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Found 10 papers, 2 papers with code
Multi-Granularity Attention Model for Group Recommendation
Group recommendation provides personalized recommendations to a group of users based on their shared interests, preferences, and characteristics.
Exploring the Spatiotemporal Features of Online Food Recommendation Service
Online Food Recommendation Service (OFRS) has remarkable spatiotemporal characteristics and the advantage of being able to conveniently satisfy users' needs in a timely manner.
Relightable Neural Human Assets from Multi-view Gradient Illuminations
UltraStage will be publicly available to the community to stimulate significant future developments in various human modeling and rendering tasks.
BASM: A Bottom-up Adaptive Spatiotemporal Model for Online Food Ordering Service
Therefore, we address this challenge by proposing a Bottom-up Adaptive Spatiotemporal Model(BASM) to adaptively fit the spatiotemporal data distribution, which further improve the fitting capability of the model.
Spatiotemporal-Enhanced Network for Click-Through Rate Prediction in Location-based Services
In Location-Based Services(LBS), user behavior naturally has a strong dependence on the spatiotemporal information, i. e., in different geographical locations and at different times, user click behavior will change significantly.
Digital-twin-enhanced metal tube bending forming real-time prediction method based on Multi-source-input MTL
To address this issue, a digital-twin-enhanced (DT-enhanced) metal tube bending forming real-time prediction method based on multi-source-input multi-task learning (MTL) is proposed.
A generic physics-informed neural network-based framework for reliability assessment of multi-state systems
The gradient projection operation significantly mitigates the detrimental effects caused by the gradient interference when training PINN, thus accelerating the convergence speed of PINN to high-precision solutions to MSS reliability assessment.
An Uncertainty-Informed Framework for Trustworthy Fault Diagnosis in Safety-Critical Applications
There has been a growing interest in deep learning-based prognostic and health management (PHM) for building end-to-end maintenance decision support systems, especially due to the rapid development of autonomous systems.
Physics-Informed Deep Learning: A Promising Technique for System Reliability Assessment
Considerable research has been devoted to deep learning-based predictive models for system prognostics and health management in the reliability and safety community.
Neural Relighting and Expression Transfer On Video Portraits
We present a neural relighting and expression transfer technique to transfer the facial expressions from a source performer to a portrait video of a target performer while enabling dynamic relighting.
