**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.

**TD3** builds on the [DDPG](https://paperswithcode.com/method/ddpg) algorithm for reinforcement learning, with a couple of modifications aimed at tackling overestimation bias with the value function. In particular, it utilises [clipped double Q-learning](https://paperswithcode.com/method/clipped-double-q-learning), delayed update of target and policy networks, and [target policy smoothing](https://paperswithcode.com/method/target-policy-smoothing) (which is similar to a [SARSA](https://paperswithcode.com/method/sarsa) based update; a safer update, as they provide higher value to actions resistant to perturbations).

Addressing Function Approximation Error in Actor-Critic Methods

Neural Tangent Transfer

Finding trainable sparse networks through Neural Tangent Transfer

**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.

Source	Finding trainable sparse networks through Neural Tangent Transfer
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com