A **Dutch Eligibility Trace** is a type of [eligibility trace](https://paperswithcode.com/method/eligibility-trace) where the trace increments grow less quickly than the accumulative eligibility trace (helping avoid large variance updates). For the memory vector $\textbf{e}\_{t} \in \mathbb{R}^{b} \geq \textbf{0}$:

$$\mathbf{e\_{0}} = \textbf{0}$$

$$\textbf{e}\_{t} = \gamma\lambda\textbf{e}\_{t-1} + \left(1-\alpha\gamma\lambda\textbf{e}\_{t-1}^{T}\phi\_{t}\right)\phi\_{t}$$

**Nesterov Accelerated Gradient** is a momentum-based [SGD](https://paperswithcode.com/method/sgd) optimizer that "looks ahead" to where the parameters will be to calculate the gradient **ex post** rather than **ex ante**:

$$ v\_{t} = \gamma{v}\_{t-1} + \eta\nabla\_{\theta}J\left(\theta-\gamma{v\_{t-1}}\right) $$
$$\theta\_{t} = \theta\_{t-1} + v\_{t}$$

Like SGD with momentum $\gamma$ is usually set to $0.9$.

The intuition is that the [standard momentum](https://paperswithcode.com/method/sgd-with-momentum) method first computes the gradient at the current location and then takes a big jump in the direction of the updated accumulated gradient. In contrast Nesterov momentum first makes a big jump in the direction of the previous accumulated gradient and then measures the gradient where it ends up and makes a correction. The idea being that it is better to correct a mistake after you have made it. 

Image Source: [Geoff Hinton lecture notes](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)

Nesterov Accelerated Gradient

Dutch Eligibility Trace

**PCA Whitening** is a processing step for image based data that makes input less redundant. Adjacent pixel or feature values can be highly correlated, and whitening through the use of [PCA](https://paperswithcode.com/method/pca) reduces this degree of correlation.

Image Source: [Wikipedia](https://en.wikipedia.org/wiki/Principal_component_analysis#/media/File:GaussianScatterPCA.svg)

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com