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Showing 451–475 of 581 papers

TitleStatusHype
Dataflow Accelerator Architecture for Autonomous Machine Computing—0
The state-of-the-art in web-scale semantic information processing for cloud computing—0
Topology-based Clusterwise Regression for User Segmentation and Demand Forecasting—0
Towards Accurate Prediction for High-Dimensional and Highly-Variable Cloud Workloads with Deep Learning—0
Towards AIOps in Edge Computing Environments—0
Towards Confidential Computing: A Secure Cloud Architecture for Big Data Analytics and AI—0
Towards Deep Federated Defenses Against Malware in Cloud Ecosystems—0
Towards Energy and Time Efficient Resource Allocation in IoT-Fog-Cloud Environment—0
Towards General Industrial Intelligence: A Survey of Continual Large Models in Industrial IoT—0
Towards Learned Predictability of Storage Systems—0
Towards using Reinforcement Learning for Scaling and Data Replication in Cloud Systems—0
Transferable Knowledge for Low-cost Decision Making in Cloud Environments—0
Transformer-Based Model for Cold Start Mitigation in FaaS Architecture—0
Transformers and Large Language Models for Efficient Intrusion Detection Systems: A Comprehensive Survey—0
Transformers in Reinforcement Learning: A Survey—0
Ultra High-Dimensional Nonlinear Feature Selection for Big Biological Data—0
Understanding artificial intelligence ethics and safety—0
Understanding Silent Data Corruption in LLM Training—0
Unsupervised deep learning framework for temperature-compensated damage assessment using ultrasonic guided waves on edge device—0
URegM: a unified prediction model of resource consumption for refactoring software smells in open source cloud—0
Using AI for Mitigating the Impact of Network Delay in Cloud-based Intelligent Traffic Signal Control—0
Using Distance Estimation and Deep Learning to Simplify Calibration in Food Calorie Measurement—0
Using Geographic Load Shifting to Reduce Carbon Emissions—0
Value-Based Reinforcement Learning for Digital Twins in Cloud Computing—0
Adaptive Verifiable Coded Computing: Towards Fast, Secure and Private Distributed Machine Learning—0
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