Every idea extracted from recent arXiv mathematics papers — verified and unverified. Click an idea to open its full card; badges show the empirical verdict.
Generate temporal attention or convolution weights with the Graham–Knuth–Patashnik recurrence instead of learning every lag weight independently. For nonnegative recurrence parameters, the resulting lag sequence is strongly log-concave, so its normalized kernel is naturally unimodal and suppresses high-frequency sign-free oscillations without requiring a separate smoothness penalty. The six parameters can be learned per head, channel group, or layer, giving O(1) learned parameters for an…
Treat repeated residual blocks as an infinite directed transition system, damp transitions according to their depth, and regularize a finite part of the resulting Fredholm log-determinant. Subtracting a dilogarithmic counterterm prevents the regularizer from being dominated by infinitely repeated short cycles, while retaining information about global recurrent amplification.
Add a spectral regularizer to a linear state-space or recurrent layer that controls the overlap between its controllable and observable state directions. The regularizer uses the paper's identity to monitor eigenvalues of (I+PQ)^{-1}, equivalently the squared canonical correlations between reachable and observable subspaces, and penalizes degenerate or overly concentrated spectra.
Add a mean-field stochastic binary recurrent layer with an explicit susceptibility controller. The layer estimates the response statistic \(\chi=\beta^2N^{-1}\sum_i\operatorname{sech}^4(u_i)\) and either penalizes or clips it below \(1-\delta\), preventing the high-gain regime in which replicas with identical weights develop strongly divergent states. The expected benefit is more stable long-horizon recurrence and lower variance across stochastic forward passes.
Use the hysteresis threshold as a regularizer for attractor diversity. Estimate how many initial states converge to each fixed point and select thresholds that maximize basin entropy or penalize domination by one attractor, reducing attractor collapse in discrete recurrent classifiers and memory modules.
Represent selected hidden features as z = sqrt(N) exp(i theta), with a persistent phase and an explicitly stochastic amplitude. Regularize the ratio between coherent power |E[z]|^2 and total power E[|z|^2] toward the condensate prediction pi/4, while optionally matching higher amplitude moments.