Implicit Regularization Effects in Multi-Task Learning and Fine-Tuning of Overparameterized Neural Networks
The author explores implicit regularization effects in multi-task learning and fine-tuning, highlighting biases towards feature sharing and sparse task-specific feature learning. The study uncovers a novel nested feature selection regime that enhances performance through sparsity.