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Found 4 papers, 2 papers with code
Avoiding Spurious Local Minima in Deep Quadratic Networks
Despite their practical success, a theoretical understanding of the loss landscape of neural networks has proven challenging due to the high-dimensional, non-convex, and highly nonlinear structure of such models.
Efficient Estimation of Compressible State-Space Models with Application to Calcium Signal Deconvolution
In this paper, we consider linear state-space models with compressible innovations and convergent transition matrices in order to model spatiotemporally sparse transient events.
Sampling Requirements for Stable Autoregressive Estimation
Assuming that the parameters are compressible, we analyze the performance of the $\ell_1$-regularized least squares as well as a greedy estimator of the parameters and characterize the sampling trade-offs required for stable recovery in the non-asymptotic regime.
Robust Estimation of Self-Exciting Generalized Linear Models with Application to Neuronal Modeling
We consider the problem of estimating self-exciting generalized linear models from limited binary observations, where the history of the process serves as the covariate.
