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附录7：参考文献
本部分整理教材中引用的论文、书籍和在线资源。
论文
基础理论
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Universal Approximation Theorem (1989)
Scaling Laws for Neural Language Models (2020)
计算机视觉
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AlexNet (2012) - ImageNet Classification with Deep Convolutional Neural Networks
ResNet (2015) - Deep Residual Learning for Image Recognition
YOLO (2016) - You Only Look Once: Unified, Real-Time Object Detection
U-Net (2015) - Convolutional Networks for Biomedical Image Segmentation
Transformer与大模型
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Attention Is All You Need (2017)
BERT: Pre-training of Deep Bidirectional Transformers (2018)
GPT-3: Language Models are Few-Shot Learners (2020)
Training Language Models to Follow Instructions with Human Feedback (2022)
生成式AI
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Denoising Diffusion Probabilistic Models (2020)
High-Resolution Image Synthesis with Latent Diffusion Models (2022)
ControlNet (2023)
AI Agent
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LLM Powered Autonomous Agents (2023)
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ReAct: Synergizing Reasoning and Acting in Language Models (2022)
书籍
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深度学习
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Deep Learning (Ian Goodfellow et al.)
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Neural Networks and Deep Learning (Michael Nielsen)
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强化学习
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Reinforcement Learning: An Introduction (Sutton & Barto)
在线资源
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课程
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CS231n: Convolutional Neural Networks for Visual Recognition
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Fast.ai Practical Deep Learning for Coders
工具文档
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PyTorch: https://pytorch.org/docs/
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Ultralytics YOLO: https://docs.ultralytics.com/
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LangChain: https://python.langchain.com/
博客
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Lil'Log (Lilian Weng): https://lilianweng.github.io/
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The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/
设计AI相关
学术期刊
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Landscape and Urban Planning
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Environment and Planning B: Urban Analytics and City Science
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Automation in Construction
会议
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CAAD Futures
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ACADIA
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eCAADe
数据集
计算机视觉
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ImageNet
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COCO (Common Objects in Context)
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MNIST
空间数据
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OpenStreetMap
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路网数据、POI数据等
许可说明
部分内容引用自公开资源，遵循相应许可协议使用。
最后更新：2026年4月