Overview:  Reinforcement learning in 2025 is more practical than ever, with Python libraries evolving to support real-world simulations, robotics, and deci ...

This is the official repository of the paper "TabM: Advancing Tabular Deep Learning With Parameter-Efficient Ensembling". It consists of two parts: One dot represents a performance score on one ...

Using a custom "camera-to-rice" platform combined with deep-learning methods for feature extraction, matching, segmentation, and denoising, the system ...

Researchers from Marshall University and the University of Missouri have developed G2PDeep, an innovative web-based platform ...

A research team at Duke University has developed a new AI framework that can uncover simple, understandable rules that govern ...

The Autonomy + Box integration is available today. Developers can deploy a working autonomous app into their Box environment in under 10 minutes using Autonomy's step-by-step guides or by leveraging a ...

Abstract: Deep learning, as an important branch of machine learning, has been widely applied in computer vision, natural language processing, speech recognition, and more. However, recent studies have ...

Taiwo Feyijimi stands at a rare crossroads where advanced artificial intelligence, engineering education, and human learning converge. As a doctoral candidate in Engineering Education Transformations ...

Abstract: The booming development of deep learning applications and services heavily relies on large deep learning models and massive data in the cloud. However, cloud-based deep learning encounters ...

Deep neural networks (DNNs) are a class of artificial neural networks (ANNs) that are deep in the sense that they have many layers of hidden units between the input and output layers. Deep neural ...