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20242026
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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7 papers · 1 filter

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…

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…

cs.LG2024

Deep Learning and Machine Learning -- Python Data Structures and Mathematics Fundamental: From Theory to Practice

Silin Chen, Ziqian Bi, Junyu Liu +15

This book provides a comprehensive introduction to the foundational concepts of machine learning (ML) and deep learning (DL). It bridges the gap between theoretical mathematics and…

cs.LG2024

Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- AutoML from Basics to State-of-the-Art Techniques

Pohsun Feng, Ziqian Bi, Yizhu Wen +13

A comprehensive guide to Automated Machine Learning (AutoML) is presented, covering fundamental principles, practical implementations, and future trends. The paper is structured to…