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» Learning and Generalization with the Information Bottleneck
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CPAIOR
2009
Springer
16 years 1 months ago
Learning How to Propagate Using Random Probing
Abstract. In constraint programming there are often many choices regarding the propagation method to be used on the constraints of a problem. However, simple constraint solvers usu...
Efstathios Stamatatos, Kostas Stergiou
SIGECOM
2009
ACM
114views ECommerce» more  SIGECOM 2009»
16 years 1 months ago
Policy teaching through reward function learning
Policy teaching considers a Markov Decision Process setting in which an interested party aims to influence an agent’s decisions by providing limited incentives. In this paper, ...
Haoqi Zhang, David C. Parkes, Yiling Chen
DIMEA
2008
164views Multimedia» more  DIMEA 2008»
15 years 8 months ago
Lessons learned: game design for large public displays
This paper presents the design and deployment of Polar Defence, an interactive game for a large public display. We designed this display based on a model of "users" and ...
Matthias Finke, Anthony Tang, Rock Leung, Michael ...
ACL
2003
15 years 8 months ago
Learning to Predict Pitch Accents and Prosodic Boundaries in Dutch
We train a decision tree inducer (CART) and a memory-based classifier (MBL) on predicting prosodic pitch accents and breaks in Dutch text, on the basis of shallow, easy-to-comput...
Erwin Marsi, Martin Reynaert, Antal van den Bosch,...
NIPS
2001
15 years 8 months ago
Probabilistic principles in unsupervised learning of visual structure: human data and a model
To find out how the representations of structured visual objects depend on the co-occurrence statistics of their constituents, we exposed subjects to a set of composite images wit...
Shimon Edelman, Benjamin P. Hiles, Hwajin Yang, Na...