Search Results for author: Andreas Hellander

Found 7 papers, 3 papers with code

FedQAS: Privacy-aware machine reading comprehension with federated learning

1 code implementation9 Feb 2022 Addi Ait-Mlouk, Sadi Alawadi, Salman Toor, Andreas Hellander

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.

Conversational Question Answering Federated Learning +3

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

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

Apache Spark Streaming and HarmonicIO: A Performance and Architecture Comparison

no code implementations20 Jul 2018 Ben Blamey, Andreas Hellander, Salman Toor

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

2 code implementations28 Jun 2018 Adrien Coulier, Andreas Hellander

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.

Distributed Computing

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

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