Improving transparency and representational generalizability through parallel continual learning.

This paper takes a parallel learning approach in continual learning scenarios. We define parallel continual learning as learning a sequence of tasks where the data for the previous tasks, whose distribution may have shifted over time, are also available while learning new tasks. We propose a parallel continual learning method by assigning subnetworks to each task, and simultaneously training only the assigned subnetworks on their corresponding tasks.

Scaling of internal organs during Drosophila embryonic development.

Many species show a diverse range of sizes; for example, domestic dogs have large variation in body mass. Yet, the internal structure of the organism remains similar, i.e.