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» Coping With Uncertainty in Map Learning
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AAAI
2007
15 years 1 months ago
Learning Language Semantics from Ambiguous Supervision
This paper presents a method for learning a semantic parser from ambiguous supervision. Training data consists of natural language sentences annotated with multiple potential mean...
Rohit J. Kate, Raymond J. Mooney
93
Voted
HRI
2010
ACM
15 years 6 months ago
Following directions using statistical machine translation
—Mobile robots that interact with humans in an intuitive way must be able to follow directions provided by humans in unconstrained natural language. In this work we investigate h...
Cynthia Matuszek, Dieter Fox, Karl Koscher
91
Voted
PAMI
2008
182views more  PAMI 2008»
14 years 11 months ago
Gaussian Process Dynamical Models for Human Motion
We introduce Gaussian process dynamical models (GPDMs) for nonlinear time series analysis, with applications to learning models of human pose and motion from high-dimensional motio...
Jack M. Wang, David J. Fleet, Aaron Hertzmann
100
Voted
SEMWEB
2005
Springer
15 years 5 months ago
A Bayesian Network Approach to Ontology Mapping
This paper presents our ongoing effort on developing a principled methodology for automatic ontology mapping based on BayesOWL, a probabilistic framework we developed for modeling ...
Rong Pan, Zhongli Ding, Yang Yu, Yun Peng
CVPR
2012
IEEE
13 years 2 months ago
From pixels to physics: Probabilistic color de-rendering
Consumer digital cameras use tone-mapping to produce compact, narrow-gamut images that are nonetheless visually pleasing. In doing so, they discard or distort substantial radiomet...
Ying Xiong, Kate Saenko, Trevor Darrell, Todd Zick...