23 citations · 30 across the 11 of their papers we have counts for
8 papers · 1 filter
PF-GNN: Differentiable particle filtering based approximation of universal graph representations
Mohammed Haroon Dupty, Yanfei Dong, Wee Sun Lee
Message passing Graph Neural Networks (GNNs) are known to be limited in expressive power by the 1-WL color-refinement test for graph isomorphism. Other more expressive models eithe…
Graph Representation Learning with Individualization and Refinement
Mohammed Haroon Dupty, Wee Sun Lee
Graph Neural Networks (GNNs) have emerged as prominent models for representation learning on graph structured data. GNNs follow an approach of message passing analogous to 1-dimens…
Ensemble and Auxiliary Tasks for Data-Efficient Deep Reinforcement Learning
Muhammad Rizki Maulana, Wee Sun Lee
Ensemble and auxiliary tasks are both well known to improve the performance of machine learning models when data is limited. However, the interaction between these two methods is n…
State-Aware Variational Thompson Sampling for Deep Q-Networks
Siddharth Aravindan, Wee Sun Lee
Thompson sampling is a well-known approach for balancing exploration and exploitation in reinforcement learning. It requires the posterior distribution of value-action functions to…
Neuralizing Efficient Higher-order Belief Propagation
Mohammed Haroon Dupty, Wee Sun Lee
Graph neural network models have been extensively used to learn node representations for graph structured data in an end-to-end setting. These models often rely on localized first…
Contrastive Variational Reinforcement Learning for Complex Observations
Xiao Ma, Siwei Chen, David Hsu +1
Deep reinforcement learning (DRL) has achieved significant success in various robot tasks: manipulation, navigation, etc. However, complex visual observations in natural environmen…