Yoshua Bengio Team Challenges the Task-Diversity Paradigm in Meta-Learning | Synced

A research team from Mila, Québec Artificial Intelligence Institute, Université de Montréal, CIFAR and IVADO Labs challenges the assumption that task diversity will improve model performance in meta-learning, finding instead that repeating the same tasks over the training phase can achieve performance similar to models trained on uniform sampling.