Adaptive Meta-Loss Networks: Learning Task-Agnostic Loss Functions via Evolutionary Optimization
Mirna Yunita, Xiabi Liu, Zhaoyang Hai, Rachmat MuwardiNon-Scopus1 Januari 2026Afiliasi: ID
Abstrak
Designing appropriate loss functions is critical to the success of supervised learning models. However, most conventional losses are fixed and manually designed, making them suboptimal for diverse and dynamic learning scenari... | Find, read and cite all the research you need on Tech Science Press
Identifier
- OpenAlex: https://openalex.org/W7128928259
- DOI: https://doi.org/10.32604/cmc.2026.075073