Singular Value Decomposition (SVD) constitutes a bridge between the linear algebra concepts and multi-layer neural networks-it is their linear analogy. Besides of this insight, it can be used as a good initial guess for the network parameters, leading to substantially better optimization results.
There is some theoretical evidence that deep neural networks with multiple hidden layers have a potential for more efficient representation of multidimensional mappings than shallow networks with a single hidden layer. The question is whether it is possible to exploit this theoretical advantage for finding such representations with help of numerical training methods. Tests using prototypical problems with a known mean square minimum did not confirm this hypothesis. Minima found with the help of deep networks have always been worse than those found using shallow networks. This does not directly contradict the theoretical findings-it is possible that the superior representational capacity of deep networks is genuine while finding the mean square minimum of such deep networks is a substantially harder problem than with shallow ones.
Classifying aesthetic forms -a methodology at the heart of art historyThe transformation of aesthetic styles has been at the heart of art history since its inception as a scholarly discipline in the late eighteenth century. Analyzing the single artifact and the carefully curated corpus have been the techniques for crafting hermeneutic understanding for such processes of change. Recently new instruments based on statistical techniques empower us for a fresh take on bodies of sources once disregarded as second tier complementary sources such as for instance very large corpora.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.