331 citations · 462 across the 14 of their papers we have counts for
5 papers · 1 filter
Relevance-Aware Rule: Structural Deletion of Irrelevant Conditions in Decision Trees
Jung-Sik Hong, Jeongeon Lee, Min Kyu Sim +1
Decision trees generate interpretable if--then rules, yet they contain irrelevant conditions (IRCs). These IRCs arise from the structural mechanism of tree splitting and persist ev…
Elucidating Robust Learning with Uncertainty-Aware Corruption Pattern Estimation
Jeongeun Park, Seungyoun Shin, Sangheum Hwang +1
Robust learning methods aim to learn a clean target distribution from noisy and corrupted training data where a specific corruption pattern is often assumed a priori. Our proposed…
Confidence-Aware Learning for Deep Neural Networks
Jooyoung Moon, Jihyo Kim, Younghak Shin +1
Despite the power of deep neural networks for a wide range of tasks, an overconfident prediction issue has limited their practical use in many safety-critical applications. Many re…
Self-Knowledge Distillation with Progressive Refinement of Targets
Kyungyul Kim, ByeongMoon Ji, Doyoung Yoon +1
The generalization capability of deep neural networks has been substantially improved by applying a wide spectrum of regularization methods, e.g., restricting function space, injec…
Semantic Noise Modeling for Better Representation Learning
Hyo-Eun Kim, Sangheum Hwang, Kyunghyun Cho
Latent representation learned from multi-layered neural networks via hierarchical feature abstraction enables recent success of deep learning. Under the deep learning framework, ge…