517 citations · 842 across the 38 of their papers we have counts for
6 papers · 1 filter
A Bayesian model for sparse graphs with flexible degree distribution and overlapping community structure
Juho Lee, Lancelot F. James, Seungjin Choi +1
We consider a non-projective class of inhomogeneous random graph models with interpretable parameters and a number of interesting asymptotic properties. Using the results of Bollob…
Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks
Juho Lee, Yoonho Lee, Jungtaek Kim +3
Many machine learning tasks such as multiple instance learning, 3D shape recognition, and few-shot image classification are defined on sets of instances. Since solutions to such pr…
Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare
Ingyo Chung, Saehoon Kim, Juho Lee +3
We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and preventio…
Uncertainty-Aware Attention for Reliable Interpretation and Prediction
Jay Heo, Hae Beom Lee, Saehoon Kim +4
Attention mechanism is effective in both focusing the deep learning models on relevant features and interpreting them. However, attentions may be unreliable since the networks that…
Adaptive Network Sparsification with Dependent Variational Beta-Bernoulli Dropout
Juho Lee, Saehoon Kim, Jaehong Yoon +3
While variational dropout approaches have been shown to be effective for network sparsification, they are still suboptimal in the sense that they set the dropout rate for each neur…
Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning
Yanbin Liu, Juho Lee, Minseop Park +4
The goal of few-shot learning is to learn a classifier that generalizes well even when trained with a limited number of training instances per class. The recently introduced meta-l…