Deep learning approaches, particularly convolutional neural networks (CNNs) and other architectures, were used in 49 papers. These models excel at image-based tasks such as land cover classification, ...

At a time when conflict and division dominate the headlines, a new study from UCLA finds remarkable similarities in how mice ...

In today's data-driven environment, Python has become the mainstream language in the fields of machine learning and data science due to its concise syntax, rich library support, and active community, ...

WorldQuant University (WQU) has launched the Deep Learning Fundamentals Lab, a free, 16-week online certificate program ...

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This manuscript describes an AI-automated microscopy-based approach to characterize both bacterial and host cell responses associated with Shigella infection of epithelial cells. The methodology is ...

STM-Graph is a Python framework for analyzing spatial-temporal urban data and doing predictions using Graph Neural Networks. It provides a complete end-to-end pipeline from raw event data to trained ...

Abstract: Precise modeling of dynamical systems can be crucial for engineering applications. Traditional analytical models often struggle when capturing real-world complexities due to challenges in ...

Abstract: Many problems in science and engineering can be mathematically modeled using partial differential equations (PDEs), which are essential for fields like computational fluid dynamics (CFD), ...

The operation of future 6th-generation (6G) mobile networks will increasingly rely on the ability of Deep Reinforcement Learning (DRL) to optimize network decisions in real-time. However, trained DRL ...