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.
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
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
The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator.
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.
Computation of document image quality metrics often depends upon the availability of a ground truth image corresponding to the document.