2026_DesignAI/appendix/附录6-参考文献.md

附录6：参考文献

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

4. Universal Approximation Theorem (1989)

   Scaling Laws for Neural Language Models (2020)

计算机视觉

6. 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

10. 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

14. Denoising Diffusion Probabilistic Models (2020)

    High-Resolution Image Synthesis with Latent Diffusion Models (2022)

    ControlNet (2023)

AI Agent

1. LLM Powered Autonomous Agents (2023)

2. ReAct: Synergizing Reasoning and Acting in Language Models (2022)

书籍

5. 深度学习

6. Deep Learning (Ian Goodfellow et al.)

7. Neural Networks and Deep Learning (Michael Nielsen)

8. 强化学习

9. Reinforcement Learning: An Introduction (Sutton & Barto)

在线资源

12. 课程

13. CS231n: Convolutional Neural Networks for Visual Recognition

14. Fast.ai Practical Deep Learning for Coders

工具文档

16. PyTorch: https://pytorch.org/docs/

17. Ultralytics YOLO: https://docs.ultralytics.com/

18. LangChain: https://python.langchain.com/

博客

20. Lil'Log (Lilian Weng): https://lilianweng.github.io/

21. The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/

设计AI相关

学术期刊

25. Landscape and Urban Planning

26. Environment and Planning B: Urban Analytics and City Science

27. Automation in Construction

会议

29. CAAD Futures

30. ACADIA

31. eCAADe

数据集

计算机视觉

35. ImageNet

36. COCO (Common Objects in Context)

37. MNIST

空间数据

39. OpenStreetMap

40. 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