Machine learning for synthetic data generation: a review
Machine learning heavily relies on data, but real-world applications often encounter various
data-related issues. These include data of poor quality, insufficient data points leading to …
data-related issues. These include data of poor quality, insufficient data points leading to …
Uncertainty quantification and interpretability for clinical trial approval prediction
Background: Clinical trial is a crucial step in the development of a new therapy (eg,
medication) and is remarkably expensive and time-consuming. Forecasting the approval of …
medication) and is remarkably expensive and time-consuming. Forecasting the approval of …
Synthetic data in AI: Challenges, applications, and ethical implications
In the rapidly evolving field of artificial intelligence, the creation and utilization of synthetic
datasets have become increasingly significant. This report delves into the multifaceted …
datasets have become increasingly significant. This report delves into the multifaceted …
Extra Proximal-Gradient Network with Learned Regularization for Image Compressive Sensing Reconstruction
Learned optimization algorithms are promising approaches to inverse problems by
leveraging advanced numerical optimization schemes and deep neural network techniques …
leveraging advanced numerical optimization schemes and deep neural network techniques …
Distributed dynamic safe screening algorithms for sparse regularization
Distributed optimization has been widely used as one of the most efficient approaches for
model training with massive samples. However, large-scale learning problems with both …
model training with massive samples. However, large-scale learning problems with both …