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20242026
most citedJailbreaking and Mitigation of Vulnerabilities in Large Language Models

4 citations · 6 across the 3 of their papers we have counts for

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cs.LG20251 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.LG20252 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.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.LG20251 cited

Deep Learning and Machine Learning, Advancing Big Data Analytics and Management: Tensorflow Pretrained Models

Keyu Chen, Ziqian Bi, Qian Niu +12

The application of TensorFlow pre-trained models in deep learning is explored, with an emphasis on practical guidance for tasks such as image classification and object detection. T…

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.LG2024

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…