# 参考文献

本部分整理教材中引用的论文、书籍和在线资源。

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## 论文

### 基础理论

- Universal Approximation Theorem (1989)
- Scaling Laws for Neural Language Models (2020)

### 计算机视觉

- 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

- 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

- Denoising Diffusion Probabilistic Models (2020)
- High-Resolution Image Synthesis with Latent Diffusion Models (2022)
- ControlNet (2023)

### AI Agent

- LLM Powered Autonomous Agents (2023)
- ReAct: Synergizing Reasoning and Acting in Language Models (2022)

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## 书籍

### 深度学习

- Deep Learning (Ian Goodfellow et al.)
- Neural Networks and Deep Learning (Michael Nielsen)

### 强化学习

- Reinforcement Learning: An Introduction (Sutton & Barto)

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## 在线资源

### 课程

- CS231n: Convolutional Neural Networks for Visual Recognition
- Fast.ai Practical Deep Learning for Coders

### 工具文档

- PyTorch: https://pytorch.org/docs/
- Ultralytics YOLO: https://docs.ultralytics.com/
- LangChain: https://python.langchain.com/

### 博客

- Lil'Log (Lilian Weng): https://lilianweng.github.io/
- The Illustrated Transformer: https://jalammar.github.io/illustrated-transformer/

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## 设计AI相关

### 学术期刊

- Landscape and Urban Planning
- Environment and Planning B: Urban Analytics and City Science
- Automation in Construction

### 会议

- CAAD Futures
- ACADIA
- eCAADe

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## 数据集

### 计算机视觉

- ImageNet
- COCO (Common Objects in Context)
- MNIST

### 空间数据

- OpenStreetMap
- 路网数据、POI数据等

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## 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。

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**最后更新**：2026年4月