In a study published in Robot Learning journal, researchers propose a new learning-based path planning framework that allows mobile robots to navigate safely and efficiently using a Transformer model.

Medical researchers at Mass General Brigham say the self-supervised foundational model can identify inherent features from ...

Supervised learning algorithms like Random Forests, XGBoost, and LSTMs dominate crypto trading by predicting price directions or values from labeled historical data, enabling precise signals such as ...

Abstract: Reducing energy consumption has become a pressing need for modern machine learning, which has achieved many of its most impressive results by scaling to larger and more energy-consumptive ...

State Key Laboratory of Water Pollution Control and Green Resource Recycling, Key Laboratory of Yangtze River Water Environment of Ministry of Education, Shanghai Institute of Pollution Control and ...

Abstract: With the rise of e-commerce, personalized recommendation algorithms have received much attention in recent years. Meanwhile, multimodal recommendation algorithms have become the next ...

An image depicting the integration of AI technologies in banking, showcasing how legacy banks can evolve with AI advancements for improved customer experiences and operational efficiency. Supervised ...

The emergence of using Machine Learning Techniques in software testing started in the 2000s with the rise of Model-Based Testing and early bug prediction models trained on historical defect data. It ...

The field of machine learning is traditionally divided into two main categories: "supervised" and "unsupervised" learning. In supervised learning, algorithms are trained on labeled data, where each ...