The Experts below are selected from a list of 410619 Experts worldwide ranked by ideXlab platform
Jiawei Yuan - One of the best experts on this subject based on the ideXlab platform.
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privacy preserving back propagation neural Network Learning made practical with cloud computing
IEEE Transactions on Parallel and Distributed Systems, 2014Co-Authors: Jiawei YuanAbstract:To improve the accuracy of Learning result, in practice multiple parties may collaborate through conducting joint Back-Propagation neural Network Learning on the union of their respective data sets. During this process no party wants to disclose her/his private data to others. Existing schemes supporting this kind of collaborative Learning are either limited in the way of data partition or just consider two parties. There lacks a solution that allows two or more parties, each with an arbitrarily partitioned data set, to collaboratively conduct the Learning. This paper solves this open problem by utilizing the power of cloud computing. In our proposed scheme, each party encrypts his/her private data locally and uploads the ciphertexts into the cloud. The cloud then executes most of the operations pertaining to the Learning algorithms over ciphertexts without knowing the original private data. By securely offloading the expensive operations to the cloud, we keep the computation and communication costs on each party minimal and independent to the number of participants. To support flexible operations over ciphertexts, we adopt and tailor the BGN "doubly homomorphic" encryption algorithm for the multiparty setting. Numerical analysis and experiments on commodity cloud show that our scheme is secure, efficient, and accurate.
Kathleen Marchal - One of the best experts on this subject based on the ideXlab platform.
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validating module Network Learning algorithms using simulated data
BMC Bioinformatics, 2007Co-Authors: Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van Den Bulcke, Koenraad Van Leemput, Piet Van Remortel, Martin Kuiper, Kathleen MarchalAbstract:Background In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module Network Learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module Network Learning algorithms. We introduce a software package for Learning module Networks, called LeMoNe, which incorporates a novel strategy for Learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance.
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Validating module Network Learning algorithms using simulated data
BMC bioinformatics, 2007Co-Authors: Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van Den Bulcke, Koenraad Van Leemput, Piet Van Remortel, Martin Kuiper, Kathleen MarchalAbstract:In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module Network Learning algorithms. We introduce a software package for Learning module Networks, called LeMoNe, which incorporates a novel strategy for Learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the Learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.
Tom Michoel - One of the best experts on this subject based on the ideXlab platform.
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validating module Network Learning algorithms using simulated data
BMC Bioinformatics, 2007Co-Authors: Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van Den Bulcke, Koenraad Van Leemput, Piet Van Remortel, Martin Kuiper, Kathleen MarchalAbstract:Background In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Despite the demonstrated success of such algorithms in uncovering biologically relevant regulatory relations, further developments in the area are hampered by a lack of tools to compare the performance of alternative module Network Learning strategies. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module Network Learning algorithms. We introduce a software package for Learning module Networks, called LeMoNe, which incorporates a novel strategy for Learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance.
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Validating module Network Learning algorithms using simulated data
BMC bioinformatics, 2007Co-Authors: Tom Michoel, Steven Maere, Eric Bonnet, Anagha Joshi, Yvan Saeys, Tim Van Den Bulcke, Koenraad Van Leemput, Piet Van Remortel, Martin Kuiper, Kathleen MarchalAbstract:In recent years, several authors have used probabilistic graphical models to learn expression modules and their regulatory programs from gene expression data. Here, we demonstrate the use of the synthetic data generator SynTReN for the purpose of testing and comparing module Network Learning algorithms. We introduce a software package for Learning module Networks, called LeMoNe, which incorporates a novel strategy for Learning regulatory programs. Novelties include the use of a bottom-up Bayesian hierarchical clustering to construct the regulatory programs, and the use of a conditional entropy measure to assign regulators to the regulation program nodes. Using SynTReN data, we test the performance of LeMoNe in a completely controlled situation and assess the effect of the methodological changes we made with respect to an existing software package, namely Genomica. Additionally, we assess the effect of various parameters, such as the size of the data set and the amount of noise, on the inference performance. Overall, application of Genomica and LeMoNe to simulated data sets gave comparable results. However, LeMoNe offers some advantages, one of them being that the Learning process is considerably faster for larger data sets. Additionally, we show that the location of the regulators in the LeMoNe regulation programs and their conditional entropy may be used to prioritize regulators for functional validation, and that the combination of the bottom-up clustering strategy with the conditional entropy-based assignment of regulators improves the handling of missing or hidden regulators.
Tatseng Chua - One of the best experts on this subject based on the ideXlab platform.
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multiple social Network Learning and its application in volunteerism tendency prediction
International ACM SIGIR Conference on Research and Development in Information Retrieval, 2015Co-Authors: Xuemeng Song, Luming Zhang, Mohammad Akbari, Tatseng ChuaAbstract:We are living in the era of social Networks, where people throughout the world are connected and organized by multiple social Networks. The views revealed by different social Networks may vary according to the different services they offer. They are complimentary to each other and comprehensively characterize a specific user from different perspectives. As compared to the scare knowledge conveyed by a single source, appropriate aggregation of multiple social Networks offers us a better opportunity for deep user understanding. The challenges, however, co-exist with opportunities. The first challenge lies in the existence of block-wise missing data, caused by the fact that some users may be very active in certain social Networks while inactive in others. The second challenge is how to collaboratively integrate multiple social Networks. Towards this end, we first proposed a novel model for data missing completion by seamlessly exploring the knowledge from multiple sources. We then developed a robust multiple social Network Learning model, and applied it to the application of volunteerism tendency prediction. Extensive experiments on real world dataset verify the effectiveness of our scheme. The proposed scheme is applicable to many other domains, such as demographic inference and interest prediction.
James K. Mills - One of the best experts on this subject based on the ideXlab platform.
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Robotic System Sensitivity to Neural Network Learning Rate: Theory, Simulation, and Experiments
The International Journal of Robotics Research, 2000Co-Authors: Christopher M. Clark, James K. MillsAbstract:Selection of neural Network Learning rates to obtain satisfactory performance from neural Network controllers is a challenging problem. To assist in the selection of Learning rates, this paper investigates robotic system sensitivity to neural Network (NN) Learning rate. The work reported here consists of experimental and simulation results. A neural Network controller module, developed for the purpose of experimental evaluation of neural Network controller performance of a CRS Robotics Corporation A460 robot, allows testing of NN controllers using real-time iterative Learning. The A460 is equipped with a joint position proportional, integral, and derivative (PID) controller. The neural Network module supplies a signal to compensate for remaining errors in the PID-controlled system. A robot simulation, which models this PID-controlled A460 robot and NN controller, was also developed to allow the calculation of sensitivity to the NN Learning rate. This paper describes the implementation of three NN architec...
