16 citations · 74 across the 11 of their papers we have counts for
17 papers
Discrete Factorial Representations as an Abstraction for Goal Conditioned Reinforcement Learning
Riashat Islam, Hongyu Zang, Anirudh Goyal +6
Goal-conditioned reinforcement learning (RL) is a promising direction for training agents that are capable of solving multiple tasks and reach a diverse set of objectives. How to \…
MGNNI: Multiscale Graph Neural Networks with Implicit Layers
Juncheng Liu, Bryan Hooi, Kenji Kawaguchi +1
Recently, implicit graph neural networks (GNNs) have been proposed to capture long-range dependencies in underlying graphs. In this paper, we introduce and justify two weaknesses o…
EIGNN: Efficient Infinite-Depth Graph Neural Networks
Juncheng Liu, Kenji Kawaguchi, Bryan Hooi +2
Graph neural networks (GNNs) are widely used for modelling graph-structured data in numerous applications. However, with their inherently finite aggregation layers, existing GNN mo…
Training Free Graph Neural Networks for Graph Matching
Zhiyuan Liu, Yixin Cao, Fuli Feng +4
We present a framework of Training Free Graph Matching (TFGM) to boost the performance of Graph Neural Networks (GNNs) based graph matching, providing a fast promising solution wit…
Adaptive Discrete Communication Bottlenecks with Dynamic Vector Quantization
Dianbo Liu, Alex Lamb, Xu Ji +4
Vector Quantization (VQ) is a method for discretizing latent representations and has become a major part of the deep learning toolkit. It has been theoretically and empirically sho…
ExpertNet: A Symbiosis of Classification and Clustering
Shivin Srivastava, Kenji Kawaguchi, Vaibhav Rajan
A widely used paradigm to improve the generalization performance of high-capacity neural models is through the addition of auxiliary unsupervised tasks during supervised training.…