Visual Studio Magazine

Neural Network Quantile Regression Using C#

However, a relatively new form of quantile regression is neural network quantile regression -- a variation of neural network regression. By using a custom loss function that penalizes low predictions ...
Semiconductor Engineering

Machine Learning Approach for Fast Electromigration Aware Aging Prediction in Incremental Design of Large Scale On-Chip Power Grid Network

Researchers use a machine learning (ML) approach to obtain the EM-aware aging prediction of the power grid (PG) network. They use neural network–based regression as their core ML technique to ...
Ars Technica

Here’s what’s really going on inside an LLM’s neural network

With most computer programs—even complex ones—you can meticulously trace through the code and memory usage to figure out why that program generates any specific behavior or output. That's generally ...
JSTOR Daily

Oil Spill Detection of Kerch Strait in November 2007 from Dual-Polarized TerraSAR-X Image Using Artificial and Convolutional Neural Network Regression Models

The oil spill is the main marine disaster. It is known that the data mining based method performs better in detecting oil than the traditional SAR based method to distinguish from lookalikes. Recently ...
pv magazine International

Optimum tilt angle for PV systems ranging from 0° to 90°

A Slovak research team has developed a mathematical model for determining the optimum tilt angle of a solar system ranging from 0° to 90°. Their framework was tested on an experimental setup in ...
Visual Studio Magazine

Neural Network Regression from Scratch Using C#

Compared to other regression techniques, a well-tuned neural network regression system can produce the most accurate prediction model, says Dr. James McCaffrey of Microsoft Research in presenting this ...