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120
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ESOP
2011
Springer
14 years 1 months ago
Measure Transformer Semantics for Bayesian Machine Learning
Abstract. The Bayesian approach to machine learning amounts to inferring posterior distributions of random variables from a probabilistic model of how the variables are related (th...
Johannes Borgström, Andrew D. Gordon, Michael...
84
Voted
RECOMB
2003
Springer
15 years 10 months ago
Transforming men into mice: the Nadeau-Taylor chromosomal breakage model revisited
Although analysis of genome rearrangements was pioneered by Dobzhansky and Sturtevant 65 years ago, we still know very little about the rearrangement events that produced the exis...
Pavel A. Pevzner, Glenn Tesler
88
Voted
QOSA
2010
Springer
15 years 2 months 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
ICPP
2008
IEEE
15 years 4 months ago
On the Design of Fast Pseudo-Random Number Generators for the Cell Broadband Engine and an Application to Risk Analysis
Numerical simulations in computational physics, biology, and finance, often require the use of high quality and efficient parallel random number generators. We design and optimi...
David A. Bader, Aparna Chandramowlishwaran, Virat ...
129
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ICCV
2011
IEEE
13 years 10 months ago
Perturb-and-MAP Random Fields: Using Discrete Optimization\\to Learn and Sample from Energy Models
We propose a novel way to induce a random field from an energy function on discrete labels. It amounts to locally injecting noise to the energy potentials, followed by finding t...
George Papandreou, Alan L. Yuille