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most citedFrom Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

9 citations · 51 across the 27 of their papers we have counts for

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5 papers · 1 filter

cs.CL2025★ 1 cited

Feature Alignment and Representation Transfer in Knowledge Distillation for Large Language Models

Junjie Yang, Junhao Song, Xudong Han +9

Knowledge distillation (KD) is a technique for transferring knowledge from complex teacher models to simpler student models, significantly enhancing model efficiency and accuracy.…

cs.LG2025

Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement

Chiung-Yi Tseng, Junhao Song, Ziqian Bi +4

In the era of data-driven intelligence, the paradox of data abundance and annotation scarcity has emerged as a critical bottleneck in the advancement of machine learning. This pape…

cs.LG2025

Generative Adversarial Networks Bridging Art and Machine Intelligence

Junhao Song, Yichao Zhang, Ziqian Bi +25

Generative Adversarial Networks (GAN) have greatly influenced the development of computer vision and artificial intelligence in the past decade and also connected art and machine i…

q-bio.GN2025★ 2 cited

From In Silico to In Vitro: A Comprehensive Guide to Validating Bioinformatics Findings

Tianyang Wang, Silin Chen, Yunze Wang +18

Translating computational predictions into experimentally validated biological knowledge remains one of the central challenges in modern bioinformatics. This review critically exam…

cs.AI2025★ 9 cited

From Aleatoric to Epistemic: Exploring Uncertainty Quantification Techniques in Artificial Intelligence

Tianyang Wang, Yunze Wang, Jun Zhou +16

Uncertainty quantification (UQ) is a critical aspect of artificial intelligence (AI) systems, particularly in high-risk domains such as healthcare, autonomous systems, and financia…