<ul><li>The mysterious generalization abilities of overparameterized neural networks are conventionally attributed to gradient descent.</li><li>The volume hypothesis challenges this view by suggesting that neural networks can generalize well even without gradient descent, using Guess & Check method.</li><li>A recent theoretical study investigated this hypothesis for matrix factorization and found that generalization under Guess & Check deteriorates with increasing width, while it improves with increasing depth.</li><li>The study highlights the complexity of understanding whether neural networks require gradient descent to generalize effectively, even in simple settings.</li></ul>

Do Neural Networks Need Gradient Descent to Generalize? A Theoretical Study

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