This method combines multiple optimization techniques like [ADAM](https://paperswithcode.com/method/adam) and [SGD](https://paperswithcode.com/method/sgd) or PADAM. This method can be applied to any couple of optimizers.

Image credit: [Combining Optimization Methods Using an Adaptive Meta Optimizer](https://www.mdpi.com/1999-4893/14/6/186)

A variant of the popular k-means algorithm that integrates constraint satisfaction into its objective function.

Original paper : Active Semi-Supervision for Pairwise Constrained Clustering, Basu et al. 2004

PCKMeans

ATMO

Combining Optimization Methods Using an Adaptive Meta Optimizer

**Local Response Normalization** is a normalization layer that implements the idea of lateral inhibition. Lateral inhibition is a concept in neurobiology that refers to the phenomenon of an excited neuron inhibiting its neighbours: this leads to a peak in the form of a local maximum, creating contrast in that area and increasing sensory perception. In practice, we can either normalize within the same channel or normalize across channels when we apply LRN to convolutional neural networks.

$$ b_{c} = a_{c}\left(k + \frac{\alpha}{n}\sum_{c'=\max(0, c-n/2)}^{\min(N-1,c+n/2)}a_{c'}^2\right)^{-\beta} $$

Where the size is the number of neighbouring channels used for normalization, $\alpha$ is multiplicative factor, $\beta$ an exponent and $k$ an additive factor

Source	Combining Optimization Methods Using an Adaptive Meta Optimizer
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com