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AAAI
2011
14 years 6 months ago
Coarse-to-Fine Inference and Learning for First-Order Probabilistic Models
Coarse-to-fine approaches use sequences of increasingly fine approximations to control the complexity of inference and learning. These techniques are often used in NLP and visio...
Chloe Kiddon, Pedro Domingos
AAAI
2012
13 years 8 months ago
Quality Expectation-Variance Tradeoffs in Crowdsourcing Contests
We examine designs for crowdsourcing contests, where participants compete for rewards given to superior solutions of a task. We theoretically analyze tradeoffs between the expecta...
Xi Alice Gao, Yoram Bachrach, Peter Key, Thore Gra...
AAAI
2012
13 years 8 months ago
Ontological Smoothing for Relation Extraction with Minimal Supervision
Relation extraction, the process of converting natural language text into structured knowledge, is increasingly important. Most successful techniques use supervised machine learni...
Congle Zhang, Raphael Hoffmann, Daniel S. Weld
AAAI
2012
13 years 8 months ago
Double-Bit Quantization for Hashing
Hashing, which tries to learn similarity-preserving binary codes for data representation, has been widely used for efficient nearest neighbor search in massive databases due to i...
Weihao Kong, Wu-Jun Li
AAAI
2012
13 years 8 months ago
Towards Discovering What Patterns Trigger What Labels
In many real applications, especially those involving data objects with complicated semantics, it is generally desirable to discover the relation between patterns in the input spa...
Yu-Feng Li, Ju-Hua Hu, Yuang Jiang, Zhi-Hua Zhou