An Efficient Fisher Matrix Approximation Method for Large-Scale Neural Network Optimization.

Although the shapes of the parameters are not crucial for designing first-order optimization methods in large scale empirical risk minimization problems, they have important impact on the size of the matrix to be inverted when developing second-order type methods. In this article, we propose an efficient and novel second-order method based on the parameters in the real matrix space [Formula: see text] and a matrix-product approximate Fisher matrix (MatFisher) by using the products of gradients. The size of the matrix to be inverted is much smaller than that of the Fisher information matrix in the real vector space [Formula: see text].

Clinical decision support model for tooth extraction therapy derived from electronic dental records.

Statement Of Problem: Tooth extraction therapy serves as a key initial step in many prosthodontic treatment plans. Dentists must make an appropriate decision on the tooth extraction therapy considering multiple determinants and whether a clinical decision support (CDS) model might help.

Purpose: The purpose of this retrospective records study was to construct a CDS model to predict tooth extraction therapy in clinical situations by using electronic dental records (EDRs).