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Found 8 papers, 4 papers with code
Model Uncertainty in Evolutionary Optimization and Bayesian Optimization: A Comparative Analysis
Black-box optimization problems, which are common in many real-world applications, require optimization through input-output interactions without access to internal workings.
Enhancing SAEAs with Unevaluated Solutions: A Case Study of Relation Model for Expensive Optimization
Furthermore, the surrogate-selected unevaluated solutions with high potential have been shown to significantly enhance the efficiency of the algorithm.
Arbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transport
Finding a transformation between two unknown probability distributions from finite samples is crucial for modeling complex data distributions and performing tasks such as sample generation, domain adaptation and statistical inference.
NF-ULA: Langevin Monte Carlo with Normalizing Flow Prior for Imaging Inverse Problems
Bayesian methods for solving inverse problems are a powerful alternative to classical methods since the Bayesian approach offers the ability to quantify the uncertainty in the solution.
A Dataset-free Deep learning Method for Low-Dose CT Image Reconstruction
Low-dose CT (LDCT) imaging attracted a considerable interest for the reduction of the object's exposure to X-ray radiation.
Robust Graph Representation Learning for Local Corruption Recovery
The performance of graph representation learning is affected by the quality of graph input.
Semi-Implicit Back Propagation
Neural network has attracted great attention for a long time and many researchers are devoted to improve the effectiveness of neural network training algorithms.
An Edge Driven Wavelet Frame Model for Image Restoration
Wavelet frame systems are known to be effective in capturing singularities from noisy and degraded images.
