10 citations · 19 across the 3 of their papers we have counts for
3 papers
cs.LG2020★ 1 cited
Revisiting Explicit Regularization in Neural Networks for Well-Calibrated Predictive Uncertainty
Taejong Joo, Uijung Chung
From the statistical learning perspective, complexity control via explicit regularization is a necessity for improving the generalization of over-parameterized models. However, the…
cs.LG2020★ 10 cited
Being Bayesian about Categorical Probability
Taejong Joo, Uijung Chung, Min-Gwan Seo
Neural networks utilize the softmax as a building block in classification tasks, which contains an overconfidence problem and lacks an uncertainty representation ability. As a Baye…
cs.LG2020★ 8 cited
Regularizing activations in neural networks via distribution matching with the Wasserstein metric
Taejong Joo, Donggu Kang, Byunghoon Kim
Regularization and normalization have become indispensable components in training deep neural networks, resulting in faster training and improved generalization performance. We pro…