We present a simple new Monte Carlo algorithm for evaluating probabilities of observations in complex latent variable models, such as Deep Belief Networks. While the method is bas...
We present an automatic approach to tree annotation in which basic nonterminal symbols are alternately split and merged to maximize the likelihood of a training treebank. Starting...
Slav Petrov, Leon Barrett, Romain Thibaux, Dan Kle...
In this study, the problem of updating a printer characterization in response to systematic changes in print-device characteristics is addressed with two distinct approaches: the ...
Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social i...
Eric Bonabeau, Andrej Sobkowski, Guy Theraulaz, Je...
An original Reinforcement Learning (RL) methodology is proposed for the design of multi-agent systems. In the realistic setting of situated agents with local perception, the task o...