In the beginning machines learned in darkness, and data scientists struggled in the void to explain them. Let there be light. InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train inter...
Kording and Jonas tried to analyse the connections of the chip, the effects of destroying individual transistor, local activities and other recordings. They found that many measures are very similar between the brain and the microprocessor — and yet the unlimited data that could be collected throu...
Pathways to spatial inequality of property flood risk among US counties In the next step, we first implemented PCA, a statistical technique used for dimensionality reduction27, to the eight features to identify the most important components of urban form and structure that contribute to the spatial...
This happens because the frequencies close to f are more linear than the high frequencies, resulting in an overall reduction of turbulent energy production. A difference in the energy pathways is also apparent in the comparison of Fig. 2A, B. We use these two cases as examples to show how ...
This type of CNN pooling is beneficial when we want to retain the background information along with the main features. While it’s not as aggressive as Max Pooling in terms of dimensionality reduction, it provides a balanced approach by preserving more information from the input feature map. ...
To put this in some perspective (and coming back to the original topic of probability distributions for images), let’s take an example of the handwritten digits represented by featuresx1andx2. That is, we have used some sort of dimensionality reduction technique to bring down 3k dimensions to...
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The extracted activations underwent a step of dimensionality reduction, using principal components analysis (PCA), fitted on the training set, to project the activations into a 512-dimensional space. For recurrent networks, PCA was fitted for all time steps simulta- neously. Thi...
PCA is an established dimensionality reduction method allowing the data to be described using a small number of uncorrelated variables (the principal components, PCs) while retaining as much information (variance in the original data) as possible. Furthermore, given the ordering of PCs according to...
“Explaining the topology of real networks” section illustrates its application to explain the topological structure of networks from different nature. “A panoramic view offered by local properties” section uses dimensionality reduction methods to evaluate the performance of the proposed metric when ...