54 citations · 104 across the 13 of their papers we have counts for
7 papers · 1 filter
Unsupervised Hyperbolic Representation Learning via Message Passing Auto-Encoders
Jiwoong Park, Junho Cho, Hyung Jin Chang +1
Most of the existing literature regarding hyperbolic embedding concentrate upon supervised learning, whereas the use of unsupervised hyperbolic embedding is less well explored. In…
Combining Task Predictors via Enhancing Joint Predictability
Kwang In Kim, Christian Richardt, Hyung Jin Chang
Predictor combination aims to improve a (target) predictor of a learning task based on the (reference) predictors of potentially relevant tasks, without having access to the intern…
Implications of Human Irrationality for Reinforcement Learning
Haiyang Chen, Hyung Jin Chang, Andrew Howes
Recent work in the behavioural sciences has begun to overturn the long-held belief that human decision making is irrational, suboptimal and subject to biases. This turn to the rati…
Class-Attentive Diffusion Network for Semi-Supervised Classification
Jongin Lim, Daeho Um, Hyung Jin Chang +2
Recently, graph neural networks for semi-supervised classification have been widely studied. However, existing methods only use the information of limited neighbors and do not deal…
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning
Jongwon Choi, Kwang Moo Yi, Jihoon Kim +5
Active Learning for discriminative models has largely been studied with the focus on individual samples, with less emphasis on how classes are distributed or which classes are hard…
Symmetric Graph Convolutional Autoencoder for Unsupervised Graph Representation Learning
Jiwoong Park, Minsik Lee, Hyung Jin Chang +2
We propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a graph. In contrast to the existing graph autoencoders with asym…