<ul data-eligibleForWebStory="false"><li>Researchers introduce a Bayesian approach to understand the success of overparameterized deep neural networks (DNNs) by considering network architecture, training algorithms, and data structure.</li><li>They show that DNNs exhibit an Occam's razor-like inductive bias towards simple functions, which helps counteract the growth of complex functions, leading to their remarkable performance.</li><li>By analyzing Boolean function classification and utilizing a prior over functions determined by the network, researchers accurately predict the posterior for DNNs trained with stochastic gradient descent.</li><li>This study demonstrates that structured data and the intrinsic Occam's razor principle play a significant role in the success of deep neural networks.</li></ul>

Deep neural networks have an inbuilt Occam's razor

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