Deep Learning Is Not So Mysterious or Different (arxiv.org)

A recent study argues that the peculiar generalization behaviors observed in deep neural networks, such as benign overfitting and double descent, are not unique to these models but can be understood through established frameworks like PAC-Bayes. The authors propose that instead of viewing deep learning as a distinct class of models, researchers should recognize it as part of a broader family where soft inductive biases help to explain why these phenomena occur. By embracing a flexible hypothesis space that encourages simpler solutions consistent with the data, the findings reveal that the behaviors seen in neural networks can be intuitively understood and rigorously characterized. This research is significant for the AI/ML community as it challenges the prevailing notion that deep learning operates outside traditional machine learning principles. It suggests that many of the unique traits of deep learning, such as representation learning and mode connectivity, might arise from the same foundational ideas that govern all model classes. The study emphasizes that recognizing the connections between deep learning and other modeling techniques could lead to further advancements in understanding and optimizing these systems, potentially paving the way for new strategies in model development and application.