<ul data-eligibleForWebStory="false"><li>Deep neural networks are often viewed as different due to their generalization behavior, including benign overfitting and double descent.</li><li>The anomalies observed in neural networks can be understood using traditional generalization frameworks like PAC-Bayes.</li><li>The concept of soft inductive biases is key in explaining neural networks' generalization behavior, advocating for a flexible hypothesis space.</li><li>While deep learning shares commonalities with other model classes, it stands out in representation learning and universality.</li></ul>

Deep Learning is Not So Mysterious or Different

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