arxiv.orgpaper

Learning CNN Filters via Generalized Stein's Method

This arXiv paper formulates CNNs as a generalized index model with matrix-valued input, treating convolution filters as index vectors, and proposes an SVD-based estimator built on a generalized first-order Stein's formula to learn those filters directly rather than via gradient descent. The authors show the estimator achieves an optimal convergence rate comparable to generalized linear models with known link functions, and report that it outperforms standard deep learning optimizers like Adam in simulations and real-data experiments; they also note the method generalizes to nonlinear dimension reduction and representation learning.