Attention is an important mechanism that can be employed for a variety of deep learning models across many different domains and tasks. Additionally, the various measures for evaluating attention models are reviewed, and methods to characterize the structure of attention models based on the proposed framework are discussed.
This research advances how AI systems learn, reason, and solve problems — with direct implications for automation and scientific discovery.
Read the full paper
Access the original peer-reviewed research via OpenAlex.
| Category | 🤖 Artificial Intelligence |
| Published | Nov 09, 2021 |
| Journal | IEEE Transactions on Knowledge and Data Engineering |
| Authors | Gianni Brauwers, Flavius Frăsincar |
| DOI | 10.1109/tkde.2021.3126456 |
| Citations | 636 |
| Source | OpenAlex |