Learning to Optimize Workflow Scheduling for an Edge–Cloud Computing Environment
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Cloud Computing"
DOI: 10.1109/tcc.2024.3408006
Abstract: The widespread deployment of intelligent Internet of Things (IoT) devices brings tighter latency demands on complex workload patterns such as workflows. In such applications, tremendous dataflows are generated and processed in accordance with specific service… read more here.
Keywords: cloud computing; edge cloud; environment; learning optimize ... See more keywords
Contrastive Unsupervised Representation Learning With Optimize-Selected Training Samples
Sign Up to like & getrecommendations! Published in 2024 at "IEEE Transactions on Neural Networks and Learning Systems"
DOI: 10.1109/tnnls.2024.3424331
Abstract: Contrastive unsupervised representation learning (CURL) is a technique that seeks to learn feature sets from unlabeled data. It has found widespread and successful application in unsupervised feature learning, with the design of positive and negative… read more here.
Keywords: method; unsupervised representation; contrastive unsupervised; learning optimize ... See more keywords
Learning to Optimize on Riemannian Manifolds
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Pattern Analysis and Machine Intelligence"
DOI: 10.1109/tpami.2022.3215702
Abstract: Many learning tasks are modeled as optimization problems with nonlinear constraints, such as principal component analysis and fitting a Gaussian mixture model. A popular way to solve such problems is resorting to Riemannian optimization algorithms,… read more here.
Keywords: training scheme; analysis; riemannian manifolds; learning optimize ... See more keywords