"Correlational neural networks" - looking at learning from multiple perspectives of the same thing to increase representation learning.

@article{chandar2016neuralcompjour, author = {Chandar, Sarath and Khapra, Mitesh M and Larochelle, Hugo and Ravindran, Balaraman}, date-added = {2024-08-01 10:47:30 -0400}, date-modified = {2024-08-01 10:50:01 -0400}, journal = {Neural Computation}, keywords = {correlation-learning, machine-learning, inductive-bias, autoencoders}, number = {2}, pages = {257--285}, pdf = {https://www.researchgate.net/profile/Balaraman-Ravindran/publication/275588055_Correlational_Neural_Networks/links/55ed84d308ae21d099c75c00/Correlational-Neural-Networks.pdf}, publisher = {MIT Press}, title = {Correlational neural networks}, venue-short = {NeuralCompJour}, volume = {28}, year = {2016}}

I believe that correlation and causation does not pertain to this argument, nor can I think of a way this particular argument would benifit.

This is a correlative argument by saying the increase of money in the broken system will cause it to become even more corrupt. A little bit further down, it talks about body cams and how with access to do that officers are able to change the footage to their liking.

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Magnitude of pearson correlation coeficient.

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An example of partial correlation.

Several sets of (x, y) points, with the correlation coefficient of x and y for each set. Note that the correlation reflects the non-linearity and direction of a linear relationship (top row), but not the slope of that relationship (middle), nor many aspects of nonlinear relationships (bottom). N.B.: the figure in the center has a slope of 0 but in that case the correlation coefficient is undefined because the variance of Y is zero.