Deep learning models, such as convolutional neural networks (CNNs) and recurrent neural networks (RNNs) are designed to partly emulate the functioning and structure of biological neural networks. As a ...

Opinion: Lidiya Mishchenko and Pooya Shoghi explain how to bridge a gap preventing successful patent claims to protect new ...

Two RIKEN researchers have used a scheme for simplifying data to mimic how the brain of a fruit fly reduces the complexity of information about smells it perceives. This could also help enhance our ...

A new technical paper titled “Domain Adaptation for Image Classification of Defects in Semiconductor Manufacturing” was ...

This project explores the application of neural networks for classifying numeric data by leveraging segment-based features. The work includes data preprocessing, neural network design, model ...

Continuous regression testing is a time constrained process aimed at detecting potential regressions introduced in source code integration. Test prioritization is an approach to increase the ...

LeetCode isn’t a sprint; it’s a marathon. You won’t become a coding master overnight. It takes time, effort, and, most ...

Implementation of various neural graph classification model (not node classification) Training and test of various Graph Neural Networks (GNNs) models using graph classification datasets; Input graph: ...

Carnegie Mellon’s School of Computer Science is widely recognized as one of the first and best computer science programs in the world. Our programs train the next generation of innovators to solve ...