Xử lý dữ liệu lớn cho bài toán phân loại ảnh đa miền với CLIP và Conformal Prediction trên nền tảng Hadoop MapReduce
Tóm tắt
Từ khóa
Tài liệu tham khảo
Abdalla, H. B., Kumar, Y., Zhao, Y.,
& Tosi, D. (2025). A comprehensive survey of MapReduce models for
processing big data. Big Data and Cognitive Computing, 9(4), 77.
https://doi.org/10.3390/bdcc9040077.
Angelopoulos, A. N., & Bates, S.
(2021a). A gentle introduction to conformal prediction and distribution-free
uncertainty quantification. arXiv preprint arXiv:2107.07511.
https://arxiv.org/abs/2107.07511.
Angelopoulos, A. N., Bates, S.,
Jordan, M., & Malik, J. (2021b). Uncertainty sets for image classifiers
using conformal prediction. In Proceedings of the 9th International Conference
on Learning Representations (ICLR 2021). OpenReview.net.
Angelopoulos, A. N., Bates, S.,
Candès, E. J., Jordan, M. I., & Lei, L. (2023). Learn then test:
Calibrating predictive algorithms to achieve risk control. arXiv preprint
arXiv:2110.01052. https://arxiv.org/abs/2110.01052.
Barber, R. F., Candès, E. J., Ramdas,
A., & Tibshirani, R. J. (2023). Conformal prediction beyond
exchangeability. The Annals of Statistics, 51(2), 816-845.
https://doi.org/10.1214/23-AOS2276.
Dean, J., & Ghemawat, S. (2008).
MapReduce: Simplified data processing on large clusters. Communications of the
ACM, 51(1), 107-113. https://doi.org/10.1145/1327452.1327492.
Fang, A., Ilharco, G., Wortsman, M.,
Wan, Y., Shankar, V., Achille, A., Taori, R., Popescu, A. M., Schmidt, M.,
& Schmidt, L. (2022). Data determines distributional robustness in
contrastive language image pre-training (CLIP). In Proceedings of the 39th
International Conference on Machine Learning (ICML 2022). PMLR.
Jia, C., Yang, Y.,
Xia, Y., Chen, Y.-T., Parekh, Z., Pham, H., Le, Q. V., Sung, Y.-H., Li, Z., &
Duerig, T. (2021). Scaling up visual and vision-language representation
learning with noisy text supervision. In Proceedings of the 38th International
Conference on Machine Learning (ICML 2021) (Vol. 139, pp. 4904-4916). PMLR.
Kumar, B., Palepu, A., Tuwani, R.,
& Beam, A. (2022). Towards reliable zero shot classification in
self-supervised models with conformal prediction. arXiv preprint
arXiv:2210.15805. https://arxiv.org/abs/2210.15805.
Papadopoulos, H. (2023). Inductive
conformal prediction: Theory and application to neural networks. In V.
Balasubramanian, V. N. Ho, & V. Vovk (Eds.), Conformal prediction for
reliable machine learning (pp. 23-56). Elsevier.
https://doi.org/10.1016/B978-0-12-398537-8.00007-X.
Radford, A., Kim, J.
W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A.,
Mishkin, P., Clark, J., Krueger, G., & Sutskever, I. (2021). Learning
transferable visual models from natural language supervision. In Proceedings of
the 38th International Conference on Machine Learning (ICML 2021) (Vol. 139,
pp. 8748-8763). PMLR.
Romano, Y., Sesia, M., & Candès,
E. (2020). Classification with valid and adaptive coverage. In Advances in
Neural Information Processing Systems (NeurIPS 2020) (Vol. 33, pp. 3581-3591).
Curran Associates.
Sadinle, M., Lei, J., & Wasserman,
L. (2019). Least ambiguous set-valued classifiers with bounded error levels.
Journal of the American Statistical Association, 114(525), 223-234.
https://doi.org/10.1080/01621459.2017.1395341.
Silva-Rodríguez, J., Ben Ayed, I., &
Dolz, J. (2025). Conformal prediction for zero-shot models. In Proceedings of
the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2025).
IEEE.
Vovk, V., Gammerman, A., & Shafer,
G. (2005). Algorithmic learning in a random world. Springer.
Vovk, V. (2013). Cross-conformal
predictors. Annals of Mathematics and Artificial
Intelligence, 74(1–2), 9–28. https://doi.org/10.1007/s10472-013-9368-4.