scenes. Collective behaviors ...
Bolei Zhou, Xiaogang Wang, Xiaoou Tang
KDD
2002
ACM
136views Data Mining» more  KDD 2002»
16 years 24 days ago
Relational Markov models and their application to adaptive web navigation
Relational Markov models (RMMs) are a generalization of Markov models where states can be of different types, with each type described by a different set of variables. The domain ...
Corin R. Anderson, Pedro Domingos, Daniel S. Weld