9 citations · 39 across the 18 of their papers we have counts for
19 papers
Is Conformal Factuality for RAG-based LLMs Robust? Novel Metrics and Systematic Insights
Yi Chen, Daiwei Chen, Sukrut Madhav Chikodikar +2
Large language models (LLMs) frequently hallucinate, limiting their reliability in knowledge-intensive applications. Retrieval-augmented generation (RAG) and conformal factuality h…
Why GRPO Needs Normalization: A Local-Curvature Perspective on Adaptive Gradients
Cheng Ge, Caitlyn Heqi Yin, Hao Liang +1
Reinforcement learning (RL) has become a key driver of language model reasoning. Among RL algorithms, Group Relative Policy Optimization (GRPO) is the de facto standard, avoiding t…
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, 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 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…
Large Language Model Benchmarks in Medical Tasks
Lawrence K. Q. Yan, Qian Niu, Ming Li +16
With the increasing application of large language models (LLMs) in the medical domain, evaluating these models' performance using benchmark datasets has become crucial. This paper…
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…
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…
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…
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…