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Found 6 papers, 1 papers with code
On Optimizing Hyperparameters for Quantum Neural Networks
The increasing capabilities of Machine Learning (ML) models go hand in hand with an immense amount of data and computational power required for training.
FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN
Federated Learning (FL) provides a privacy-preserving mechanism for distributed training of machine learning models on networked devices (e. g., mobile devices, IoT edge nodes).
Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case
However, challenges such as (1) the encoding of data from the classical to the quantum domain, (2) hyperparameter tuning, and (3) the integration of quantum hardware into a distributed computing continuum limit the adoption of quantum machine learning for urgent analytics.
SymED: Adaptive and Online Symbolic Representation of Data on the Edge
Symbolic Representation (SR) algorithms are promising solutions to reduce the data size by converting actual raw data into symbols.
Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures
Microgrids (MGs) are important players for the future transactive energy systems where a number of intelligent Internet of Things (IoT) devices interact for energy management in the smart grid.
Towards an Integrated Platform for Big Data Analysis
This paper presents the vision of an integrated plat-form for big data analysis that combines all these aspects.
