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Survey on Large Language Model-Enhanced Reinforcement Learning: Concept, Taxonomy, and Methods
With extensive pre-trained knowledge and high-level general capabilities, large language models (LLMs) emerge as a promising avenue to augment reinforcement learning (RL) in aspects such as multi-task learning, sample efficiency, and task planning.
Power System Fault Diagnosis with Quantum Computing and Efficient Gate Decomposition
Power system fault diagnosis is crucial for identifying the location and causes of faults and providing decision-making support for power dispatchers.
Free Lunch for Federated Remote Sensing Target Fine-Grained Classification: A Parameter-Efficient Framework
Remote Sensing Target Fine-grained Classification (TFGC) is of great significance in both military and civilian fields.
MASK-CNN-Transformer For Real-Time Multi-Label Weather Recognition
The proposed model called MASK-Convolutional Neural Network-Transformer (MASK-CT) is based on the Transformer, the convolutional process, and the MASK mechanism.
TempEE: Temporal-Spatial Parallel Transformer for Radar Echo Extrapolation Beyond Auto-Regression
TempEE avoids using auto-regression and instead employs a one-step forward strategy to prevent cumulative error spreading during the extrapolation process.
Joint Featurewise Weighting and Lobal Structure Learning for Multi-view Subspace Clustering
Multi-view clustering integrates multiple feature sets, which reveal distinct aspects of the data and provide complementary information to each other, to improve the clustering performance.
