9 citations · 51 across the 27 of their papers we have counts for
29 papers
NTR: Neural Token Reconstruction for Scene Token Bottleneck in End-to-End Driving
Jiahui Li, Jiawei Sun, Zixiang Ren +7
Recent perception-free end-to-end (E2E) autonomous driving methods bypass explicit perception outputs by compressing dense image patch tokens into compact scene tokens for downstre…
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.…
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…
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…
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…
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…
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…
From Noise to Nuance: Advances in Deep Generative Image Models
Benji Peng, Chia Xin Liang, Ziqian Bi +6
Deep learning-based image generation has undergone a paradigm shift since 2021, marked by fundamental architectural breakthroughs and computational innovations. Through reviewing a…
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…
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…
A Comprehensive Survey and Guide to Multimodal Large Language Models in Vision-Language Tasks
Chia Xin Liang, Pu Tian, Caitlyn Heqi Yin +7
This survey and application guide to multimodal large language models(MLLMs) explores the rapidly developing field of MLLMs, examining their architectures, applications, and impact…
From Pixels to Prose: Advancing Multi-Modal Language Models for Remote Sensing
Xintian Sun, Benji Peng, Charles Zhang +10
Remote sensing has evolved from simple image acquisition to complex systems capable of integrating and processing visual and textual data. This review examines the development and…
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models
Charles Zhang, Benji Peng, Xintian Sun +14
Word embeddings and language models have transformed natural language processing (NLP) by facilitating the representation of linguistic elements in continuous vector spaces. This r…
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…
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…
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…
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…
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…
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…
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…
FMCW Radar Principles and Human Activity Recognition Systems: Foundations, Techniques, and Applications
Ziqian Bi, Jiawei Xu, Xinyuan Song +1
This book introduces the theoretical foundations of FMCW radar systems, including range and velocity estimation, signal processing techniques, and the generation of radar point clo…
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…
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…
From Text to Multimodality: Exploring the Evolution and Impact of Large Language Models in Medical Practice
Qian Niu, Keyu Chen, Ming Li +16
Large Language Models (LLMs) have rapidly evolved from text-based systems to multimodal platforms, significantly impacting various sectors including healthcare. This comprehensive…