Sangheum Hwang

A New Splitting Criterion for Better Interpretable Trees

2020 · IEEE Access

A new splitting criterion for classification trees that generates better decision rules in terms of interpretability is proposed in this paper. The criterion is designed to find homogeneous rules that describe a significant number of …

Confidence-Aware Learning for Deep Neural Networks

2020 · arXiv (Cornell University)

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 recent works have been proposed to mitigate this …

GTA: Guided Transfer of Spatial Attention from Object-Centric Representations

2024 · arXiv (Cornell University)

Utilizing well-trained representations in transfer learning often results in superior performance and faster convergence compared to training from scratch. However, even if such good representations are transferred, a model can easily overfit the limited training …