**Prescribed GANs** add noise to the output of a density network and optimize an entropy-regularized adversarial loss. The added noise renders tractable approximations of the predictive log-likelihood and stabilizes the training procedure. The entropy regularizer encourages PresGANs to capture all the modes of the data distribution. Fitting PresGANs involves computing the intractable gradients of the [entropy regularization](https://paperswithcode.com/method/entropy-regularization) term; PresGANs sidestep this intractability using
unbiased stochastic estimates.

**Neural Tangent Transfer**, or **NTT**, is a method for finding trainable sparse networks in a label-free manner. Specifically, NTT finds sparse networks whose training dynamics, as characterized by the neural tangent kernel, mimic those of dense networks in function space.

Neural Tangent Transfer

Finding trainable sparse networks through Neural Tangent Transfer

PresGAN

Prescribed Generative Adversarial Networks

Spectral clustering has attracted increasing attention due to
the promising ability in dealing with nonlinearly separable datasets [15], [16]. In spectral clustering, the spectrum of the graph Laplacian is used to reveal the cluster structure. The spectral clustering algorithm mainly consists of two steps: 1) constructs the low dimensional embedded representation of the data based on the eigenvectors of the graph Laplacian, 2) applies k-means on the constructed low dimensional data to obtain the clustering result. Thus,

Source	Prescribed Generative Adversarial Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com