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Fedaux: Leveraging unlabeled auxiliary data in federated learning
Federated distillation (FD) is a popular novel algorithmic paradigm for Federated learning
(FL), which achieves training performance competitive to prior parameter averaging-based …
(FL), which achieves training performance competitive to prior parameter averaging-based …
Creinns: Credal-set interval neural networks for uncertainty estimation in classification tasks
Effective uncertainty estimation is becoming increasingly attractive for enhancing the
reliability of neural networks. This work presents a novel approach, termed Credal-Set …
reliability of neural networks. This work presents a novel approach, termed Credal-Set …
Machine learning for health: algorithm auditing & quality control
L Oala, AG Murchison, P Balachandran… - Journal of medical …, 2021 - Springer
Developers proposing new machine learning for health (ML4H) tools often pledge to match
or even surpass the performance of existing tools, yet the reality is usually more …
or even surpass the performance of existing tools, yet the reality is usually more …
The Troublesome Kernel: On Hallucinations, No Free Lunches, and the Accuracy-Stability Tradeoff in Inverse Problems
Methods inspired by artificial intelligence (AI) are starting to fundamentally change
computational science and engineering through breakthrough performance on challenging …
computational science and engineering through breakthrough performance on challenging …
An introduction to optimization under uncertainty--A short survey
Optimization equips engineers and scientists in a variety of fields with the ability to transcribe
their problems into a generic formulation and receive optimal solutions with relative ease …
their problems into a generic formulation and receive optimal solutions with relative ease …
Dataset similarity to assess semisupervised learning under distribution mismatch between the labeled and unlabeled datasets
Semisupervised deep learning (SSDL) is a popular strategy to leverage unlabeled data for
machine learning when labeled data is not readily available. In real-world scenarios …
machine learning when labeled data is not readily available. In real-world scenarios …
A smoothing interval neural networks-based Caputo fractional-order gradient learning algorithm
Q Shao, Y Liu, R Wang, Y Liu - International Journal of Machine Learning …, 2024 - Springer
Smoothing interval neural networks (SINNs) are widely recognized for their effectiveness in
handling uncertain data across various domains. However, training SINNs using the integer …
handling uncertain data across various domains. However, training SINNs using the integer …
Data models for dataset drift controls in machine learning with optical images
Camera images are ubiquitous in machine learning research. They also play a central role
in the delivery of important services spanning medicine and environmental surveying …
in the delivery of important services spanning medicine and environmental surveying …
[PDF][PDF] FG-AI4H DEL5. 4 Training and test data specification
S Sector - itu.int
Summary ITU-T FG-AI4H Deliverable DEL5. 4 provides guidelines on the systematic way of
preparing technical requirements specifications for datasets used in the training and testing …
preparing technical requirements specifications for datasets used in the training and testing …