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UAI
2008
13 years 10 months ago
Learning Arithmetic Circuits
Graphical models are usually learned without regard to the cost of doing inference with them. As a result, even if a good model is learned, it may perform poorly at prediction, be...
Daniel Lowd, Pedro Domingos
SIAMIS
2011
13 years 3 months ago
Large Scale Bayesian Inference and Experimental Design for Sparse Linear Models
Abstract. Many problems of low-level computer vision and image processing, such as denoising, deconvolution, tomographic reconstruction or superresolution, can be addressed by maxi...
Matthias W. Seeger, Hannes Nickisch
IEEEPACT
2008
IEEE
14 years 3 months ago
Characterizing and modeling the behavior of context switch misses
One of the essential features in modern computer systems is context switching, which allows multiple threads of execution to time-share a limited number of processors. While very ...
Fang Liu, Fei Guo, Yan Solihin, Seongbeom Kim, Abd...
FLAIRS
2010
13 years 11 months ago
Grounded Event-Based and Modal Representations for Objects, Relations, Beliefs, Etc.
Intelligent software agents (agents) adhering to the action selection paradigm have only one primary task that they need accomplish at any given time: to choose their next action....
Ryan James McCall, Stan Franklin, David Friedlande...
QOSA
2010
Springer
14 years 21 days ago
Validating Model-Driven Performance Predictions on Random Software Systems
Abstract. Software performance prediction methods are typically validated by taking an appropriate software system, performing both performance predictions and performance measurem...
Vlastimil Babka, Petr Tuma, Lubomír Bulej