3 accepted papers
Many program synthesis tasks prove too challenging for even state-of-the-art language models to solve in single attempts. Search-based evolutionary methods offer a promising alternative by exploring solution spaces iteratively, but their effectiveness remain limited by the fixed capabilities of the…
The ability to invent novel and interesting problems is a remarkable feature of human intelligence that drives innovation, art, and science. We propose a method that aims to automate this process by harnessing the power of state-of-the-art generative models to produce a diversity of challenging yet…
"This paper addresses the very challenging problem of online task-free continual learning in which a sequence of new tasks is learned from non-stationary data using each sample only once for training and without knowledge of task boundaries. We propose in this paper an efficient semi-distributed ass…