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Found 7 papers, 4 papers with code
Knowledge-Assisted Dual-Stage Evolutionary Optimization of Large-Scale Crude Oil Scheduling
On the basis of the proposed model, a dual-stage evolutionary algorithm driven by heuristic rules (denoted by DSEA/HR) is developed, where the dual-stage search mechanism consists of global search and local refinement.
EMT-NAS:Transferring Architectural Knowledge Between Tasks From Different Datasets
In deep learning, this is usually achieved by sharing a common neural network architecture and jointly training the weights.
Towards Fairness-Aware Multi-Objective Optimization
Recent years have seen the rapid development of fairness-aware machine learning in mitigating unfairness or discrimination in decision-making in a wide range of applications.
A Federated Data-Driven Evolutionary Algorithm for Expensive Multi/Many-objective Optimization
Data-driven optimization has found many successful applications in the real world and received increased attention in the field of evolutionary optimization.
A Federated Data-Driven Evolutionary Algorithm
Data-driven evolutionary optimization has witnessed great success in solving complex real-world optimization problems.
Ternary Compression for Communication-Efficient Federated Learning
Learning over massive data stored in different locations is essential in many real-world applications.
Perception and Navigation in Autonomous Systems in the Era of Learning: A Survey
Second, we review the visual-based environmental perception and understanding methods based on deep learning, including deep learning-based monocular depth estimation, monocular ego-motion prediction, image enhancement, object detection, semantic segmentation, and their combinations with traditional vSLAM frameworks.
