Knowledge distillation is an increasingly influential technique in deep learning that involves transferring the knowledge embedded in a large, complex “teacher” network to a smaller, more efficient ...

Although few-shot learning (FSL) has achieved great progress, it is still an enormous challenge especially when the source and target sets are from different domains, which is also known as ...

A woman holds a cell phone in front of a computer screen displaying the DeepSeek logo (Photo by Artur Widak, NurPhoto via Getty Images) At this month’s Paris AI Summit, the global conversation around ...

What if the most powerful artificial intelligence models could teach their smaller, more efficient counterparts everything they know—without sacrificing performance? This isn’t science fiction; it’s ...

Businesses are increasingly aiming to scale AI, but they often encounter constraints such as infrastructure costs and computational demands. Although large language models (LLMs) offer great potential ...

Google has been a significant contributor to technological innovation, influencing various industries through its projects. The PageRank algorithm altered how information is organized and accessed ...

Crowdsourcing efficiently delegates tasks to crowd workers for labeling, though their varying expertise can lead to errors. A key task is estimating worker expertise to infer true labels. However, the ...

Distillation, also known as model or knowledge distillation, is a process where knowledge is transferred from a large, complex AI ‘teacher’ model to a smaller and more efficient ‘student’ model. Doing ...