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

9 citations · 19 across the 5 of their papers we have counts for

collaborators

5 papers

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…

cs.AI20259 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…

cs.CR20242 cited

Deep Learning Model Security: Threats and Defenses

Tianyang Wang, Ziqian Bi, Yichao Zhang +24

Deep learning has transformed AI applications but faces critical security challenges, including adversarial attacks, data poisoning, model theft, and privacy leakage. This survey e…

cs.LG20248 cited

A Comprehensive Guide to Explainable AI: From Classical Models to LLMs

Weiche Hsieh, Ziqian Bi, Chuanqi Jiang +24

Explainable Artificial Intelligence (XAI) addresses the growing need for transparency and interpretability in AI systems, enabling trust and accountability in decision-making proce…

cs.LG2024

Deep Learning, Machine Learning, Advancing Big Data Analytics and Management

Weiche Hsieh, Ziqian Bi, Keyu Chen +23

Advancements in artificial intelligence, machine learning, and deep learning have catalyzed the transformation of big data analytics and management into pivotal domains for researc…