Papers2023 » History » Version 6
Version 5 (Evgeniy Pavlovskiy, 2023-09-21 16:22) → Version 6/9 (Evgeniy Pavlovskiy, 2023-09-21 16:23)
h1. Papers2023
h2. For 1st year students (not required to be reproducible)
1 data2vec https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language (by Evegniy Pavlovskiy)
2 arcface loss https://arxiv.org/abs/1801.07698
3 Segment Anything in Medical Images. Ma J., Wang B. Segment anything in medical images //arXiv preprint arXiv:2304.12306. – 2023. https://arxiv.org/pdf/2304.12306
4 Kirillov A. et al. Segment anything //arXiv preprint arXiv:2304.02643. – 2023. https://arxiv.org/pdf/2304.02643
5 ChatGPT
5.1 Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios https://link.springer.com/article/10.1007/s10916-023-01925-4
5.2. ChatGPT influence (Mikhail)
6. http://dbgroup.cs.tsinghua.edu.cn/ligl/publications.html (by Li Heng)
7. Similar Search: https://dl.acm.org/doi/pdf/10.1145/3318464.3386131
7.1 embedding vectors. Representation Learning (ICRL - International Conference on Representation learning 2023, 2022, etc.) (Li Heng)
7.2 Similar vector search.
8. Zhang Y. et al. Bytetrack: Multi-object tracking by associating every detection box //European Conference on Computer Vision. – Cham : Springer Nature Switzerland, 2022. – С. 1-21. URL: https://arxiv.org/abs/2110.06864 (by Qianyi Huang)
9. Novikov A., Rakhuba M., Oseledets I. Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds //SIAM Journal on Scientific Computing. – 2022. – Т. 44. – №. 2. – С. A843-A869. https://arxiv.org/pdf/2103.14974 (by E.P.)
10. Liu X. et al. Image inpainting algorithm based on tensor decomposition and weighted nuclear norm //Multimedia Tools and Applications. – 2023. – Т. 82. – №. 3. – С. 3433-3458. URL: https://link.springer.com/article/10.1007/s11042-022-12635-3 (by E.P.)
h2. For 2nd year students (required to be reproducible)
1. (Anna) https://arxiv.org/pdf/2106.09685.pdf
2. (Dmitry)
2.1 DeepFM: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C]
2.2 Wide & Deep Learning for Recommender Systems
2.3 xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
2.4 Deep Learning Recommendation Model for Personalization and Recommendation Systems
h2. For 1st year students (not required to be reproducible)
1 data2vec https://ai.facebook.com/research/data2vec-a-general-framework-for-self-supervised-learning-in-speech-vision-and-language (by Evegniy Pavlovskiy)
2 arcface loss https://arxiv.org/abs/1801.07698
3 Segment Anything in Medical Images. Ma J., Wang B. Segment anything in medical images //arXiv preprint arXiv:2304.12306. – 2023. https://arxiv.org/pdf/2304.12306
4 Kirillov A. et al. Segment anything //arXiv preprint arXiv:2304.02643. – 2023. https://arxiv.org/pdf/2304.02643
5 ChatGPT
5.1 Evaluating the feasibility of ChatGPT in healthcare: an analysis of multiple clinical and research scenarios https://link.springer.com/article/10.1007/s10916-023-01925-4
5.2. ChatGPT influence (Mikhail)
6. http://dbgroup.cs.tsinghua.edu.cn/ligl/publications.html (by Li Heng)
7. Similar Search: https://dl.acm.org/doi/pdf/10.1145/3318464.3386131
7.1 embedding vectors. Representation Learning (ICRL - International Conference on Representation learning 2023, 2022, etc.) (Li Heng)
7.2 Similar vector search.
8. Zhang Y. et al. Bytetrack: Multi-object tracking by associating every detection box //European Conference on Computer Vision. – Cham : Springer Nature Switzerland, 2022. – С. 1-21. URL: https://arxiv.org/abs/2110.06864 (by Qianyi Huang)
9. Novikov A., Rakhuba M., Oseledets I. Automatic differentiation for Riemannian optimization on low-rank matrix and tensor-train manifolds //SIAM Journal on Scientific Computing. – 2022. – Т. 44. – №. 2. – С. A843-A869. https://arxiv.org/pdf/2103.14974 (by E.P.)
10. Liu X. et al. Image inpainting algorithm based on tensor decomposition and weighted nuclear norm //Multimedia Tools and Applications. – 2023. – Т. 82. – №. 3. – С. 3433-3458. URL: https://link.springer.com/article/10.1007/s11042-022-12635-3 (by E.P.)
h2. For 2nd year students (required to be reproducible)
1. (Anna) https://arxiv.org/pdf/2106.09685.pdf
2. (Dmitry)
2.1 DeepFM: DeepFM: A Factorization-Machine based Neural Network for CTR Prediction[C]
2.2 Wide & Deep Learning for Recommender Systems
2.3 xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systems
2.4 Deep Learning Recommendation Model for Personalization and Recommendation Systems