An Evolutionary Approach to Dynamic Introduction of Tasks in Large-scale Multitask Learning Systems. (arXiv:2205.12755v1 [cs.LG])

Multitask learning assumes that models capable of learning from multiple
tasks can achieve better quality and efficiency via knowledge transfer, a key
feature of human learning. Though, state of the art ML models rely on high
customization for each task and leverage size and data scale rather than
scaling the number of tasks. Also, continual learning, that adds the temporal
aspect to multitask, is often focused to the study of common pitfalls such as
catastrophic forgetting instead of being studied …

arxiv introduction learning multitask learning scale systems