no code implementations • 10 Feb 2024 • Junjie Chu, Prashant Singh, Salman Toor
We successfully train machine learning models to replace the fuzzy negotiation system to improve processing speed.
no code implementations • 21 Jan 2024 • Liang Cheng, Prashant Singh, Francesco Ferranti
An inverse modeling approach avoids the need for coupling a forward model with an optimizer and directly performs the prediction of the optimal design parameters values.
1 code implementation • 1 Dec 2023 • Aleksandr Karakulev, Dave Zachariah, Prashant Singh
We present an efficient parameter-free approach for statistical learning from corrupted training sets.
no code implementations • 17 Aug 2023 • Colby Fronk, Jaewoong Yun, Prashant Singh, Linda Petzold
Symbolic regression with polynomial neural networks and polynomial neural ordinary differential equations (ODEs) are two recent and powerful approaches for equation recovery of many science and engineering problems.
1 code implementation • 15 Jul 2021 • Youngmin Park, Prashant Singh, Thomas G. Fai
We consider the stochastic analog of the vesicle transport model in [Park and Fai, The Dynamics of Vesicles Driven Into Closed Constrictions by Molecular Motors.
no code implementations • 4 May 2021 • Anusua Trivedi, Mohit Jain, Nikhil Kumar Gupta, Markus Hinsche, Prashant Singh, Markus Matiaschek, Tristan Behrens, Mirco Militeri, Cameron Birge, Shivangi Kaushik, Archisman Mohapatra, Rita Chatterjee, Rahul Dodhia, Juan Lavista Ferres
Malnutrition is a global health crisis and is the leading cause of death among children under five.
2 code implementations • 27 Feb 2021 • Morgan Ekmefjord, Addi Ait-Mlouk, Sadi Alawadi, Mattias Åkesson, Prashant Singh, Ola Spjuth, Salman Toor, Andreas Hellander
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.
no code implementations • 12 Feb 2021 • Fredrik Wrede, Robin Eriksson, Richard Jiang, Linda Petzold, Stefan Engblom, Andreas Hellander, Prashant Singh
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
no code implementations • 17 Dec 2020 • Prashant Singh, Mona Mohamed Elamin, Salman Toor
This problem is more visible in the context of medium and small scale data center operators (the long tail of e-infrastructure providers).
Distributed, Parallel, and Cluster Computing
no code implementations • 31 Jan 2020 • Mattias Åkesson, Prashant Singh, Fredrik Wrede, Andreas Hellander
The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator.
no code implementations • 22 May 2018 • Prashant Singh, Andreas Hellander
This allows approximate Bayesian computation rejection sampling to dynamically focus on a distribution over well performing summary statistics as opposed to a fixed set of statistics.
no code implementations • 11 Dec 2017 • Prashant Singh, Ekta Vats, Anders Hast
Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document.
no code implementations • 6 Sep 2017 • Ekta Vats, Anders Hast, Prashant Singh
Document image binarization is often a challenging task due to various forms of degradation.