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» Approximate Learning of Dynamic Models
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BC
1999
108views more  BC 1999»
15 years 4 months ago
Exact digital simulation of time-invariant linear systems with applications to neuronal modeling
An ecient new method for the exact digital simulation of time-invariant linear systems is presented. Such systems are frequently encountered as models for neuronal systems, or as s...
Stefan Rotter, Markus Diesmann
COLT
2000
Springer
15 years 9 months ago
The Computational Complexity of Densest Region Detection
We investigate the computational complexity of the task of detecting dense regions of an unknown distribution from un-labeled samples of this distribution. We introduce a formal l...
Shai Ben-David, Nadav Eiron, Hans-Ulrich Simon
190
Voted
UAI
2008
15 years 6 months ago
Efficient Inference in Persistent Dynamic Bayesian Networks
Numerous temporal inference tasks such as fault monitoring and anomaly detection exhibit a persistence property: for example, if something breaks, it stays broken until an interve...
Tomás Singliar, Denver Dash
CGA
1999
15 years 4 months ago
Dynamics Modeling and Culling
emsintovirtualenvironments,whileabstracting the modeling process as much as possible. To achieve efficiency,weconcentrateoncullingdynamicalsystems: if the system is not in view, we...
Stephen Chenney, Jeffrey Ichnowski, David A. Forsy...
AAAI
2010
15 years 6 months ago
Integrating Sample-Based Planning and Model-Based Reinforcement Learning
Recent advancements in model-based reinforcement learning have shown that the dynamics of many structured domains (e.g. DBNs) can be learned with tractable sample complexity, desp...
Thomas J. Walsh, Sergiu Goschin, Michael L. Littma...