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NECO
2010
136views more  NECO 2010»
13 years 5 months ago
Learning to Represent Spatial Transformations with Factored Higher-Order Boltzmann Machines
To allow the hidden units of a restricted Boltzmann machine to model the transformation between two successive images, Memisevic and Hinton (2007) introduced three-way multiplicat...
Roland Memisevic, Geoffrey E. Hinton
ECML
2007
Springer
14 years 1 months ago
Learning Directed Probabilistic Logical Models: Ordering-Search Versus Structure-Search
We discuss how to learn non-recursive directed probabilistic logical models from relational data. This problem has been tackled before by upgrading the structure-search algorithm i...
Daan Fierens, Jan Ramon, Maurice Bruynooghe, Hendr...
AAAI
1994
13 years 8 months ago
Small is Beautiful: A Brute-Force Approach to Learning First-Order Formulas
We describe a method for learning formulas in firstorder logic using a brute-force, smallest-first search. The method is exceedingly simple. It generates all irreducible well-form...
Steven Minton, Ian Underwood
ILP
2004
Springer
14 years 23 days ago
First Order Random Forests with Complex Aggregates
Random forest induction is a bagging method that randomly samples the feature set at each node in a decision tree. In propositional learning, the method has been shown to work well...
Celine Vens, Anneleen Van Assche, Hendrik Blockeel...
AAAI
2011
12 years 7 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