The convergence-rate conjecture for deep-limit minimisers
The convergence-rate conjecture for deep-limit minimisers
Let be minimisers of the finite-depth energies , and suppose the assumptions of the convergence theorem for and are satisfied. Let be a limit point of , and let be a subsequence converging to . With denoting the distance on the parameter space, the conjecture asserts that there exists such that
The convergence-rate conjecture. Under these assumptions, the distance between the convergent subsequence of finite-depth minimisers and its limit is . This conjectured rate is motivated by Taylor expansions of the recovery sequence, which suggest an rate for the minima when the relevant regularity is available, together with a local bound controlling parameter distance by the energy error. The result is not proved in the source.
Sources & referencesView supporting material
Primary source
Matthew Thorpe and Yves van Gennip, “Deep Limits of Residual Neural Networks”, arXiv:1810.11741 (2022).
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