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Found 7 papers, 3 papers with code
FedQAS: Privacy-aware machine reading comprehension with federated learning
In addition, we present the architecture and implementation of the system, as well as provide a reference evaluation based on the SQUAD dataset, to showcase how it overcomes data privacy issues and enables knowledge sharing between alliance members in a Federated learning setting.
Scalable federated machine learning with FEDn
Federated machine learning has great promise to overcome the input privacy challenge in machine learning.
Robust and integrative Bayesian neural networks for likelihood-free parameter inference
State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference.
Convolutional Neural Networks as Summary Statistics for Approximate Bayesian Computation
The proposed approach is demonstrated on two benchmark problem and one challenging inference problem learning parameters in a high-dimensional stochastic genetic oscillator.
Apache Spark Streaming and HarmonicIO: A Performance and Architecture Comparison
Studies have demonstrated that Apache Spark, Flink and related frameworks can perform stream processing at very high frequencies, whilst tending to focus on small messages with a computationally light `map' stage for each message; a common enterprise use case.
Distributed, Parallel, and Cluster Computing
Orchestral: a lightweight framework for parallel simulations of cell-cell communication
By the use of operator-splitting we decouple the simulation of reaction-diffusion kinetics inside the cells from the simulation of molecular cell-cell interactions occurring on the boundaries between cells.
Multi-Statistic Approximate Bayesian Computation with Multi-Armed Bandits
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.
