# 제20장: 참고 문헌

#### 연구 논문 및 기사

1. **A Comprehensive Introduction to Different Types of Convolutional Neural Networks**\
   K. Simonyan, A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," *International Conference on Learning Representations*, 2015.
2. **The ResNet Paper**\
   K. He, X. Zhang, S. Ren, J. Sun, "Deep Residual Learning for Image Recognition," *Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition*, 2016, pp. 770-778.
3. **Optimizing Gradient Descent Algorithm**\
   D. Kingma, J. Ba, "Adam: A Method for Stochastic Optimization," *International Conference on Learning Representations*, 2015.
4. **Transformer: The Attention Mechanism That Revolutionized NLP**\
   A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, I. Polosukhin, "Attention is All You Need," *NeurIPS*, 2017.
5. **Advances in Generative Adversarial Networks (GANs)**\
   I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, Y. Bengio, "Generative Adversarial Nets," *Advances in Neural Information Processing Systems*, 2014.
6. **Significance of Autoencoders in Dimensionality Reduction**\
   G. Hinton, R. R. Salakhutdinov, "Reducing the Dimensionality of Data with Neural Networks," *Science*, vol. 313, no. 5786, pp. 504-507, 2006.
7. **Exploration of Reinforcement Learning Algorithms**\
   V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, D. Hassabis, "Human-level control through deep reinforcement learning," *Nature*, vol. 518, pp. 529-533, 2015.
8. **Understanding the Bias-Variance Tradeoff**\
   T. Hastie, R. Tibshirani, J. Friedman, "The Elements of Statistical Learning: Data Mining, Inference, and Prediction," *Springer Series in Statistics*, 2009.
9. **A Survey of Model Compression Techniques**\
   S. Han, H. Mao, W. J. Dally, "Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding," *International Conference on Learning Representations*, 2016.
10. **Exploring Transfer Learning in Neural Networks**\
    Y. Bengio, "Deep Learning of Representations: Looking Forward," *Statistical Language and Speech Processing*, Lecture Notes in Computer Science, vol. 7978, Springer, 2013, pp. 1-37.

#### 서적 및 교재

11. **Deep Learning**\
    I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning," *MIT Press*, 2016.
12. **Pattern Recognition and Machine Learning**\
    C. M. Bishop, "Pattern Recognition and Machine Learning," *Springer*, 2006.
13. **Bayesian Reasoning and Machine Learning**\
    D. Barber, "Bayesian Reasoning and Machine Learning," *Cambridge University Press*, 2012.
14. **Reinforcement Learning: An Introduction**\
    R. S. Sutton, A. G. Barto, "Reinforcement Learning: An Introduction," *MIT Press*, 2nd ed., 2018.
15. **Machine Learning: A Probabilistic Perspective**\
    K. P. Murphy, "Machine Learning: A Probabilistic Perspective," *MIT Press*, 2012.

#### 기술 보고서 및 백서

16. **GloVe: Global Vectors for Word Representation**\
    J. Pennington, R. Socher, C. D. Manning, "GloVe: Global Vectors for Word Representation," *Conference on Empirical Methods in Natural Language Processing (EMNLP)*, 2014.
17. **Deep Learning for Healthcare: Opportunities, Challenges and Implications**\
    E. Topol, "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again," *Basic Books*, 2019.
18. **The Impact of AI on Society**\
    E. Brynjolfsson, A. McAfee, "The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies," *W\.W. Norton & Company*, 2014.
19. **Explainable Artificial Intelligence (XAI): Concepts and Applications**\
    D. Gunning, "Explainable Artificial Intelligence (XAI)," *Defense Advanced Research Projects Agency (DARPA)*, 2017.
20. **AI Ethics and Bias: Comprehensive Overview**\
    R. Binns, "Fairness in Machine Learning: Lessons from Political Philosophy," *Proceedings of the 2018 Conference on Fairness, Accountability, and Transparency*, 2018.