No.	Date	Contents	Reference	Video
1	2021/10/03	Seminar on Artificial Intelligence for Engineering Applications - Introduction to ML	Links	Link
2	2021/10/20	​Seminar on Artificial Intelligence for Engineering Applications - Do Vision Transformers See Like Convolutional Neural Networks?​	Video	Link
3	2021/11/02	​Seminar on Artificial Intelligence for Engineering Applications - Boltzmann Generators -- Sampling Equilibrium States of Many-Body Systems with Deep Learning	Paper ​Videos​	​Link
4	2021/11/26	​Seminar on Artificial Intelligence for Engineering Applications - Transformer	​Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems,	Link
5	2021/12/01	​Seminar on Artificial Intelligence for Engineering Applications - U-Net	​Ronneberger, O., Fischer, P., & Brox, T. (2015). U-net: Convolutional networks for biomedical image segmentation. International Conference on Medical image computing and computer-assisted intervention,	​Link
6	2022/12/15	​Seminar on Artificial Intelligence for Engineering Applications - GraphSage	Hamilton, W. L., Ying, R., & Leskovec, J. (2017, December). Inductive representation learning on large graphs. In Proceedings of the 31st International Conference on Neural Information Processing Systems (pp. 1025-1035). ​A Comprehensive Case-Study of GraphSage using PyTorchGeometric and Open-Graph-Benchmark,  Material Video	​Link
7	2022/01/21	ValueSeminar on Artificial Intelligence for Engineering Applications - Masked Autoencoders Are Scalable Vision Learners	Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., & Gelly, S. (2020). An image is worth 16x16 words: Transformers for image recognition at scale. arXiv preprint arXiv:2010.11929. He, K., Chen, X., Xie, S., Li, Y., Dollár, P., & Girshick, R. (2021). Masked autoencoders are scalable vision learners. arXiv preprint arXiv:2111.06377. JinWon Lee (2021). PR-355: Masked Autoencoders Are Scalable Vision Learners. Retrieved from youtube  DeepReader (2021). MAE: Masked Autoencoders Are Scalable Vision Learners. Retrieved from youtube Jia-Yau Shiau (2021). Masked Autoencoders: 借鏡BERT與ViT的Self-Supervised Learners. Retrieved from youtube	Link