Variational AutoEncoder
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Kingma, D. P., & Welling, M.</br> (2013).</br> <a href="https://doi.org/10.48550/arXiv.1312.6114">Auto-encoding variational bayes.</a></br> arXiv preprint arXiv:1312.6114.
Gal, Y., & Ghahramani, Z.</br> (2016, June).</br> <a href="https://doi.org/10.48550/arXiv.1506.02142">Dropout as a bayesian approximation: Representing model uncertainty in deep learning.</a></br> ...
Blundell, C., Cornebise, J., Kavukcuoglu, K., & Wierstra, D.</br> (2015, June).</br> <a href="https://proceedings.mlr.press/v37/blundell15">Weight uncertainty in neural network.</a></br> In Interna...
Based on the lecture “Bayesian Modeling (2024-1)” by Prof. Yeo Jin Chung, Dept. of AI, Big Data & Management, College of Business Administration, Kookmin Univ.
Based on the lecture “Bayesian Modeling (2024-1)” by Prof. Yeo Jin Chung, Dept. of AI, Big Data & Management, College of Business Administration, Kookmin Univ.
Based on the lecture “Bayesian Modeling (2024-1)” by Prof. Yeo Jin Chung, Dept. of AI, Big Data & Management, College of Business Administration, Kookmin Univ.
Based on the lecture “Intro. to Machine Learning (2023-2)” by Prof. Je Hyuk Lee, Dept. of Data Science, The Grad. School, Kookmin Univ.
Based on the lecture “Bayesian Modeling (2024-1)” by Prof. Yeo Jin Chung, Dept. of AI, Big Data & Management, College of Business Administration, Kookmin Univ.
DiD 이중차분법(Difference-in-Differences; DiD): 평행 추세 가정 하에 정책 시행에 따른 인과적 효과를 반사실과의 추세 차이로써 추정하는 준실험설계법(Quasi-Experimental) 평행 추세 가정(Parallel Trends Assumption): 관측 불가능한 반사실(counterf...
Based on the lecture “Bayesian Modeling (2024-1)” by Prof. Yeo Jin Chung, Dept. of AI, Big Data & Management, College of Business Administration, Kookmin Univ.