人工智能经典论文分类指南

本专题精选了人工智能领域具有里程碑意义的45篇论文，这些论文构成了现代AI研究的理论基础，是每一位AI研究者和学习者的必读文献。

1. 机器学习基础 (8篇)

  • A Few Useful Things to Know About Machine Learning
    Domingos, P. (2012)
  • Support-Vector Networks
    Cortes, C., & Vapnik, V. (1995)
  • Random Forests
    Breiman, L. (2001)
  • Gradient Boosting Machines
    Friedman, J. H. (2001)

2. 深度学习革命 (10篇)

  • ImageNet Classification with Deep Convolutional Neural Networks
    Krizhevsky, A., et al. (2012)
  • Deep Residual Learning for Image Recognition
    He, K., et al. (2015)
  • Attention Is All You Need
    Vaswani, A., et al. (2017)

3. 自然语言处理 (9篇)

  • BERT: Pre-training of Deep Bidirectional Transformers
    Devlin, J., et al. (2018)
  • GPT-3: Language Models are Few-Shot Learners
    Brown, T., et al. (2020)

4. 计算机视觉 (8篇)

5. 强化学习 (6篇)

6. 生成模型 (4篇)