Fronk C; Yun J; Singh P; Petzold. L
Bayesian polynomial neural networks and polynomial neural ordinary differential equations. Journal Article
In: PLOS Computational Biology, 2024.
@article{nokey,
title = {Bayesian polynomial neural networks and polynomial neural ordinary differential equations.},
author = {Colby Fronk and Jaewoong Yun and Prashant Singh and Linda Petzold.},
doi = {arXiv:2308.10892},
year = {2024},
date = {2024-08-01},
urldate = {2023-08-01},
journal = {PLOS Computational Biology},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Karakulev A; Zachariah D; Singh P
Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables Conference
Proceedings of the 41st International Conference on Machine Learning (ICML 2024), 2024.
@conference{nokey,
title = {Adaptive Parameter-Free Robust Learning using Latent Bernoulli Variables},
author = {Aleksandr Karakulev and Dave Zachariah and Prashant Singh},
url = {https://icml.cc/virtual/2024/poster/32797},
doi = {https://doi.org/10.48550/arXiv.2312.00585},
year = {2024},
date = {2024-07-25},
urldate = {2024-07-25},
booktitle = {Proceedings of the 41st International Conference on Machine Learning (ICML 2024)},
abstract = {We present an efficient parameter-free approach for statistical learning from corrupted training sets. We identify corrupted and non-corrupted samples using latent Bernoulli variables, and therefore formulate the robust learning problem as maximization of the likelihood where latent variables are marginalized out. The resulting optimization problem is solved via variational inference using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level and identifying outliers, while adding minimal computational overhead. We demonstrate our robust learning method on a wide variety of machine learning tasks including online learning and deep learning where it exhibits ability to adapt to different levels of noise and attain high prediction accuracy.},
keywords = {Bayesian Inference, Deep Learning, Optimization, Robust Learning},
pubstate = {published},
tppubtype = {conference}
}
We present an efficient parameter-free approach for statistical learning from corrupted training sets. We identify corrupted and non-corrupted samples using latent Bernoulli variables, and therefore formulate the robust learning problem as maximization of the likelihood where latent variables are marginalized out. The resulting optimization problem is solved via variational inference using an efficient Expectation-Maximization based method. The proposed approach improves over the state-of-the-art by automatically inferring the corruption level and identifying outliers, while adding minimal computational overhead. We demonstrate our robust learning method on a wide variety of machine learning tasks including online learning and deep learning where it exhibits ability to adapt to different levels of noise and attain high prediction accuracy.
Enberg R; Costa M F; Koay Y S; Moretti S; Singh P; Waltari H
Enhancing Robustness: BSM Parameter Inference with n1D-CNN and Novel Data Augmentation Conference Forthcoming
European AI for Fundamental Physics Conference (to appear), Forthcoming.
@conference{nokey,
title = {Enhancing Robustness: BSM Parameter Inference with n1D-CNN and Novel Data Augmentation},
author = {Rikard Enberg and Max Fusté Costa and Yong Sheng Koay and Stefano Moretti and Prashant Singh and Harri Waltari},
year = {2024},
date = {2024-04-30},
booktitle = {European AI for Fundamental Physics Conference (to appear)},
keywords = {Deep Learning, Inverse Problem},
pubstate = {forthcoming},
tppubtype = {conference}
}
Cheng L; Singh P; Ferranti F
Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks Journal Article
In: IEEE Access, vol. 12, pp. 55218-55224, 2024.
@article{cheng2024transfer,
title = {Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks},
author = {Liang Cheng and Prashant Singh and Francesco Ferranti},
url = {https://ieeexplore.ieee.org/abstract/document/10486893},
doi = {10.1109/ACCESS.2024.3383790},
year = {2024},
date = {2024-04-02},
urldate = {2024-01-01},
journal = {IEEE Access},
volume = {12},
pages = {55218-55224},
keywords = {Deep Learning, Inverse Problem},
pubstate = {published},
tppubtype = {article}
}
Enberg R; Costa M F; Koay Y S; Moretti S; Singh P; Waltari H
BSM models and parameter inference via an n-channel 1D-CNN Conference
Sixth annual workshop of the LPCC inter-experimental machine learning working group, CERN, Geneva, 2024.
@conference{nokey,
title = {BSM models and parameter inference via an n-channel 1D-CNN},
author = {Rikard Enberg and Max Fusté Costa and Yong Sheng Koay and Stefano Moretti and Prashant Singh and Harri Waltari},
url = {https://indico.cern.ch/event/1297159/contributions/5729212/attachments/2789892/4865115/IML3.pdf},
year = {2024},
date = {2024-01-12},
urldate = {2024-01-12},
booktitle = {Sixth annual workshop of the LPCC inter-experimental machine learning working group, CERN, Geneva},
keywords = {Deep Learning, Inverse Problem},
pubstate = {published},
tppubtype = {conference}
}
Chu J; Singh P; Toor S
Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities Conference
2023 IEEE 16th International Conference on Cloud Computing (CLOUD), IEEE IEEE, 2023.
@conference{nokey,
title = {Efficient Resource Scheduling for Distributed Infrastructures Using Negotiation Capabilities},
author = {Junjie Chu and Prashant Singh and Salman Toor},
url = {https://ieeexplore.ieee.org/abstract/document/10255003},
doi = {https://doi.org/10.1109/CLOUD60044.2023.00065},
year = {2023},
date = {2023-07-02},
urldate = {2023-07-02},
booktitle = {2023 IEEE 16th International Conference on Cloud Computing (CLOUD)},
publisher = {IEEE},
organization = {IEEE},
abstract = {The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models.},
keywords = {Deep Learning, Distributed Computing, Surrogate Modeling},
pubstate = {published},
tppubtype = {conference}
}
The information explosion drives enterprises and individuals to rent cloud computing infrastructure for their applications in the cloud. However, the agreements between cloud computing providers and clients are often inefficient. We propose an agent-based auto-negotiation system for resource scheduling using fuzzy logic. Our method completes a one-to-one auto-negotiation process and generates optimal offers for providers and clients. We compare the impact of different member functions, fuzzy rule sets, and negotiation scenarios on the offers to optimize the system. Our proposed method efficiently utilizes resources and offers interpretability, high flexibility, and customization. We successfully train machine learning models to replace the fuzzy negotiation system, improving processing speed. The article also highlights potential future improvements to the proposed system and machine learning models.
Javed O; Singh P; Reger G; Toor S
To test, or not to test: A proactive approach for deciding complete performance test initiation Conference
2022 IEEE International Conference on Big Data (Big Data), IEEE, 2022.
@conference{nokey,
title = {To test, or not to test: A proactive approach for deciding complete performance test initiation},
author = {Omar Javed and Prashant Singh and Gilles Reger and Salman Toor},
url = {https://doi.ieeecomputersociety.org/10.1109/BigData55660.2022.10020543},
doi = {10.1109/BigData55660.2022.10020543},
year = {2022},
date = {2022-07-01},
urldate = {2022-07-01},
booktitle = {2022 IEEE International Conference on Big Data (Big Data)},
pages = {4758-4767},
publisher = {IEEE},
abstract = {Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.},
keywords = {Classification, Software, Unsupervised Learning},
pubstate = {published},
tppubtype = {conference}
}
Software performance testing requires a set of inputs that exercise different sections of the code to identify performance issues. However, running tests on a large set of inputs can be a very time consuming process. It is even more problematic when test inputs are constantly growing, which is the case with a large-scale scientific organization such as CERN where the process of performing scientific experiment generates plethora of data that is analyzed by physicists leading to new scientific discoveries. Therefore, in this article, we present a test input minimization approach based on a clustering technique to handle the issue of testing on growing data. Furthermore, we use clustering information to propose an automatic approach that recommends the tester to decide when to run the complete test suite for performance testing. To demonstrate the efficacy of our approach, we applied it to two different code updates of a web service which is used at CERN and we found that the recommendation for performance test initiation made by our approach for an update with bottleneck is valid.
Jiang R; Singh P; Wrede F; Hellander A; Petzold L
Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods Journal Article
In: PLoS computational biology, vol. 18, no. 1, pp. e1009830, 2022.
@article{jiang2022identification,
title = {Identification of dynamic mass-action biochemical reaction networks using sparse Bayesian methods},
author = {Richard Jiang and Prashant Singh and Fredrik Wrede and Andreas Hellander and Linda Petzold},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1009830},
doi = {https://doi.org/10.1371/journal.pcbi.1009830},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {PLoS computational biology},
volume = {18},
number = {1},
pages = {e1009830},
publisher = {Public Library of Science San Francisco, CA USA},
keywords = {Bayesian Inference, Inverse Problem, Model Identification},
pubstate = {published},
tppubtype = {article}
}
Coulier A; Singh P; Sturrock M; Hellander A
Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation Journal Article
In: PLOS Computational Biology, vol. 18, no. 12, pp. e1010683, 2022.
@article{coulier2022systematic,
title = {Systematic comparison of modeling fidelity levels and parameter inference settings applied to negative feedback gene regulation},
author = {Adrien Coulier and Prashant Singh and Marc Sturrock and Andreas Hellander},
url = {https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010683},
doi = {https://doi.org/10.1371/journal.pcbi.1010683},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {PLOS Computational Biology},
volume = {18},
number = {12},
pages = {e1010683},
publisher = {Public Library of Science San Francisco, CA USA},
keywords = {Bayesian Inference, Inverse Problem},
pubstate = {published},
tppubtype = {article}
}
Wrede F; Eriksson R; Jiang R; Petzold L; Engblom S; Hellander A; Singh P
Robust and integrative Bayesian neural networks for likelihood-free parameter inference Proceedings Article
In: 2022 International Joint Conference on Neural Networks (IJCNN), pp. 1–10, IEEE 2022.
@inproceedings{wrede2022robust,
title = {Robust and integrative Bayesian neural networks for likelihood-free parameter inference},
author = {Fredrik Wrede and Robin Eriksson and Richard Jiang and Linda Petzold and Stefan Engblom and Andreas Hellander and Prashant Singh},
url = {https://ieeexplore.ieee.org/abstract/document/9892800
https://arxiv.org/pdf/2102.06521},
doi = {https://doi.org/10.1109/IJCNN55064.2022.9892800},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 International Joint Conference on Neural Networks (IJCNN)},
pages = {1--10},
organization = {IEEE},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Ekmefjord M; Ait-Mlouk A; Alawadi S; Åkesson M; Singh P; Spjuth O; Toor S; Hellander A
Scalable federated machine learning with fedn Proceedings Article
In: 2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid), pp. 555–564, IEEE 2022.
@inproceedings{ekmefjord2022scalable,
title = {Scalable federated machine learning with fedn},
author = {Morgan Ekmefjord and Addi Ait-Mlouk and Sadi Alawadi and Mattias Åkesson and Prashant Singh and Ola Spjuth and Salman Toor and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/9826069
https://arxiv.org/pdf/2103.00148
},
doi = {https://doi.org/10.1109/CCGrid54584.2022.00065},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
booktitle = {2022 22nd IEEE International Symposium on Cluster, Cloud and Internet Computing (CCGrid)},
pages = {555--564},
organization = {IEEE},
keywords = {Federated Learning, Software},
pubstate = {published},
tppubtype = {inproceedings}
}
Javed O; Singh P; Reger G; Toor S
To test, or not to test: A proactive approach for deciding complete performance test initiation Journal Article
In: arXiv preprint arXiv:2205.14749, 2022.
@article{javed2022test,
title = {To test, or not to test: A proactive approach for deciding complete performance test initiation},
author = {Omar Javed and Prashant Singh and Giles Reger and Salman Toor},
url = {https://arxiv.org/pdf/2205.14749},
year = {2022},
date = {2022-01-01},
urldate = {2022-01-01},
journal = {arXiv preprint arXiv:2205.14749},
keywords = {Software, Unsupervised Learning},
pubstate = {published},
tppubtype = {article}
}
Singh P; Wrede F; Hellander A
Scalable machine learning-assisted model exploration and inference using Sciope Journal Article
In: Bioinformatics, vol. 37, no. 2, pp. 279–281, 2021.
@article{singh2021scalable,
title = {Scalable machine learning-assisted model exploration and inference using Sciope},
author = {Prashant Singh and Fredrik Wrede and Andreas Hellander},
url = {https://academic.oup.com/bioinformatics/article/37/2/279/5876021},
doi = {https://doi.org/10.1093/bioinformatics/btaa673},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {2},
pages = {279--281},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem, Optimization, Software, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Jiang R; Jacob B; Geiger M; Matthew S; Rumsey B; Singh P; Wrede F; Yi T; Drawert B; Hellander A; others
Epidemiological modeling in stochss live! Journal Article
In: Bioinformatics, vol. 37, no. 17, pp. 2787–2788, 2021.
@article{jiang2021epidemiological,
title = {Epidemiological modeling in stochss live!},
author = {Richard Jiang and Bruno Jacob and Matthew Geiger and Sean Matthew and Bryan Rumsey and Prashant Singh and Fredrik Wrede and Tau-Mu Yi and Brian Drawert and Andreas Hellander and others},
url = {https://academic.oup.com/bioinformatics/article/37/17/2787/6123781
https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btab061/40342592/btab061.pdf},
doi = {https://doi.org/10.1093/bioinformatics/btab061},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {Bioinformatics},
volume = {37},
number = {17},
pages = {2787--2788},
publisher = {Oxford University Press},
keywords = {Bayesian Inference, Software},
pubstate = {published},
tppubtype = {article}
}
Akesson M; Singh P; Wrede F; Hellander A
Convolutional neural networks as summary statistics for approximate bayesian computation Journal Article
In: IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021.
@article{akesson2021convolutional,
title = {Convolutional neural networks as summary statistics for approximate bayesian computation},
author = {Mattias Akesson and Prashant Singh and Fredrik Wrede and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/9525290},
doi = {https://doi.org/10.1109/TCBB.2021.3108695},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {IEEE/ACM Transactions on Computational Biology and Bioinformatics},
publisher = {IEEE},
keywords = {Bayesian Inference, Deep Learning, Inverse Problem},
pubstate = {published},
tppubtype = {article}
}
Jiang R M; Wrede F; Singh P; Hellander A; Petzold L R
Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators Journal Article
In: BMC bioinformatics, vol. 22, no. 1, pp. 1–17, 2021.
@article{jiang2021accelerated,
title = {Accelerated regression-based summary statistics for discrete stochastic systems via approximate simulators},
author = {Richard M Jiang and Fredrik Wrede and Prashant Singh and Andreas Hellander and Linda R Petzold},
url = {https://link.springer.com/article/10.1186/s12859-021-04255-9},
doi = {https://doi.org/10.1186/s12859-021-04255-9},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
journal = {BMC bioinformatics},
volume = {22},
number = {1},
pages = {1--17},
publisher = {BioMed Central},
keywords = {Bayesian Inference, Inverse Problem, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Ju L; Singh P; Toor S
Proactive autoscaling for edge computing systems with kubernetes Proceedings Article
In: Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion, pp. 1–8, 2021.
@inproceedings{ju2021proactive,
title = {Proactive autoscaling for edge computing systems with kubernetes},
author = {Li Ju and Prashant Singh and Salman Toor},
url = {https://dl.acm.org/doi/abs/10.1145/3492323.3495588},
doi = {https://doi.org/10.1145/3492323.3495588},
year = {2021},
date = {2021-01-01},
urldate = {2021-01-01},
booktitle = {Proceedings of the 14th IEEE/ACM International Conference on Utility and Cloud Computing Companion},
pages = {1--8},
keywords = {Distributed Computing},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Elamin M M; Toor S
Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets Proceedings Article
In: 2020 IEEE Green Technologies Conference (GreenTech), pp. 109–114, IEEE 2020.
@inproceedings{singh2020towards,
title = {Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets},
author = {Prashant Singh and Mona Mohamed Elamin and Salman Toor},
url = {https://ieeexplore.ieee.org/abstract/document/9289758
https://arxiv.org/pdf/2012.09579
},
doi = {https://doi.org/10.1109/GreenTech46478.2020.9289758},
year = {2020},
date = {2020-01-01},
urldate = {2020-01-01},
booktitle = {2020 IEEE Green Technologies Conference (GreenTech)},
pages = {109--114},
organization = {IEEE},
keywords = {Deep Learning, Distributed Computing, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Vats E; Hast A
Learning surrogate models of document image quality metrics for automated document image processing Proceedings Article
In: 2018 13th IAPR International Workshop on Document Analysis Systems (DAS), pp. 67–72, IEEE 2018.
@inproceedings{singh2018learning,
title = {Learning surrogate models of document image quality metrics for automated document image processing},
author = {Prashant Singh and Ekta Vats and Anders Hast},
url = {https://ieeexplore.ieee.org/abstract/document/8395173
https://arxiv.org/pdf/1712.03738},
doi = {https://doi.org/10.1109/DAS.2018.14},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 13th IAPR International Workshop on Document Analysis Systems (DAS)},
pages = {67--72},
organization = {IEEE},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Hellander A
Hyperparameter optimization for approximate Bayesian computation Proceedings Article
In: 2018 Winter Simulation Conference (WSC), pp. 1718–1729, IEEE 2018.
@inproceedings{singh2018hyperparameter,
title = {Hyperparameter optimization for approximate Bayesian computation},
author = {Prashant Singh and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/8632304
},
doi = {https://doi.org/10.1109/WSC.2018.8632304},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {2018 Winter Simulation Conference (WSC)},
pages = {1718--1729},
organization = {IEEE},
keywords = {Bayesian Inference, Inverse Problem, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Hellander A
Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference Proceedings Article
In: ESM 2018, October 24--26, Ghent, Belgium, pp. 22–27, EUROSIS 2018.
@inproceedings{singh2018multi,
title = {Multi-objective optimization driven construction of uniform priors for likelihood-free parameter inference},
author = {Prashant Singh and Andreas Hellander},
year = {2018},
date = {2018-01-01},
urldate = {2018-01-01},
booktitle = {ESM 2018, October 24--26, Ghent, Belgium},
pages = {22--27},
organization = {EUROSIS},
keywords = {Bayesian Inference, Inverse Problem, Optimization},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; van der Herten J; Deschrijver D; Couckuyt I; Dhaene T
A sequential sampling strategy for adaptive classification of computationally expensive data Journal Article
In: Structural and Multidisciplinary Optimization, vol. 55, no. 4, pp. 1425–1438, 2017.
@article{singh2017sequential,
title = {A sequential sampling strategy for adaptive classification of computationally expensive data},
author = {Prashant Singh and Joachim van der Herten and Dirk Deschrijver and Ivo Couckuyt and Tom Dhaene},
url = {https://link.springer.com/article/10.1007/s00158-016-1584-1
https://users.ugent.be/~didschri/papers/2017_04_Springer_SMO.pdf
},
doi = {https://doi.org/10.1007/s00158-016-1584-1},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Structural and Multidisciplinary Optimization},
volume = {55},
number = {4},
pages = {1425--1438},
publisher = {Springer Berlin Heidelberg},
keywords = {Classification, Inverse Problem, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Rossi M; Couckuyt I; Deschrijver D; Rogier H; Dhaene T
Constrained multi-objective antenna design optimization using surrogates Journal Article
In: International Journal of Numerical Modelling: Electronic Networks, Devices and Fields, vol. 30, no. 6, pp. e2248, 2017.
@article{singh2017constrained,
title = {Constrained multi-objective antenna design optimization using surrogates},
author = {Prashant Singh and Marco Rossi and Ivo Couckuyt and Dirk Deschrijver and Hendrik Rogier and Tom Dhaene},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/jnm.2248
http://sumo.intec.ugent.be/sites/default/files/dirk_pubs/2017_11_Wiley_IJNM.pdf},
doi = {https://doi.org/10.1002/jnm.2248},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {International Journal of Numerical Modelling: Electronic Networks, Devices and Fields},
volume = {30},
number = {6},
pages = {e2248},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Couckuyt I; Elsayed K; Deschrijver D; Dhaene T
Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-kriging surrogate models Journal Article
In: Journal of Optimization Theory and Applications, vol. 175, no. 1, pp. 172–193, 2017.
@article{singh2017multi,
title = {Multi-objective geometry optimization of a gas cyclone using triple-fidelity co-kriging surrogate models},
author = {Prashant Singh and Ivo Couckuyt and Khairy Elsayed and Dirk Deschrijver and Tom Dhaene},
url = {https://link.springer.com/article/10.1007/s10957-017-1114-3
http://sumo.intec.ugent.be/sites/default/files/dirk_pubs/2017_10_Springer_JOTA.pdf},
doi = {https://doi.org/10.1007/s10957-017-1114-3},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
journal = {Journal of Optimization Theory and Applications},
volume = {175},
number = {1},
pages = {172--193},
publisher = {Springer US},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Hellander A
Surrogate assisted model reduction for stochastic biochemical reaction networks Proceedings Article
In: 2017 Winter Simulation Conference (WSC), pp. 1773–1783, IEEE 2017.
@inproceedings{singh2017surrogate,
title = {Surrogate assisted model reduction for stochastic biochemical reaction networks},
author = {Prashant Singh and Andreas Hellander},
url = {https://ieeexplore.ieee.org/abstract/document/8247915
},
doi = {https://doi.org/10.1109/WSC.2017.8247915},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {2017 Winter Simulation Conference (WSC)},
pages = {1773--1783},
organization = {IEEE},
keywords = {Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Vats E; Hast A; Singh P
Automatic document image binarization using bayesian optimization Proceedings Article
In: Proceedings of the 4th International Workshop on Historical Document Imaging and Processing, pp. 89–94, 2017.
@inproceedings{vats2017automatic,
title = {Automatic document image binarization using bayesian optimization},
author = {Ekta Vats and Anders Hast and Prashant Singh},
url = {https://dl.acm.org/doi/abs/10.1145/3151509.3151520
https://arxiv.org/pdf/1709.01782
},
doi = {https://doi.org/10.1145/3151509.3151520},
year = {2017},
date = {2017-01-01},
urldate = {2017-01-01},
booktitle = {Proceedings of the 4th International Workshop on Historical Document Imaging and Processing},
pages = {89--94},
keywords = {Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Couckuyt I; Elsayed K; Deschrijver D; Dhaene T
Shape optimization of a cyclone separator using multi-objective surrogate-based optimization Journal Article
In: Applied Mathematical Modelling, vol. 40, no. 5-6, pp. 4248–4259, 2016.
@article{singh2016shape,
title = {Shape optimization of a cyclone separator using multi-objective surrogate-based optimization},
author = {Prashant Singh and Ivo Couckuyt and Khairy Elsayed and Dirk Deschrijver and Tom Dhaene},
url = {https://www.sciencedirect.com/science/article/pii/S0307904X15007210
http://sumo.intec.ugent.be/sites/default/files/dirk_pubs/2016_03_Elsevier_APM.pdf},
doi = {https://doi.org/10.1016/j.apm.2015.11.007},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {Applied Mathematical Modelling},
volume = {40},
number = {5-6},
pages = {4248--4259},
publisher = {Elsevier},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P
Design of experiments for model-based optimization PhD Thesis
Ghent University, 2016.
@phdthesis{singh2016design,
title = {Design of experiments for model-based optimization},
author = {Prashant Singh},
url = {https://biblio.ugent.be/publication/7223691/file/7223692},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
school = {Ghent University},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {phdthesis}
}
Singh P; Claeys T; Vandenbosch G A; Pissoort D
Automated line-based sequential sampling and modeling algorithm for EMC near-field scanning Journal Article
In: IEEE Transactions on Electromagnetic Compatibility, vol. 59, no. 2, pp. 704–709, 2016.
@article{singh2016automated,
title = {Automated line-based sequential sampling and modeling algorithm for EMC near-field scanning},
author = {Prashant Singh and Tim Claeys and Guy AE Vandenbosch and Davy Pissoort},
url = {https://ieeexplore.ieee.org/abstract/document/7776759
https://lirias.kuleuven.be/retrieve/639699},
doi = {https://doi.org/10.1109/TEMC.2016.2632741},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {IEEE Transactions on Electromagnetic Compatibility},
volume = {59},
number = {2},
pages = {704--709},
publisher = {IEEE},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Gong X; Trogh J; Braet Q; Tanghe E; Singh P; Plets D; Hoebeke J; Deschrijver D; Dhaene T; Martens L; others
Measurement-based wireless network planning, monitoring, and reconfiguration solution for robust radio communications in indoor factories Journal Article
In: IET Science, Measurement & Technology, vol. 10, no. 4, pp. 375–382, 2016.
@article{gong2016measurement,
title = {Measurement-based wireless network planning, monitoring, and reconfiguration solution for robust radio communications in indoor factories},
author = {Xu Gong and Jens Trogh and Quentin Braet and Emmeric Tanghe and Prashant Singh and David Plets and Jeroen Hoebeke and Dirk Deschrijver and Tom Dhaene and Luc Martens and others},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/iet-smt.2015.0213
https://ietresearch.onlinelibrary.wiley.com/doi/pdfdirect/10.1049/iet-smt.2015.0213},
doi = {https://doi.org/10.1049/iet-smt.2015.0213},
year = {2016},
date = {2016-01-01},
urldate = {2016-01-01},
journal = {IET Science, Measurement & Technology},
volume = {10},
number = {4},
pages = {375--382},
publisher = {The Institution of Engineering and Technology},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Vermeeren G; Singh P; Aerts S; Deschrijver D; Dhaene T; Joseph W; Martens L
Surrogate-based fast peak mass-averaged SAR assessment Proceedings Article
In: Annual Meeting of the Bioelectromagnetics Society and the European BioElectromagnetics Association (BioEM 2015), pp. 135–137, 2015.
@inproceedings{vermeeren2015surrogate,
title = {Surrogate-based fast peak mass-averaged SAR assessment},
author = {Günter Vermeeren and Prashant Singh and Sam Aerts and Dirk Deschrijver and Tom Dhaene and Wout Joseph and Luc Martens},
url = {https://biblio.ugent.be/publication/7257304/file/7257373.pdf},
year = {2015},
date = {2015-01-01},
urldate = {2015-01-01},
booktitle = {Annual Meeting of the Bioelectromagnetics Society and the European BioElectromagnetics Association (BioEM 2015)},
pages = {135--137},
keywords = {Inverse Problem, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Couckuyt I; Ferranti F; Dhaene T
A constrained multi-objective surrogate-based optimization algorithm Proceedings Article
In: 2014 IEEE Congress on Evolutionary Computation (CEC), pp. 3080–3087, IEEE 2014.
@inproceedings{singh2014constrained,
title = {A constrained multi-objective surrogate-based optimization algorithm},
author = {Prashant Singh and Ivo Couckuyt and Francesco Ferranti and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/6900581
https://biblio.ugent.be/publication/5733039/file/5733050.pdf},
doi = {https://doi.org/10.1109/CEC.2014.6900581},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {2014 IEEE Congress on Evolutionary Computation (CEC)},
pages = {3080--3087},
organization = {IEEE},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Ferranti F; Deschrijver D; Couckuyt I; Dhaene T
Classification aided domain reduction for high dimensional optimization Proceedings Article
In: Proceedings of the Winter Simulation Conference 2014, pp. 3928–3939, IEEE 2014.
@inproceedings{singh2014classification,
title = {Classification aided domain reduction for high dimensional optimization},
author = {Prashant Singh and Francesco Ferranti and Dirk Deschrijver and Ivo Couckuyt and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/7020218
https://biblio.ugent.be/publication/5955500/file/5955522},
doi = {https://doi.org/10.1109/WSC.2014.7020218},
year = {2014},
date = {2014-01-01},
urldate = {2014-01-01},
booktitle = {Proceedings of the Winter Simulation Conference 2014},
pages = {3928--3939},
organization = {IEEE},
keywords = {Classification, Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Deschrijver D; Pissoort D; Dhaene T
Adaptive classification algorithm for EMC-compliance testing of electronic devices Journal Article
In: Electronics Letters, vol. 49, no. 24, pp. 1526–1528, 2013.
@article{singh2013adaptive,
title = {Adaptive classification algorithm for EMC-compliance testing of electronic devices},
author = {Prashant Singh and Dirk Deschrijver and Davy Pissoort and Tom Dhaene},
url = {https://ietresearch.onlinelibrary.wiley.com/doi/full/10.1049/el.2013.2766
https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/el.2013.2766},
doi = {https://doi.org/10.1049/el.2013.2766},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {Electronics Letters},
volume = {49},
number = {24},
pages = {1526--1528},
publisher = {The Institution of Engineering and Technology},
keywords = {Classification, Inverse Problem, Optimization, Surrogate Modeling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Deschrijver D; Dhaene T
A balanced sequential design strategy for global surrogate modeling Proceedings Article
In: 2013 Winter Simulations Conference (WSC), pp. 2172–2179, IEEE 2013.
@inproceedings{singh2013balanced,
title = {A balanced sequential design strategy for global surrogate modeling},
author = {Prashant Singh and Dirk Deschrijver and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/6721594
https://biblio.ugent.be/publication/4315532/file/4315539},
doi = {https://doi.org/10.1109/WSC.2013.6721594},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
booktitle = {2013 Winter Simulations Conference (WSC)},
pages = {2172--2179},
organization = {IEEE},
keywords = {Optimization, Sampling, Surrogate Modeling},
pubstate = {published},
tppubtype = {inproceedings}
}
Singh P; Deschrijver D; Pissoort D; Dhaene T
Accurate hotspot localization by sampling the near-field pattern of electronic devices Journal Article
In: IEEE Transactions on Electromagnetic Compatibility, vol. 55, no. 6, pp. 1365–1368, 2013.
@article{singh2013accurate,
title = {Accurate hotspot localization by sampling the near-field pattern of electronic devices},
author = {Prashant Singh and Dirk Deschrijver and Davy Pissoort and Tom Dhaene},
url = {https://ieeexplore.ieee.org/abstract/document/6522868
https://biblio.ugent.be/publication/4210867/file/4210870.pdf},
doi = {https://doi.org/10.1109/TEMC.2013.2265158},
year = {2013},
date = {2013-01-01},
urldate = {2013-01-01},
journal = {IEEE Transactions on Electromagnetic Compatibility},
volume = {55},
number = {6},
pages = {1365--1368},
publisher = {IEEE},
keywords = {Inverse Problem, Optimization, Sampling},
pubstate = {published},
tppubtype = {article}
}
Singh P; Deschrijver D; Pissoort D; Dhaene T
Efficient measurement procedure for hotspot detection in near-field pattern of electronic devices Proceedings Article
In: BESTCOM Meeting, 2013.
@inproceedings{singh2013efficient,
title = {Efficient measurement procedure for hotspot detection in near-field pattern of electronic devices},
author = {Prashant Singh and Dirk Deschrijver and Davy Pissoort and Tom Dhaene},
url = {https://biblio.ugent.be/publication/4083180/file/4083328.pdf},
year = {2013},
date = {2013-01-01},
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}