The Experts below are selected from a list of 16518 Experts worldwide ranked by ideXlab platform
Petri Myllymäki - One of the best experts on this subject based on the ideXlab platform.
-
A simple approach for finding the globally optimal Bayesian network structure
arXiv: Artificial Intelligence, 2012Co-Authors: Tomi Silander, Petri MyllymäkiAbstract:We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases. Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases. Our algorithm is less complicated and more efficient than the techniques presented earlier. It can be easily parallelized, and offers a possibility for efficient exploration of the best networks consistent with different variable orderings. In the experimental part of the paper we compare the performance of the algorithm to the previous state-of-the-art algorithm. Free Source-Code and an online-demo can be found at this http URL
-
a simple approach for finding the globally optimal bayesian network structure
Uncertainty in Artificial Intelligence, 2006Co-Authors: Tomi Silander, Petri MyllymäkiAbstract:We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases. Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases. Our algorithm is less complicated and more efficient than the techniques presented earlier. It can be easily parallelized, and offers a possibility for efficient exploration of the best networks consistent with different variable orderings. In the experimental part of the paper we compare the performance of the algorithm to the previous state-of-the-art algorithm. Free Source-Code and an online-demo can be found at http://b-course.hiit.fi/bene.
Robert R Tucci - One of the best experts on this subject based on the ideXlab platform.
-
Quibbs, a Code Generator for Quantum Gibbs Sampling
arXiv: Quantum Physics, 2010Co-Authors: Robert R TucciAbstract:This paper introduces Quibbs v1.3, a Java application available for Free. (Source Code included in the distribution.) Quibbs is a \Code generator" for quantum Gibbs sampling: after the user inputs some les that specify a classical Bayesian network, Quibbs outputs a quantum circuit for performing Gibbs sampling of that Bayesian network on a quantum computer. Quibbs implements an algorithm described in earlier papers, that combines various apple pie techniques such as: an adaptive xedpoint version of Grover’s algorithm, Szegedy operators, quantum phase estimation and quantum multiplexors.
-
QOperAv, a Code Generator for Generating Quantum Circuits for Evaluating Certain Quantum Operator Averages
2010Co-Authors: Robert R TucciAbstract:This paper introduces QOperAv v1.5, a Java application available for Free. (Source Code included in the distribution.) QOperAv is a “Code generator ” for generating quantum circuits. The quantum circuits generated by QOperAv can be used to evaluate with polynomial efficiency the average of f(A) for some simple (that is, computable with polynomial efficiency) function f and a Hermitian operator A, pro-vided that we know how to compile exp(iA) with polynomial efficiency. QOperAv implements an algorithm described in earlier papers, that combines various standard techniques such as quantum phase estimation and quantum multiplexors. 1 a
-
Code generator for quantum simulated annealing
arXiv: Quantum Physics, 2009Co-Authors: Robert R TucciAbstract:This paper introduces QuSAnn v1.2 and Multiplexor Expander v1.2, two Java applications available for Free. (Source Code included in the distribution.) QuSAnn is a \Code generator" for quantum simulated annealing: after the user inputs some parameters, it outputs a quantum circuit for performing simulated annealing on a quantum computer. The quantum circuit implements the algorithm of Wocjan et al. (arXiv:0804.4259), which improves on the original algorithm of Somma et al. (arXiv:0712.1008). The quantum circuit generated by QuSAnn includes some quantum multiplexors. The application Multiplexor Expander allows the user to replace each of those multiplexors by a sequence of more elementary gates such as multiply controlled NOTs and qubit rotations.
Tomi Silander - One of the best experts on this subject based on the ideXlab platform.
-
A simple approach for finding the globally optimal Bayesian network structure
arXiv: Artificial Intelligence, 2012Co-Authors: Tomi Silander, Petri MyllymäkiAbstract:We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases. Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases. Our algorithm is less complicated and more efficient than the techniques presented earlier. It can be easily parallelized, and offers a possibility for efficient exploration of the best networks consistent with different variable orderings. In the experimental part of the paper we compare the performance of the algorithm to the previous state-of-the-art algorithm. Free Source-Code and an online-demo can be found at this http URL
-
a simple approach for finding the globally optimal bayesian network structure
Uncertainty in Artificial Intelligence, 2006Co-Authors: Tomi Silander, Petri MyllymäkiAbstract:We study the problem of learning the best Bayesian network structure with respect to a decomposable score such as BDe, BIC or AIC. This problem is known to be NP-hard, which means that solving it becomes quickly infeasible as the number of variables increases. Nevertheless, in this paper we show that it is possible to learn the best Bayesian network structure with over 30 variables, which covers many practically interesting cases. Our algorithm is less complicated and more efficient than the techniques presented earlier. It can be easily parallelized, and offers a possibility for efficient exploration of the best networks consistent with different variable orderings. In the experimental part of the paper we compare the performance of the algorithm to the previous state-of-the-art algorithm. Free Source-Code and an online-demo can be found at http://b-course.hiit.fi/bene.
Tucci, Robert R. - One of the best experts on this subject based on the ideXlab platform.
-
QOperAv, a Code Generator for Generating Quantum Circuits for Evaluating Certain Quantum Operator Averages
2010Co-Authors: Tucci, Robert R.Abstract:This paper introduces QOperAv v1.5, a Java application available for Free. (Source Code included in the distribution.) QOperAv is a "Code generator" for generating quantum circuits. The quantum circuits generated by QOperAv can be used to evaluate with polynomial efficiency the average of $f(A)$ for some simple (that is, computable with polynomial efficiency) function $f$ and a Hermitian operator $A$, provided that we know how to compile $\exp(iA)$ with polynomial efficiency. QOperAv implements an algorithm described in earlier papers, that combines various standard techniques such as quantum phase estimation and quantum multiplexors.Comment: 5 pages, 5 files(1 .tex, 1 .sty, 1 .pdf, 1 .txt, 1 .xxx)Source Code in QOperAv1-5.tx
-
Quibbs, a Code Generator for Quantum Gibbs Sampling
2010Co-Authors: Tucci, Robert R.Abstract:This paper introduces Quibbs v1.3, a Java application available for Free. (Source Code included in the distribution.) Quibbs is a "Code generator" for quantum Gibbs sampling: after the user inputs some files that specify a classical Bayesian network, Quibbs outputs a quantum circuit for performing Gibbs sampling of that Bayesian network on a quantum computer. Quibbs implements an algorithm described in earlier papers, that combines various apple pie techniques such as: an adaptive fixed-point version of Grover's algorithm, Szegedy operators, quantum phase estimation and quantum multiplexors.Comment: 21 pages (16 files: 1 .tex, 1 .sty, 14 .pdf);V2-added 2 new files(.xxx, .txt) txt file contains quibbs1.4 Source cod
-
Code Generator for Quantum Simulated Annealing
2009Co-Authors: Tucci, Robert R.Abstract:This paper introduces QuSAnn v1.2 and Multiplexor Expander v1.2, two Java applications available for Free. (Source Code included in the distribution.) QuSAnn is a "Code generator" for quantum simulated annealing: after the user inputs some parameters, it outputs a quantum circuit for performing simulated annealing on a quantum computer. The quantum circuit implements the algorithm of Wocjan et al. (arXiv:0804.4259), which improves on the original algorithm of Somma et al. (arXiv:0712.1008). The quantum circuit generated by QuSAnn includes some quantum multiplexors. The application Multiplexor Expander allows the user to replace each of those multiplexors by a sequence of more elementary gates such as multiply controlled NOTs and qubit rotations.Comment: 33 pages (files: 1 .tex, 2 .sty, 19 .pdf)-v.2 improved appendix about szegedy op
Sebastian Elbaum - One of the best experts on this subject based on the ideXlab platform.
-
Quality assurance under the open Source development model
Journal of Systems and Software, 2003Co-Authors: Luyin Zhao, Sebastian ElbaumAbstract:The open Source development model has defied traditional software development practices by generating widely accepted products (e.g., Linux, Apache, Perl) while following unconventional principles such as the distribution of Free Source Code and massive user participation. Those achievements have initiated and supported many declarations about the potential of the open Source model to accelerate the development of reliable software. However, the pronouncements in favor or against this model have been usually argumentative, lacking of empirical evidence to support either position. Our work uses a survey to overcome those limitations. The study explores how software quality assurance is performed under the open Source model, how it differs from more traditional software development models, and whether some of those differences could translate into practical advantages given the right circumstances. The findings indicate that open Source has certainly introduced a new dimension in large-scale distributed software development. However, we also discovered that the potential of open Source might not be exploitable under all scenarios. Furthermore, we found that many of the open Source quality assurance activities are still evolving.
