Efficient Learning Algorithms With Limited Resources

This article discusses the challenges of implementing machine learning in real-world scenarios where data and computational resources are scarce. The authors propose exploring efficient learning methodologies that merge data and machine efficiency, such as compressed networks, to reduce data volume. They invite experts from various fields to collaborate and contribute to the development of innovative methodologies that can reshape the trajectory of machine learning and its applications.