Foundational studies have also evaluated various data generation methods through systematic benchmarking, demonstrating that tailored synthetic datasets can effectively accelerate methodological ...

To this purpose, we aimed to (1) apply innovative synthetic data generation methods to real-world data sets of different hematologic malignancies including comprehensive clinical and genomic ...

Part of the challenge for regulators, researchers and developers is the wide range of synthetic data generation methods, outputs and uses. A one-size-fits-all response cannot answer the question of ...

To address the difficulty of training high-quality models in some specific domains due to the lack of fine-grained annotation resources, we propose in this paper a knowledge-integrated cross-domain ...

Pew Research Center illustration (Related posts: 5 things to keep in mind when you hear about Gen Z, Millennials, Boomers and other generations and How Pew Research Center will report on generations ...

This repository provides the Pytorch implementation of DGH as described in the paper "Data Generation for Hardware-Friendly Post-Training Quantization". DGH is a method for generating images designed ...

In the process of data collection of wind power generation, due to equipment, human and other reasons, data is often missing. Therefore, it is necessary to fill in missing data to ensure the accuracy ...

Through data generation, large amounts of target data can supplement the original dataset, thereby improving the performance of data-driven methods. The case studies on the IEEE 39-bus system and the ...

Bristol's Quantitative Research Methods (QRM) degrees, also known as 'Q-Step', are social science degrees with enhanced quantitative training. These courses are designed to advance your understanding ...