A Flow Model of Neural Networks
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Based on a natural connection between ResNet and transport equation or its characteristic equation, we propose a continuous flow model for both ResNet and plain net. Through this continuous model, a ResNet can be explicitly constructed as a refinement of a plain net. The flow model provides an alternative perspective to understand phenomena in deep neural networks, such as why it is necessary and sufficient to use 2-layer blocks in ResNets, why deeper is better, and why ResNets are even deeper, and so on. It also opens a gate to bring in more tools from the huge area of differential equations.
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Cited by 1 Pith paper
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Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations
Neural flow operators with composition and separation structures are proven to universally approximate any operator in finite and infinite dimensions, recovering ResNet-type and plain architectures via time discretizations.
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