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ICML
2003
IEEE
16 years 7 months ago
Q-Decomposition for Reinforcement Learning Agents
The paper explores a very simple agent design method called Q-decomposition, wherein a complex agent is built from simpler subagents. Each subagent has its own reward function and...
Stuart J. Russell, Andrew Zimdars
IJCAI
1989
15 years 7 months ago
Building Robust Learning Systems by Combining Induction and Optimization
Each concept description language and search strategy has an inherent inductive bias, a preference for some hypotheses over others. No single inductive bias performs optimally on ...
David K. Tcheng, Bruce L. Lambert, Stephen C. Y. L...
AICOM
2006
105views more  AICOM 2006»
15 years 6 months ago
Evolutionary concept learning in First Order Logic: An overview
This paper presents an overview of recent systems for Inductive Logic Programming (ILP). After a short description of the two popular ILP systems FOIL and Progol, we focus on meth...
Federico Divina
ICML
2010
IEEE
15 years 7 months ago
Learning Deep Boltzmann Machines using Adaptive MCMC
When modeling high-dimensional richly structured data, it is often the case that the distribution defined by the Deep Boltzmann Machine (DBM) has a rough energy landscape with man...
Ruslan Salakhutdinov
SIGIR
2010
ACM
15 years 10 months ago
Discriminative models of integrating document evidence and document-candidate associations for expert search
Generative models such as statistical language modeling have been widely studied in the task of expert search to model the relationship between experts and their expertise indicat...
Yi Fang, Luo Si, Aditya P. Mathur