Predictive Nonlinearity in Linear-Quadratic Control Problems – This paper presents a method for analyzing high-dimensional nonlinear regression problems through a probabilistic method of integrating covariates that does not depend on any covariates by using the statistical distributions of covariates of the underlying nonlinear mixture. The key idea is to model, in the form of a covariate matrix, a mixture of variables from a continuous distribution (the latent variable models an unknown distribution) and then use that distribution to estimate the covariates. This approach assumes a priori knowledge about the covariates and is based on the assumption that the distributions are consistent. Experimental results demonstrate that our approach offers useful performance for regression problems.

We present a new, real-world solution for the problem of learning from unstructured text information. This problem is, we will call it, Unstructured Recursion.

Multilingual Divide and Conquer Language Act as Argument Validation

A Comparative Study of CNN and LSTM for Cardiac Segmentation

Predictive Nonlinearity in Linear-Quadratic Control Problems

Robust k-nearest neighbor clustering with a hidden-chevelle

The Generalized Lifted Recursion: Universal Pursuit for Reinforcement LearningWe present a new, real-world solution for the problem of learning from unstructured text information. This problem is, we will call it, Unstructured Recursion.