Search Results for author:
Found 5 papers, 2 papers with code
Heterogeneous Multi-Agent Reinforcement Learning for Zero-Shot Scalable Collaboration
Second, we introduce a heterogeneous layer for decision-making, whose parameters are specifically generated by the learned latent variables.
Scalable Communication for Multi-Agent Reinforcement Learning via Transformer-Based Email Mechanism
However, existing works either broadcast the messages leading to information redundancy, or learn targeted communication by modeling all the other agents as targets, which is not scalable when the number of agents varies.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Adaptive Unfolding Total Variation Network for Low-Light Image Enhancement
Real-world low-light images suffer from two main degradations, namely, inevitable noise and poor visibility.
Deep Metric Learning-Based Feature Embedding for Hyperspectral Image Classification
In the first task, when only a few labeled samples are available, we employ ideas from metric learning based on deep embedding features and make a similarity learning between pairs of samples.
Few-Shot Image Classification
Hyperspectral Image Classification
+3
Low-Rank-Sparse Subspace Representation for Robust Regression
Learning robust regression model from high-dimensional corrupted data is an essential and difficult problem in many practical applications.
