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Evolution and Efficiency in Neural Architecture Search: Bridging the Gap Between Expert Design and Automated Optimization
The paper provides a comprehensive overview of Neural Architecture Search (NAS), emphasizing its evolution from manual design to automated, computationally-driven approaches.
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit
Transformer requires a fixed number of layers and heads which makes them inflexible to the complexity of individual samples and expensive in training and inference.
Joint Detection Algorithm for Multiple Cognitive Users in Spectrum Sensing
This paper first introduces three common logical circuit decision criteria in hard decisions and analyzes their decision rigor.
FedEmb: A Vertical and Hybrid Federated Learning Algorithm using Network And Feature Embedding Aggregation
Federated learning (FL) is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server.
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