Four UNIST graduate students have earned top distinctions at the 32nd Samsung HumanTech Paper Awards, securing one Gold, one Silver, and two Bronze Prizes. The achievement highlights the university’s continued strength in next-generat... Read More
Abstract Model robustness indicates a model’s capability to generalize well on unforeseen distributional shifts, including data corruptions and adversarial attacks. Data augmentation is one of the most prevalent and effective ways to... Read More
Four faculty members from UNIST have been elected as new members of the National Academy of Engineering of Korea (NAEK), one of Korea’s most prestigious honors related to engineering and technology. On January 11, NAEK announced that... Read More
Abstract Deep learning models have been successful in many areas, but understanding their behavior remains a challenge. Most prior explainable AI (XAI) approaches have focused on interpreting how models make predictions. In contrast, we int... Read More
Abstract Automatically assessing handwritten mathematical solutions is an important problem in educational technology with practical applications, but remains a significant challenge due to the diverse formats, unstructured layouts, and sym... Read More
Abstract Large-scale datasets are usually required to train deep neural networks; however, they increase computational complexity, hindering practical applications. Recently, dataset distillation for images and texts has attracted considera... Read More
Abstract Recently, semantic communications have drawn great attention as the groundbreaking concept surpasses the limited capacity of Shannon’s theory. Specifically, semantic communications probably become crucial in realizing visual... Read More
Sharing cutting-edge global technological trends, presenting research achievements, and fostering industry-academia collaboration, UNIST Graduate School of Artificial Intelligence hosted the ‘2025 UNIST AI Technology Open Workshop... Read More
Abstract Flat minima, known to enhance generalization and robustness in supervised learning, remain largely unexplored in generative models. In this work, we systematically investigate the role of loss surface flatness in generative models,... Read More
UNIST has been selected to participate in the ‘AI Star Fellowship‘ project, a key initiative supported by the Ministry of Science and ICT (MSIT) and the Institute for Info & communication Tech Planning & evaluation (IITP... Read More
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