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Found 8 papers, 2 papers with code
Towards Next-Generation Urban Decision Support Systems through AI-Powered Generation of Scientific Ontology using Large Language Models -- A Case in Optimizing Intermodal Freight Transportation
These facilitate the development of urban decision support systems by enhancing the data and metadata modeling, the integration of complex datasets, the coupling of multi-domain simulation models, and the formulation of decision-making metrics and workflow.
Leveraging Generative AI for Smart City Digital Twins: A Survey on the Autonomous Generation of Data, Scenarios, 3D City Models, and Urban Designs
The digital transformation of modern cities by integrating advanced information, communication, and computing technologies has marked the epoch of data-driven smart city applications for efficient and sustainable urban management.
Learning Two-Time-Scale Representations For Large Scale Recommendations
We propose a surprisingly simple but effective two-time-scale (2TS) model for learning user representations for recommendation.
Shallow VAEs with RealNVP Prior Can Perform as Well as Deep Hierarchical VAEs
Using powerful posterior distributions is a popular technique in variational inference.
On the Necessity and Effectiveness of Learning the Prior of Variational Auto-Encoder
Using powerful posterior distributions is a popular approach to achieving better variational inference.
AutoLoss: Learning Discrete Schedules for Alternate Optimization
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.
AutoLoss: Learning Discrete Schedule for Alternate Optimization
Many machine learning problems involve iteratively and alternately optimizing different task objectives with respect to different sets of parameters.
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications
To ensure undisrupted business, large Internet companies need to closely monitor various KPIs (e. g., Page Views, number of online users, and number of orders) of its Web applications, to accurately detect anomalies and trigger timely troubleshooting/mitigation.
