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Found 7 papers, 1 papers with code
Entropy-based Guidance of Deep Neural Networks for Accelerated Convergence and Improved Performance
Neural networks have dramatically increased our capacity to learn from large, high-dimensional datasets across innumerable disciplines.
Keep It Simple: Fault Tolerance Evaluation of Federated Learning with Unreliable Clients
Federated learning (FL), as an emerging artificial intelligence (AI) approach, enables decentralized model training across multiple devices without exposing their local training data.
Feature Extractor Stacking for Cross-domain Few-shot Learning
Recently published CDFSL methods generally construct a universal model that combines knowledge of multiple source domains into one feature extractor.
Automatic end-to-end De-identification: Is high accuracy the only metric?
De-identification of electronic health records (EHR) is a vital step towards advancing health informatics research and maximising the use of available data.
A survey of automatic de-identification of longitudinal clinical narratives
Use of medical data, also known as electronic health records, in research helps develop and advance medical science.
Using Swarm Optimization To Enhance Autoencoders Images
We propose a new approach that improves the accuracy of a trained autoencoders results and answers the following question, Given a trained autoencoder, a test image, and using a real-parameter optimizer, can we generate better quality reconstructed image version than the one generated by the autoencoder?.
Improving Naive Bayes for Regression with Optimised Artificial Surrogate Data
To investigate this question we use population-based optimisation algorithms to generate artificial surrogate training data for naive Bayes for regression.
