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Effective Removal of Operational Log Messages: an Application to Model Inference [article]

Donghwan Shin, Domenico Bianculli, Lionel Briand
2020 arXiv   pre-print
It is therefore important to remove operational messages in the logs before inferring models. In this paper, we propose LogCleaner, a novel technique for removing operational logs messages.  ...  Model inference aims to extract accurate models from the execution logs of software systems.  ...  To improve the effectiveness of model inference techniques (and thus the accuracy of the inferred models) it is therefore important to pre-process the logs before inferring models, by identifying and removing  ... 
arXiv:2004.07194v2 fatcat:wy2zlzr26req7lcaip3nl5ghxy

Bayesian inference in an extended SEIR model with nonparametric disease transmission rate: an application to the Ebola epidemic in Sierra Leone

Gianluca Frasso, Philippe Lambert
2016 Biostatistics  
In particular, the flexible modeling of the disease transmission rate makes the estimation of the effective reproduction number robust to the misspecification of the initial epidemic states and to under-reporting  ...  The 2014 Ebola outbreak in Sierra Leone is analysed using a susceptible-exposed-infectious-removed (SEIR) epidemic compartmental model.  ...  to access the data used in the paper.  ... 
doi:10.1093/biostatistics/kxw027 pmid:27324411 fatcat:mjggxy477bd5jh2dvidjpxseli

Robust approximate Bayesian inference [article]

Erlis Ruli, Nicola Sartori, Laura Ventura
2019 arXiv   pre-print
Special attention is given to the application of the method to linear mixed models. Simulation results and an application to a clinical study demonstrate the usefulness of the method.  ...  In particular, we use M-estimating functions to construct suitable summary statistics in ABC algorithms. The theoretical properties of the robust posterior distributions are discussed.  ...  Comparison of robust (ABC) and full (MCMC) posterior distributions of the fixed effects of the LMM (11)-(12), fitted to: log IgG (panels (a1)-(a2)), log(INFγ+1) ((b1)-(b2)), log IL-6 ((c1)-(c2)), log IL  ... 
arXiv:1706.01752v2 fatcat:bnxlmpsd3bgrhezgn2cyq7frnm

TrackMPNN: A Message Passing Graph Neural Architecture for Multi-Object Tracking [article]

Akshay Rangesh, Pranav Maheshwari, Mez Gebre, Siddhesh Mhatre, Vahid Ramezani, Mohan M. Trivedi
2021 arXiv   pre-print
network (MPNN) that operates on these graphs to produce the desired likelihood for every association therein.  ...  Despite this, our model performs on par with state-of-the-art approaches that make use of additional sensors, as well as multiple hand-crafted and/or learned features.  ...  Acknowledgements We are grateful to the Laboratory for Intelligent & Safe Automobiles at UC San Diego for providing us with the resources and compute to run the experiments presented in the paper.  ... 
arXiv:2101.04206v4 fatcat:5wu26cjnrbffjb32dadd33lcz4

Message Errors in Belief Propagation

Alexander T. Ihler, John W. Fisher III, Alan S. Willsky
2004 Neural Information Processing Systems  
Belief propagation (BP) is an increasingly popular method of performing approximate inference on arbitrary graphical models.  ...  We analyze this effect with respect to a particular measure of message error, and show bounds on the accumulation of errors in the system.  ...  The authors would like to thank Erik Sudderth, Martin Wainwright, Tom Heskes, and Lei Chen for many helpful discussions.  ... 
dblp:conf/nips/IhlerFW04 fatcat:6ci3pthakzcfjaxwymr6upw54a

Message from Organizers

2013 2013 Second European Workshop on Software Defined Networks  
We suggest that schedulers designed with an understanding of the requirements of all process classes and their mixes, as well the abilities of the underlying architecture, might be the solution to this  ...  Welcome to the second Workshop on Interaction between Operating System and Computer Architecture (WIOSCA).  ...  ACKNOWLEDGEMENTS We would like to thank the Diablo developers at ELIS Department of University of Ghent for their valuable help on binary optimization techniques.  ... 
doi:10.1109/ewsdn.2013.5 fatcat:2cw6hqsebnf4pbql5ijcq7jgd4

Message from Organizers

2014 2014 Second International Symposium on Computing and Networking  
We suggest that schedulers designed with an understanding of the requirements of all process classes and their mixes, as well the abilities of the underlying architecture, might be the solution to this  ...  Welcome to the second Workshop on Interaction between Operating System and Computer Architecture (WIOSCA).  ...  ACKNOWLEDGEMENTS We would like to thank the Diablo developers at ELIS Department of University of Ghent for their valuable help on binary optimization techniques.  ... 
doi:10.1109/candar.2014.4 fatcat:5cv4rwqfl5hghclpdzntldq24y

Message from Organizers

2016 2016 Fourth International Symposium on Computing and Networking (CANDAR)  
We suggest that schedulers designed with an understanding of the requirements of all process classes and their mixes, as well the abilities of the underlying architecture, might be the solution to this  ...  Welcome to the second Workshop on Interaction between Operating System and Computer Architecture (WIOSCA).  ...  ACKNOWLEDGEMENTS We would like to thank the Diablo developers at ELIS Department of University of Ghent for their valuable help on binary optimization techniques.  ... 
doi:10.1109/candar.2016.0006 fatcat:ecmuyvn7rnfrdle7ndjikntkae

Message from Organizers

2005 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 05)  
We suggest that schedulers designed with an understanding of the requirements of all process classes and their mixes, as well the abilities of the underlying architecture, might be the solution to this  ...  Welcome to the second Workshop on Interaction between Operating System and Computer Architecture (WIOSCA).  ...  ACKNOWLEDGEMENTS We would like to thank the Diablo developers at ELIS Department of University of Ghent for their valuable help on binary optimization techniques.  ... 
doi:10.1109/cvpr.2005.491 fatcat:j4aleqdnrfevfkj6tdvqdle2qu

Message Passing and Combinatorial Optimization [article]

Siamak Ravanbakhsh
2015 arXiv   pre-print
This thesis studies inference in discrete graphical models from an algebraic perspective and the ways inference can be used to express and approximate NP-hard combinatorial problems.  ...  We investigate the complexity and reducibility of various inference problems, in part by organizing them in an inference hierarchy.  ...  cannot justify application of message passing to graphs with loops.  ... 
arXiv:1508.05013v1 fatcat:qtgk3j6pyfd4jhst4lvul2j3fa

Message Passing for Collective Graphical Models

Tao Sun, Daniel Sheldon, Akshat Kumar
2015 International Conference on Machine Learning  
Mathematically, the algorithm is a strict generalization of BP-it can be viewed as an extension to minimize the Bethe free energy plus additional energy terms that are non-linear functions of the marginals  ...  Collective graphical models (CGMs) are a formalism for inference and learning about a population of independent and identically distributed individuals when only noisy aggregate data are available.  ...  Approximate MAP inference is an effective subroutine within the E-step of an expectation maximization (EM) algorithm for learning CGM parameters (Sheldon et al., 2013) .  ... 
dblp:conf/icml/SunSK15 fatcat:wx3tfayelzhcfk6bkdatfet4sy

Message from Organizers

2015 2015 Third International Symposium on Computing and Networking (CANDAR)  
We suggest that schedulers designed with an understanding of the requirements of all process classes and their mixes, as well the abilities of the underlying architecture, might be the solution to this  ...  While in the past, only complex professional codes ran on parallel computers, the commoditization of parallel computers is opening the door for many desktop applications to benefit from parallelization  ...  ACKNOWLEDGEMENTS We would like to thank the Diablo developers at ELIS Department of University of Ghent for their valuable help on binary optimization techniques.  ... 
doi:10.1109/candar.2015.4 fatcat:fcvejkemfjaczbcxgjrviru3pu

Message Passing Neural Processes [article]

Ben Day, Cătălina Cangea, Arian R. Jamasb, Pietro Liò
2020 arXiv   pre-print
We address this shortcoming by introducing Message Passing Neural Processes (MPNPs), the first class of NPs that explicitly makes use of relational structure within the model.  ...  Neural Processes (NPs) are powerful and flexible models able to incorporate uncertainty when representing stochastic processes, while maintaining a linear time complexity.  ...  , Penelope Jones, Petar Veličković and Toby Shevlane for their comments on an earlier version of this work.  ... 
arXiv:2009.13895v1 fatcat:nd33gccxgze6pae5papmthqssa

Message from the Program Chairs

2006 2006 IEEE International Conference on Web Services (ICWS'06)  
Abstract-In this paper we present a simulation-based analysis to infer the fault resilience of real-time systems.  ...  Satisfying these requirements enables tasksets to be generated in a moderate amount of time, allows effects of specific parameters to be explored without the problem of confounding variables, and ensures  ...  ACKNOWLEDGMENTS The authors would like to thank Enrico Bini for his contribution to discussions about the applicability of the UUniFast algorithm to the multiprocessor case.  ... 
doi:10.1109/icws.2006.90 dblp:conf/icws/X06a fatcat:bq447df4qzfsboa7pcpu7lcjty

d-VMP: Distributed Variational Message Passing

Andrés R. Masegosa, Ana M. Martínez, Helge Langseth, Thomas D. Nielsen, Antonio Salmerón, Darío Ramos-López, Anders L. Madsen
2016 European Workshop on Probabilistic Graphical Models  
The proposed algorithm compares favourably to stochastic variational inference both in terms of speed and quality of the learned models.  ...  The scheme is based on map-reduce operations, and utilizes the memory management of modern big data frameworks like Apache Flink to obtain a time-efficient and scalable implementation.  ...  Acknowledgments This work was performed as part of the AMIDST project.  ... 
dblp:conf/pgm/MasegosaMLNSRM16 fatcat:4d5hiagsajf5jpsrfnxxqvw7oq
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