Variable-depth width–radius approximation law
Establish whether the Dyadic–Triangular Activation satisfies the conjectured variable-depth approximation law $\mathcal E_\DTA(N,L,T)\asymp[N^2L^2\log(eNT)]^{-\beta/d}$ uniformly above fixed width and depth thresholds for $T\ge1$, and consequently whether the regression tradeoff extends to $N_M^2L_M^2\log(eN_MT_M)\asymp M^{d/(2\beta+d)}$.
References
Allowing depth to vary suggests the conjecture $\mathcal E_\DTA(N,L,T)\asymp[N2L2\log(eNT)]{-\beta/d}$, uniformly above fixed width and depth thresholds and for $T\ge1$.
— Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression
(2609.25710 - Li et al., 22 Sep 2026) in Section 6, “Conclusion”
Another question is whether the same optimal laws hold for a broad class of activations, particularly a single elementary, explicitly specified, bounded Lipschitz activation that is real analytic on $$.
— Optimal Tradeoffs Between Network Size and Parameter Magnitude in Neural Approximation and Minimax Regression
(2609.25710 - Li et al., 22 Sep 2026) in Section 6, “Conclusion”