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)

3.  

# 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)

10. 

# 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/

22. 

# 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

32. 

# 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