200 citations · 281 across the 6 of their papers we have counts for
6 papers · 1 filter
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
Wonguk Cho, Jinha Park, Taesup Kim
Continual domain shift poses a significant challenge in real-world applications, particularly in situations where labeled data is not available for new domains. The challenge of ac…
Variational Temporal Abstraction
Taesup Kim, Sungjin Ahn, Yoshua Bengio
We introduce a variational approach to learning and inference of temporally hierarchical structure and representation for sequential data. We propose the Variational Temporal Abstr…
Fast AutoAugment
Sungbin Lim, Ildoo Kim, Taesup Kim +2
Data augmentation is an essential technique for improving generalization ability of deep learning models. Recently, AutoAugment has been proposed as an algorithm to automatically s…
Edge-labeling Graph Neural Network for Few-shot Learning
Jongmin Kim, Taesup Kim, Sungwoong Kim +1
In this paper, we propose a novel edge-labeling graph neural network (EGNN), which adapts a deep neural network on the edge-labeling graph, for few-shot learning. The previous grap…
Bayesian Model-Agnostic Meta-Learning
Taesup Kim, Jaesik Yoon, Ousmane Dia +3
Learning to infer Bayesian posterior from a few-shot dataset is an important step towards robust meta-learning due to the model uncertainty inherent in the problem. In this paper,…
Deep Directed Generative Models with Energy-Based Probability Estimation
Taesup Kim, Yoshua Bengio
Training energy-based probabilistic models is confronted with apparently intractable sums, whose Monte Carlo estimation requires sampling from the estimated probability distributio…