Scalable Multi-Modal Representation Learning Networks

Information Science, Computer Science, Artificial Intelligence,
multi-modal learning   representation learning   inductive models   sparse regularization   hypergraph embedding  

Academic Background In the field of artificial intelligence, Multi-modal Representation Learning (MMRL) is a powerful paradigm aimed at mapping inputs from different modalities into a shared representation space. For example, in social networks, users often share both images and text simultaneously. Through multi-modal representation learning, mode...

Dual Representation Learning for One-Step Clustering of Multi-View Data

Information Science, Artificial Intelligence,
multi-view data   dual representation learning   consistent knowledge   unique knowledge   one-step clustering  

In real-world applications, multi-view data is widely available. Multi-view data refers to data collected from multiple sources or through multiple representations, such as different language versions of the same news story or disease data obtained through different medical tests. Multi-view learning is an effective method for mining multi-view dat...

A Comprehensive Survey of Loss Functions and Metrics in Deep Learning

Information Science, Computer Science, Artificial Intelligence,
deep learning   loss functions   performance metrics   computer vision   natural language processing   retrieval augmented generation  

Deep Learning, as a crucial branch of artificial intelligence, has achieved significant progress in recent years across various fields such as computer vision and natural language processing. However, the success of deep learning largely depends on the choice of loss functions and performance metrics. Loss functions are used to measure the differen...

AI-Driven Job Scheduling in Cloud Computing: A Comprehensive Review

Information Science, Computer Science, Artificial Intelligence,
AI-driven   cloud computing   job scheduling   energy efficiency   hybrid AI models   machine learning   resource allocation  

Academic Background With the rapid development of cloud computing technology, the demand for efficient job scheduling in dynamic and heterogeneous cloud environments has grown significantly. Traditional scheduling algorithms perform well in simple systems but are no longer sufficient for modern, complex cloud infrastructures. Issues such as resourc...

Pythagorean Linguistic Information-Based Green Supplier Selection Using Quantum-Based Group Decision-Making Methodology and the MULTIMOORA Approach

Engineering, Environmental Engineering, Information Science, Computer Science, Artificial Intelligence,
Pythagorean Linguistic Set   MULTIMOORA   Quantum Probability Theory   Multi-Criteria Group Decision-Making   Green Supplier Selection  

With the increasing severity of global environmental issues, companies are placing greater emphasis on green and sustainable development in supply chain management. Green Supply Chain Management (GSCM) has become a crucial means for enterprises to enhance competitiveness and achieve sustainable development. However, Green Supplier Selection (GSS) i...

A Comprehensive Review of Machine Learning Applications for Internet of Nano Things: Challenges and Future Directions

Chemistry, Nanoscience, Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence,
Machine Learning   Internet of Nano Things   Data Security   Anomaly Detection   Nanoscale Communication  

Academic Background In recent years, the rapid development of nanotechnology and the Internet of Things (IoT) has given rise to a revolutionary field—the Internet of Nano Things (IoNT). The IoNT connects nanoscale devices to the internet, enabling them to play significant roles in areas such as agriculture, military, multimedia, and healthcare. How...