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Found 8 papers, 5 papers with code
Label Noise-Robust Learning using a Confidence-Based Sieving Strategy
Identifying the samples with noisy labels and preventing the model from learning them is a promising approach to address this challenge.
Kernel Normalized Convolutional Networks
Existing convolutional neural network architectures frequently rely upon batch normalization (BatchNorm) to effectively train the model.
HyFed: A Hybrid Federated Framework for Privacy-preserving Machine Learning
Federated learning (FL) enables multiple clients to jointly train a global model under the coordination of a central server.
The FeatureCloud AI Store for Federated Learning in Biomedicine and Beyond
Machine Learning (ML) and Artificial Intelligence (AI) have shown promising results in many areas and are driven by the increasing amount of available data.
Federated Multi-Mini-Batch: An Efficient Training Approach to Federated Learning in Non-IID Environments
Federated learning has faced performance and network communication challenges, especially in the environments where the data is not independent and identically distributed (IID) across the clients.
Flimma: a federated and privacy-preserving tool for differential gene expression analysis
Aggregating transcriptomics data across hospitals can increase sensitivity and robustness of differential expression analyses, yielding deeper clinical insights.
Privacy-preserving Artificial Intelligence Techniques in Biomedicine
Artificial intelligence (AI) has been successfully applied in numerous scientific domains.
DP-CGAN: Differentially Private Synthetic Data and Label Generation
Generative Adversarial Networks (GANs) are one of the well-known models to generate synthetic data including images, especially for research communities that cannot use original sensitive datasets because they are not publicly accessible.
