Given the high computational requirements of neural network models, efficient execution is paramount. When performed trillions of times per second even the tiniest inefficiencies are multiplied into ...

Interpretability has drawn increasing attention in machine learning. Partially linear additive models provide an attractive middle ground between the simplicity of generalized linear model and the ...

Artificial intelligence (AI) is increasingly transforming computational mechanics, yet many AI-driven models remain limited by poor interpretability, weak generalization, and insufficient physical ...

Engineers have uncovered an unexpected pattern in how neural networks -- the systems leading today's AI revolution -- learn, suggesting an answer to one of the most important unanswered questions in ...

The simplified approach makes it easier to see how neural networks produce the outputs they do. A tweak to the way artificial neurons work in neural networks could make AIs easier to decipher.