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📚TheoryIntermediate

Perceptual Loss & Feature Matching

Perceptual loss compares images in a deep network's feature space rather than raw pixels, which aligns better with human judgment of similarity.

#perceptual loss#feature matching#gan+12
📚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
Advanced
Filtering by:
#gram matrix
#kernel ridge regression
#lazy training
+12
📚TheoryAdvanced

Neural Tangent Kernel (NTK)

Neural Tangent Kernel (NTK) describes how wide neural networks train like kernel machines, turning gradient descent into kernel regression in the infinite-width limit.

#neural tangent kernel#ntk#nngp+12
📚TheoryAdvanced

Reproducing Kernel Hilbert Spaces (RKHS)

An RKHS is a space of functions where evaluating a function at a point equals taking an inner product with a kernel section, which enables the “kernel trick.”

#rkhs#kernel trick#gram matrix+12
∑MathIntermediate

Positive Definite Matrices

A real symmetric matrix A is positive definite if and only if x^T A x > 0 for every nonzero vector x, and positive semidefinite if x^T A x ≥ 0.

#positive definite#positive semidefinite#cholesky decomposition+11