Micro-Data Learning: The Other End of the Spectrum

Abstract

Many fields are now snowed under with an avalanche of data, which raises considerable challenges for computer scientists. Meanwhile, robotics (among other fields) can often only use a few dozen data points because acquiring them involves a process that is expensive or time-consuming. How can an algorithm learn with only a few data points?

References in corpus (1)

• Gaussian Processes for Data-Efficient Learning in Robotics and Control

Cited by in corpus (2)

• From exploration to control: learning object manipulation skills through novelty search and local adaptation
• Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose?