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09/08/2023
Robust Representation Learning for Privacy-Preserving Machine Learning: A Multi-Objective Autoencoder Approach
Several domains increasingly rely on machine learning in their applicati...
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01/16/2023
PlasmoFAB: A Benchmark to Foster Machine Learning for Plasmodium falciparum Protein Antigen Candidate Prediction
Motivation: Machine learning methods can be used to support scientific d...
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12/07/2022
Bringing the Algorithms to the Data – Secure Distributed Medical Analytics using the Personal Health Train (PHT-meDIC)
The need for data privacy and security – enforced through increasingly s...
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12/02/2022
COmic: Convolutional Kernel Networks for Interpretable End-to-End Learning on (Multi-)Omics Data
Motivation: The size of available omics datasets is steadily increasing ...
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02/07/2022
CECILIA: Comprehensive Secure Machine Learning Framework
Since machine learning algorithms have proven their success in data mini...
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11/03/2021
Convolutional Motif Kernel Networks
Artificial neural networks are exceptionally good in learning to detect ...
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02/17/2021
ppAUC: Privacy Preserving Area Under the Curve with Secure 3-Party Computation
Computing an AUC as a performance measure to compare the quality of diff...
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12/04/2020
ESCAPED: Efficient Secure and Private Dot Product Framework for Kernel-based Machine Learning Algorithms with Applications in Healthcare
To train sophisticated machine learning models one usually needs many tr...
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11/06/2019
Privacy Preserving Gaze Estimation using Synthetic Images via a Randomized Encoding Based Framework
Eye tracking is handled as one of the key technologies for applications ...
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11/20/2018
An interpretable multiple kernel learning approach for the discovery of integrative cancer subtypes
Due to the complexity of cancer, clustering algorithms have been used to...
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01/02/2017
Towards multiple kernel principal component analysis for integrative analysis of tumor samples
Personalized treatment of patients based on tissue-specific cancer subty...
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12/20/2016
