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The smooth output assumption, and why deep networks are better than wide ones
We call it the output sharpness, and it is based on the fact that, in reality, boundaries between concepts are generally unsharp.
Understanding the double descent curve in Machine Learning
The theory of bias-variance used to serve as a guide for model selection when applying Machine Learning algorithms.
Multi-level Data Representation For Training Deep Helmholtz Machines
A vast majority of the current research in the field of Machine Learning is done using algorithms with strong arguments pointing to their biological implausibility such as Backpropagation, deviating the field's focus from understanding its original organic inspiration to a compulsive search for optimal performance.
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