Abstract: Accurate, quick forecasting of petroleum production data in short-term scenarios is a complex challenge that requires the development of reliable predictive models. Traditionally, engineers ...

Abstract: Revealing the latent low-dimensional geometric structure of high-dimensional data is a crucial task in unsupervised representation learning. Traditional manifold learning, as a typical ...

Introduction: The COVID-19 pandemic accelerated global online education, which faces “shallow learning” challenges. Deep learning is key to student competencies. Based on sociocultural theory, this ...

In this talk, Dr. Hongkai Zhao will present both mathematical and numerical analysis as well as experiments to study a few basic computational issues in using neural networks to approximate functions: ...

We are often entertained and spend a high proportion of time on digital social media platforms like TikTok, Instagram, Facebook, Twitter(X), and Snapchat, which inform and entertain us with short-form ...

A biopharma company specializing in difficult-to-produce biologics has developed a deep learning model to rapidly predict protein expression using their moss-based production process. The model can ...

Deep learning has witnessed the widespread adoption across various domains, including few-shot learning. The few-shot learning demands a fusion of deep learning and meta-learning techniques, where ...

ABSTRACT: Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly ...