Why Deep Learning Works Even Though It Shouldn't (moultano.wordpress.com)

A new perspective on deep learning challenges traditional statistical views by proposing intuitive explanations for why larger, deeper models consistently outperform their smaller counterparts, even with limited data. The author argues that in high-dimensional spaces, all sets of parameters are relatively close to optimal values, which negates concerns about local optima that statistical purists often raise. Key observations include the idea that with sufficient dimensions, models can move freely through parameter space, making it improbable that they will get trapped in local minima. This suggests that all initialization points could lead to good outcomes, further explaining the success of deep learning models. This exploration is significant for the AI/ML community as it prompts a re-evaluation of optimization strategies, particularly the emphasis on finding minima in model training. Instead, the focus should shift towards understanding how optimization algorithms prioritize learning relevant features. The insights encourage researchers to consider the averaging effect of deep models as they integrate knowledge from various representations, leading to generalized performance improvements. This shift in perspective could enhance research methodologies, providing a more comprehensive framework for understanding deep learning dynamics beyond conventional metrics.