most citedA Comprehensive Guide to Explainable AI: From Classical Models to LLMs

8 citations · 27 across the 18 of their papers we have counts for

collaborators

18 papers

q-bio.BM2025

Advanced Deep Learning Methods for Protein Structure Prediction and Design

Yichao Zhang, Ningyuan Deng, Xinyuan Song +25

After AlphaFold won the Nobel Prize, protein prediction with deep learning once again became a hot topic. We comprehensively explore advanced deep learning methods applied to prote…

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.CR2024★ 2 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.LG2024★ 8 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.CL2024

Deep Learning and Machine Learning -- Natural Language Processing: From Theory to Application

Keyu Chen, Cheng Fei, Ziqian Bi +23

With a focus on natural language processing (NLP) and the role of large language models (LLMs), we explore the intersection of machine learning, deep learning, and artificial intel…

cs.CV2024★ 1 cited

Deep Learning, Machine Learning -- Digital Signal and Image Processing: From Theory to Application

Weiche Hsieh, Ziqian Bi, Junyu Liu +16

Digital Signal Processing (DSP) and Digital Image Processing (DIP) with Machine Learning (ML) and Deep Learning (DL) are popular research areas in Computer Vision and related field…

cs.LG2024★ 1 cited

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.CV2024★ 3 cited

Deep Learning and Machine Learning -- Object Detection and Semantic Segmentation: From Theory to Applications

Jintao Ren, Ziqian Bi, Qian Niu +16

An in-depth exploration of object detection and semantic segmentation is provided, combining theoretical foundations with practical applications. State-of-the-art advancements in m…

cs.CR2024★ 4 cited

Jailbreaking and Mitigation of Vulnerabilities in Large Language Models

Benji Peng, Hanxuan Chen, Keyu Chen +12

Large Language Models (LLMs) have transformed artificial intelligence by advancing natural language understanding and generation, enabling applications across fields beyond healthc…

cs.CR2024

Mastering AI: Big Data, Deep Learning, and the Evolution of Large Language Models -- Blockchain and Applications

Pohsun Feng, Ziqian Bi, Lawrence K. Q. Yan +14

A detailed exploration of blockchain technology and its applications across various fields is provided, beginning with an introduction to cryptography fundamentals, including symme…

cs.LG2024★ 2 cited

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…

cs.DC2024★ 4 cited

Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing

Ming Li, Ziqian Bi, Tianyang Wang +14

General Purpose Graphics Processing Unit (GPGPU) computing plays a transformative role in deep learning and machine learning by leveraging the computational advantages of parallel…

cs.SE2024★ 1 cited

Deep Learning and Machine Learning: Advancing Big Data Analytics and Management with Design Patterns

Keyu Chen, Ziqian Bi, Tianyang Wang +12

This book, Design Patterns in Machine Learning and Deep Learning: Advancing Big Data Analytics Management, presents a comprehensive study of essential design patterns tailored for…

cs.CL2024★ 1 cited

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Unveiling AI's Potential Through Tools, Techniques, and Applications

Pohsun Feng, Ziqian Bi, Yizhu Wen +14

Artificial intelligence (AI), machine learning, and deep learning have become transformative forces in big data analytics and management, enabling groundbreaking advancements acros…

cs.CL2024

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Object-Oriented Programming

Tianyang Wang, Ziqian Bi, Keyu Chen +12

Object-Oriented Programming (OOP) has become a crucial paradigm for managing the growing complexity of modern software systems, particularly in fields like machine learning, deep l…

cs.CL2024

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Handy Appetizer

Benji Peng, Xuanhe Pan, Yizhu Wen +13

This book explores the role of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in driving the progress of big data analytics and management. The book fo…

cs.LG2024

DiffImpute: Tabular Data Imputation With Denoising Diffusion Probabilistic Model

Yizhu Wen, Kai Yi, Jing Ke +1

Tabular data plays a crucial role in various domains but often suffers from missing values, thereby curtailing its potential utility. Traditional imputation techniques frequently y…