Search Results for author:
Found 2 papers, 2 papers with code
Graph4Rec: A Universal Toolkit with Graph Neural Networks for Recommender Systems
In recent years, owing to the outstanding performance in graph representation learning, graph neural network (GNN) techniques have gained considerable interests in many real-world scenarios, such as recommender systems and social networks.
Learning Approximate Stochastic Transition Models
We examine the problem of learning mappings from state to state, suitable for use in a model-based reinforcement-learning setting, that simultaneously generalize to novel states and can capture stochastic transitions.
Model-based Reinforcement Learning
reinforcement-learning
+1
