Researchers at University of Southern California and University of Pennsylvania recently introduced a new nonlinear dynamical modeling framework based on recurrent neural networks (RNNs) that ...

Artificial neural networks are viable models for a wide variety of problems, including pattern classification, ... nonlinear filters and statistical regression models, ...

Dr. James McCaffrey from Microsoft Research presents a complete end-to-end demonstration of neural network quantile regression. The goal of a quantile regression problem is to predict a single numeric ...

The scikit MLPRegressor neural network module is the most powerful scikit technique for regression problems, but the technique requires lots of labeled training data (typically at least 100 items).

Get Instant Summarized Text (Gist) A biologically plausible nonlinear neural network model based on t-SNE demonstrates how fruit fly brains reduce the dimensionality of odor information.

Instead, neural networks function along an expanding curve, which can be analyzed and understood using hyperbolic geometry and information theory," says Salk Professor Tatyana Sharpee, holder of ...