Please enter a description about the method here

**Random Search** replaces the exhaustive enumeration of all combinations by selecting them randomly. This can be simply applied to the discrete setting described above, but also generalizes to continuous and mixed spaces. It can outperform Grid search, especially when only a small number of hyperparameters affects the final performance of the machine learning algorithm. In this case, the optimization problem is said to have a low intrinsic dimensionality. Random Search is also embarrassingly parallel, and additionally allows the inclusion of prior knowledge by specifying the distribution from which to sample.


Extracted from [Wikipedia](https://en.wikipedia.org/wiki/Hyperparameter_optimization#Random_search)

Source [Paper](https://dl.acm.org/doi/10.5555/2188385.2188395)

Image Source: [BERGSTRA AND BENGIO](https://dl.acm.org/doi/pdf/10.5555/2188385.2188395)

Random Search

Pose Contrastive Learning

Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment

**Euclidean Norm Regularization** is a regularization step used in [generative adversarial networks](https://paperswithcode.com/methods/category/generative-adversarial-networks), and is typically added to both the generator and discriminator losses:

$$ R\_{z} = w\_{r} \cdot ||\Delta{z}||^{2}\_{2} $$

where the scalar weight $w\_{r}$ is a parameter.

Image: [LOGAN](https://paperswithcode.com/method/logan)

Source	Domain Knowledge-Informed Self-Supervised Representations for Workout Form Assessment
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com