Search Results for author:
Found 8 papers, 0 papers with code
Hybrid FedGraph: An efficient hybrid federated learning algorithm using graph convolutional neural network
Federated learning is an emerging paradigm for decentralized training of machine learning models on distributed clients, without revealing the data to the central server.
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.
Optimizing the Passenger Flow for Airport Security Check
Due to the necessary security for the airport and flight, passengers are required to have strict security check before getting aboard.
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.
Sentiment analysis with adaptive multi-head attention in Transformer
We propose a novel framework based on the attention mechanism to identify the sentiment of a movie review document.
Model-based Reinforcement Learning for Service Mesh Fault Resiliency in a Web Application-level
Microservice-based architectures enable different aspects of web applications to be created and updated independently, even after deployment.
