Search Results for author: Prashant Singh

Found 9 papers, 2 papers with code

Coarse-grained Stochastic Model of Myosin-Driven Vesicles into Dendritic Spines

1 code implementation15 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.

Scalable federated machine learning with FEDn

2 code implementations27 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.

Federated Learning

Robust and integrative Bayesian neural networks for likelihood-free parameter inference

no code implementations12 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.

Density Estimation

Towards Smart e-Infrastructures, A Community Driven Approach Based on Real Datasets

no code implementations17 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

Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation

no code implementations31 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.

Experimental Design Time Series

Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits

no code implementations22 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.

Feature Engineering Multi-Armed Bandits +1

Learning Surrogate Models of Document Image Quality Metrics for Automated Document Image Processing

no code implementations11 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.

Binarization Hyperparameter Optimization

Automatic Document Image Binarization using Bayesian Optimization

no code implementations6 Sep 2017 Ekta Vats, Anders Hast, Prashant Singh

Document image binarization is often a challenging task due to various forms of degradation.


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