A more effective way to train machines for uncertain, real-world situations
Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
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Researchers develop an algorithm that decides when a “student” machine should follow its teacher, and when it should learn on its own.
Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.
The machine-learning algorithm identified a compound that kills Acinetobacter baumannii, a bacterium that lurks in many hospital settings.
This machine-learning method could assist with robotic scene understanding, image editing, or online recommendation systems.
A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.