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Can neural networks always be trained? On the boundaries of deep learning

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Deep learning has emerged as a competitive new tool in image reconstruction. However, recent results demonstrate such methods are typically highly unstable – tiny, almost undetectable perturbations cause severe artefacts in the reconstruction, a major concern in practice. This is paradoxical given the existence of stable state-of-the-art methods for these problems. Thus, approximation theoretical results non-constructively imply the existence of stable and accurate neural networks. Hence the fundamental question: Can we explicitly construct/train stable and accurate neural networks for image reconstruction? I will discuss two results in this direction. The first is a negative result, saying such constructions are in general impossible, even given access to the solutions of common optimisation algorithms such as basis pursuit. The second is a positive result, saying that under sparsity assumptions, such neural networks can be constructed. These neural networks are stable and theoretically competitive with state-of-the-art results from other methods. Numerical examples of competitive performance are also provided.

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