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📚TheoryAdvanced

Feature Learning vs Kernel Regime

The kernel (lazy) regime keeps neural network parameters close to their initialization, making training equivalent to kernel regression with a fixed kernel such as the Neural Tangent Kernel (NTK).

#neural tangent kernel#kernel ridge regression#lazy training+12
📚TheoryIntermediate

Implicit Bias of Gradient Descent

In underdetermined linear systems (more variables than equations), gradient descent started at zero converges to the minimum Euclidean norm solution without any explicit regularizer.

Advanced
Filtering by:
#overparameterization
#implicit bias
#gradient descent
#minimum norm
+12
📚TheoryIntermediate

Lottery Ticket Hypothesis

The Lottery Ticket Hypothesis (LTH) says that inside a large dense neural network there exist small sparse subnetworks that, when trained in isolation from their original initialization, can reach comparable accuracy to the full model.

#lottery ticket hypothesis#magnitude pruning#sparsity+12
📚TheoryIntermediate

Double Descent Phenomenon

Double descent describes how test error first follows the classic U-shape with increasing model complexity, spikes near the interpolation threshold, and then drops again in the highly overparameterized regime.

#double descent#interpolation threshold#overparameterization+12
📚TheoryAdvanced

Neural Tangent Kernel (NTK) Theory

The Neural Tangent Kernel (NTK) connects very wide neural networks to classical kernel methods, letting us study training as if it were kernel regression.

#neural tangent kernel#ntk#infinite width+12