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Title:
The Surprising Simplicity of Overparameterized Deep Neural Networks
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In the physical sciences, it has often proved fruitful to study large, complex system by means of high-dimensional, mean-field, or other asymptotic approximations. For example, when the number of interacting particles in a thermodynamic system is large, exact microscopic descriptions are intractable, but macroscopic quantities such as temperature and pressure can nevertheless provide a useful characterization of its properties. Another modern example is the 1/N expansion in quantum field theory, in which the dimensionality N of an internal symmetry group is taken to be large, often leading to simplifications and unexpected connections such as AdS/CFT dualities.
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