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

1. Deep Learning
   I. Goodfellow, Y. Bengio, A. Courville, "Deep Learning," MIT Press, 2016.

2. Pattern Recognition and Machine Learning
   C. M. Bishop, "Pattern Recognition and Machine Learning," Springer, 2006.

3. Bayesian Reasoning and Machine Learning
   D. Barber, "Bayesian Reasoning and Machine Learning," Cambridge University Press, 2012.

4. Reinforcement Learning: An Introduction
   R. S. Sutton, A. G. Barto, "Reinforcement Learning: An Introduction," MIT Press, 2nd ed., 2018.

5. Machine Learning: A Probabilistic Perspective
   K. P. Murphy, "Machine Learning: A Probabilistic Perspective," MIT Press, 2012.

기술 보고서 및 백서

1. 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.

2. Deep Learning for Healthcare: Opportunities, Challenges and Implications
   E. Topol, "Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again," Basic Books, 2019.

3. 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.

4. Explainable Artificial Intelligence (XAI): Concepts and Applications
   D. Gunning, "Explainable Artificial Intelligence (XAI)," Defense Advanced Research Projects Agency (DARPA), 2017.

5. 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.