What AI builders can learn from fraud models that run in 300 milliseconds

Mastercard's Decision Intelligence Pro uses recurrent neural networks to analyze 160 billion yearly transactions in under 50 ...
Searchenginejournal.com

Google’s Recommender System Breakthrough Detects Semantic Intent

Google published a research paper about helping recommender systems understand what users mean when they interact with them. Their goal with this new approach is to overcome the limitations inherent ...
IEEE

Privacy-Preserving Cross-Client Recommender Systems With Fully-Separable Graph Neural Networks

Abstract: Graph Neural Networks (GNNs) effectively model long-range dependencies by capturing high-order relationships in user-item graphs, emerging as a mainstream paradigm for building personalized ...
C&EN

PSCG-Net: A Multiscale Crystal Graph Neural Network for Accelerated Materials Discovery

AI for Science Institute (CUAISci), Cornell University, Ithaca, New York 14853, United States Systems Engineering, College of Engineering, Cornell University, Ithaca, New York 14853, United States AI ...
Scientific Research Publishing

Katzman, J.L., Shaham, U., Cloninger, A., Bates, J., Jiang, T. and Kluger, Y. (2018) Deepsurv: Personalized Treatment Recommender System Using a Cox Proportional Hazards Deep ...

ABSTRACT: This study presents a comprehensive clinical decision support system aimed at personalizing antidepressant treatment selection using synthetic patient data, predictive modelling, and ...
IEEE

Reinforcement Learning-Based Recommender Systems Enhanced with Graph Neural Networks

Abstract: Graph Neural Networks (GNNs) have emerged as powerful tools in recommender systems, enabling the modeling of complex user-item interactions by leveraging graph-structured representations.
Frontiers

DGAT: a dynamic graph attention neural network framework for EEG emotion recognition

Introduction: Emotion recognition based on electroencephalogram (EEG) signals has shown increasing application potential in fields such as brain-computer interfaces and affective computing. However, ...
Hackaday

A Neural Net For A Graphing Calculator?

Machine learning and neural nets can be pretty handy, and people continue to push the envelope of what they can do both in high end server farms as well as slower systems. At the extreme end of the ...
Scientific Research Publishing

An In-Depth Analysis of Graph Neural Networks and Machine Learning Approaches for Drug Repositioning ()

1 Department of Computer Science and Engineering, Kishoreganj University, Kishoreganj, Bangladesh. 2 Department of Electrical and Computer Engineering, North South University, Dhaka, Bangladesh. 3 ...
Frontiers

User recommendation method integrating hierarchical graph attention network with multimodal knowledge graph

1 Business College, California State University, Long Beach, CA, United States 2 School of Business and Management, Shanghai International Studies University, Shanghai, China In common graph neural ...