Machine learning models—especially large-scale ones like GPT, BERT, or DALL·E—are trained using enormous volumes of data.

In addition, the end result of training a particular algorithm on particular training data is a machine learning model. The model represents what the machine has learned for a particular task.

Using a three-year growth forecast model, our analysis estimates that research on transfer learning methods will grow the fastest through 2023 among the small data categories we considered.

Best machine learning model for sparse data. To help combat these issues that arise with sparse data machine learning, there are a few things to do. First, because of the noise in the model, it’s ...

Training data is the backbone of AI and machine learning systems. The data’s quality, diversity, and volume directly affect the model’s ability to learn and generalize.

The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

More information: Yuqiao Yang et al, Patch-Based Deep-Learning Model With Limited Training Dataset for Liver Tumor Segmentation in Contrast-Enhanced Hepatic Computed Tomography, IEEE Access (2025 ...