Generating synthetic data is useful when you have imbalanced training data for a particular class, for example, generating synthetic females in a dataset of employees that has many males but few ...
The key challenge in credit card fraud detection lies in the imbalance between legitimate and fraudulent transactions. Fraud cases typically represent less than 1 percent of total transactions, ...
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Interrupting encoder training in diffusion models enables more efficient generative AI
A new framework for generative diffusion models was developed by researchers at Science Tokyo, significantly improving ...
Advancements in whole-genome sequencing have revolutionized plant species characterization, providing a wealth of genotypic data for analysis. The combination of genomic selection and neural networks, ...
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