After the applied mathematician Peter Shor, then at Bell Labs in New Jersey, showed that a quantum algorithm could, in theory ...

Artificial intelligence is colliding with a hard physical limit: the energy and heat of conventional chips. As models scale ...

Abstract: Most of the content on various social media platforms has enormous textual data. Before being used in machine learning models, this textual data must be transformed into numerical formats ...

Deep learning methods such as multilayer perceptrons (MLPs) and convolutional neural networks (CNNs) have been applied to predict the complex traits in animal and plant breeding. However, it remains ...

Deep learning methods such as multilayer perceptrons and convolutional neural networks have been applied to predict the complex traits in animal and plant breeding. However, it remains challenging to ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

Introduction: Accurately predicting the on-target activity of sgRNAs remains a challenge in CRISPR-Cas9 applications, due to the limited generalization of existing models across datasets, small-sample ...

1 Hunan Provincial Key Laboratory of Finance and Economics Big Data Science and Technology, Hunan University of Finance and Economics, Changsha, China 2 College of Information Science and Engineering, ...

Freeze casting is a versatile manufacturing process for producing porous materials with tailored microstructures and properties. However, due to the complexity and variability involved, predicting ...

Abstract: Semantic segmentation using LiDAR is a fundamental aspect in perception for autonomous driving. Conventional training methodologies commonly employ one-hot encoding for labels, followed by a ...