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# 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)
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