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Document Visual Question Answering (DocVQA) has quickly grown into a central task of document understanding.
Dwork, C., Kenthapadi, K., McSherry, F., Mironov, I., Naor, M.: Our data, ourselves: Privacy via distributed noise generation. In: Vaudenay, S. (ed.) Advances in Cryptology - EUROCRYPT 2006, 25th Annual International Conference on the Theory and Applications of Cryptographic Techniques, St. Petersburg, Russia, May 28 - June 1, 2006, Proceedings. Lecture Notes in Computer Science, vol. 4004, pp. 486–503. Springer (2006). https://doi.org/10.1007/11761679_29, https://doi.org/10.1007/11761679_29
2006
Earlier work this paper cites.
Dwork, C., McSherry, F., Nissim, K., Smith, A.D.: Calibrating noise to sensitivity in private data analysis. In: Halevi, S., Rabin, T. (eds.) Theory of Cryptography, Third Theory of Cryptography Conference, TCC 2006, New York, NY, USA, March 4-7, 2006, Proceedings. Lecture Notes in Computer Science, vol. 3876, pp. 265–284. Springer (2006). https://doi.org/10.1007/11681878_14, https://doi.org/10.1007/11681878_14
2006
Earlier work this paper cites.
Ács, G., Castelluccia, C.: I have a dream! (differentially private smart metering). In: Information Hiding - 13th International Conference, IH 2011. pp. 118–132 (2011). https://doi.org/10.1007/978-3-642-24178-9_9, https://doi.org/10.1007/978-3-642-24178-9_9
2011
Earlier work this paper cites.
Rajkumar, A., Agarwal, S.: A differentially private stochastic gradient descent algorithm for multiparty classification. In: Lawrence, N.D., Girolami, M.A. (eds.) Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, AISTATS 2012, La Palma, Canary Islands, Spain, April 21-23, 2012. JMLR Proceedings, vol. 22, pp. 933–941. JMLR.org (2012), http://proceedings.mlr.press/v22/rajkumar12.html
2012
Earlier work this paper cites.
Song, S., Chaudhuri, K., Sarwate, A.D.: Stochastic gradient descent with differentially private updates. In: IEEE Global Conference on Signal and Information Processing, GlobalSIP 2013, Austin, TX, USA, December 3-5, 2013. pp. 245–248. IEEE (2013). https://doi.org/10.1109/GlobalSIP.2013.6736861, https://doi.org/10.1109/GlobalSIP.2013.6736861
2013
Earlier work this paper cites.
Dwork, C., Roth, A.: The algorithmic foundations of differential privacy. Foundations and Trends® in Theoretical Computer Science 9
2014
Earlier work this paper cites.
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Ghahramani, Z., Welling, M., Cortes, C., Lawrence, N.D., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada. pp. 3320–3328 (2014), https://proceedings.neurips.cc/paper/2014/hash/375c71349b295fbe2dcdca9206f20a06-Abstract.html
2014
Earlier work this paper cites.
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC conference on computer and communications security. pp. 1310–1321 (2015)
2015
Earlier work this paper cites.
Abadi, M., Chu, A., Goodfellow, I., McMahan, H.B., Mironov, I., Talwar, K., Zhang, L.: Deep learning with differential privacy. In: Proceedings of the 2016 ACM SIGSAC conference on computer and communications security. pp. 308–318 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
McMahan, B., Moore, E., Ramage, D., Hampson, S., y Arcas, B.A.: Communication-efficient learning of deep networks from decentralized data. In: Singh, A., Zhu, X.J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, AISTATS 2017, 20-22 April 2017, Fort Lauderdale, FL, USA. Proceedings of Machine Learning Research, vol. 54, pp. 1273–1282. PMLR (2017), http://proceedings.mlr.press/v54/mcmahan17a.html
2017
Earlier work this paper cites.
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE symposium on security and privacy (SP). pp. 3–18. IEEE (2017)
2017
Earlier work this paper cites.
Biten, A.F., Tito, R., Mafla, A., Gomez, L., Rusinol, M., Mathew, M., Jawahar, C., Valveny, E., Karatzas, D.: Icdar 2019 competition on scene text visual question answering. In: 2019 International Conference on Document Analysis and Recognition (ICDAR). pp. 1563–1570. IEEE (2019)
2019
Earlier work this paper cites.
Biten, A.F., Tito, R., Mafla, A., Gomez, L., Rusinol, M., Valveny, E., Jawahar, C., Karatzas, D.: Scene text visual question answering. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4291–4301 (2019)
2019
Earlier work this paper cites.
Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: Bert: Pre-training of deep bidirectional transformers for language understanding. In: Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). pp. 4171–4186 (2019)
2019
Earlier work this paper cites.
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S.: Parameter-efficient transfer learning for NLP. In: Chaudhuri, K., Salakhutdinov, R. (eds.) Proceedings of the 36th International Conference on Machine Learning, ICML 2019, 9-15 June 2019, Long Beach, California, USA. Proceedings of Machine Learning Research, vol. 97, pp. 2790–2799. PMLR (2019), http://proceedings.mlr.press/v97/houlsby19a.html
2019
Earlier work this paper cites.
Melis, L., Song, C., De Cristofaro, E., Shmatikov, V.: Exploiting unintended feature leakage in collaborative learning. In: 2019 IEEE symposium on security and privacy (SP). pp. 691–706. IEEE (2019)
2019
Earlier work this paper cites.
Nasr, M., Shokri, R., Houmansadr, A.: Comprehensive privacy analysis of deep learning: Passive and active white-box inference attacks against centralized and federated learning. In: 2019 IEEE symposium on security and privacy (SP). pp. 739–753. IEEE (2019)
2019
Earlier work this paper cites.
Zhu, L., Liu, Z., Han, S.: Deep leakage from gradients. Advances in Neural Information Processing Systems 32
2019
Earlier work this paper cites.
Geiping, J., Bauermeister, H., Dröge, H., Moeller, M.: Inverting gradients - how easy is it to break privacy in federated learning? In: Larochelle, H., Ranzato, M., Hadsell, R., Balcan, M., Lin, H. (eds.) Advances in Neural Information Processing Systems. vol. 33, pp. 16937–16947. Curran Associates, Inc. (2020)
2020
Earlier work this paper cites.
Koskela, A., Jälkö, J., Honkela, A.: Computing tight differential privacy guarantees using FFT. In: The 23rd International Conference on Artificial Intelligence and Statistics, (AISTATS 2020). Proceedings of Machine Learning Research, vol. 108, pp. 2560–2569. PMLR (2020), http://proceedings.mlr.press/v108/koskela20b.html
2020
Earlier work this paper cites.
Li, O., Sun, J., Yang, X., Gao, W., Zhang, H., Xie, J., Smith, V., Wang, C.: Label leakage and protection in two-party split learning. NeurIPS 2020 Workshop on Scalability, Privacy, and Security in Federated Learning (SpicyFL) (2020)
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Raffel, C., et al.: Exploring the limits of transfer learning with a unified text-to-text transformer. J. Mach. Learn. Res. 21
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
Gopi, S., Lee, Y.T., Wutschitz, L.: Numerical composition of differential privacy. In: Ranzato, M., Beygelzimer, A., Dauphin, Y.N., Liang, P., Vaughan, J.W. (eds.) Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, NeurIPS 2021, December 6-14, 2021, virtual. pp. 11631–11642 (2021), https://proceedings.neurips.cc/paper/2021/hash/6097d8f3714205740f30debe1166744e-Abstract.html
2021
Cited alongside, same era.
Kerkouche, R., Ács, G., Castelluccia, C., Genevès, P.: Constrained differentially private federated learning for low-bandwidth devices. In: de Campos, C., Maathuis, M.H. (eds.) Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence. Proceedings of Machine Learning Research, vol. 161, pp. 1756–1765. PMLR (27–30 Jul 2021)
2021
Cited alongside, same era.
Li, Z., Zhang, J., Liu, L., Liu, J.: Auditing privacy defenses in federated learning via generative gradient leakage. The IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) (2022)
2022
Later among the works it cites.
Marathe, V.J., Kanani, P.: Subject granular differential privacy in federated learning (2022)
2022
Later among the works it cites.
Mathew, M., Bagal, V., Tito, R., Karatzas, D., Valveny, E., Jawahar, C.: Infographicvqa. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 1697–1706 (2022)
2022
Later among the works it cites.
Qi, L., Lv, S., Li, H., Liu, J., Zhang, Y., She, Q., Wu, H., Wang, H., Liu, T.: Dureadervis: A: A chinese dataset for open-domain document visual question answering. In: Findings of the Association for Computational Linguistics: ACL 2022. pp. 1338–1351 (2022)
2022
Later among the works it cites.
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Kerkouche, R., Ács, G., Castelluccia, C., Genevès, P.: Compression boosts differentially private federated learning. In: 2021 IEEE European Symposium on Security and Privacy (EuroS&P). pp. 304–318 (2021). https://doi.org/10.1109/EuroSP51992.2021.00029
2021
Cited alongside, same era.
Koskela, A., Jälkö, J., Prediger, L., Honkela, A.: Tight differential privacy for discrete-valued mechanisms and for the subsampled Gaussian mechanism using FFT. In: The 24th International Conference on Artificial Intelligence and Statistics, (AISTATS 2021). Proceedings of Machine Learning Research, vol. 130, pp. 3358–3366. PMLR (2021), http://proceedings.mlr.press/v130/koskela21a.html
2021
Cited alongside, same era.
Mathew, M., Karatzas, D., Jawahar, C.: Docvqa: A dataset for vqa on document images. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2200–2209 (2021)
2021
Cited alongside, same era.
Powalski, R., Borchmann, Ł., Jurkiewicz, D., Dwojak, T., Pietruszka, M., Palka, G.: Going full-tilt boogie on document understanding with text-image-layout transformer. In: Document Analysis and Recognition–ICDAR 2021: 16th International Conference, Lausanne, Switzerland, September 5–10, 2021, Proceedings, Part II 16. pp. 732–747. Springer (2021)
2021
Cited alongside, same era.
Tanaka, R., Nishida, K., Yoshida, S.: Visualmrc: Machine reading comprehension on document images. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 35, pp. 13878–13888 (2021)
2021
Cited alongside, same era.
Tito, R., Karatzas, D., Valveny, E.: Document collection visual question answering. In: International Conference on Document Analysis and Recognition. pp. 778–792. Springer (2021)
2021
Cited alongside, same era.
Tito, R., Mathew, M., Jawahar, C., Valveny, E., Karatzas, D.: Icdar 2021 competition on document visual question answering. In: International Conference on Document Analysis and Recognition. pp. 635–649. Springer (2021)
2021
Cited alongside, same era.
Xu, Y., Xu, Y., Lv, T., Cui, L., Wei, F., Wang, G., Lu, Y., Florencio, D., Zhang, C., Che, W., et al.: Layoutlmv2: Multi-modal pre-training for visually-rich document understanding. In: Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). pp. 2579–2591 (2021)
2021
Cited alongside, same era.
Carlini, N., Chien, S., Nasr, M., Song, S., Terzis, A., Tramer, F.: Membership inference attacks from first principles. In: 2022 IEEE Symposium on Security and Privacy (SP). pp. 1897–1914. IEEE (2022)
2022
Cited alongside, same era.
2022
Later among the works it cites.
Tirumala, K., Markosyan, A., Zettlemoyer, L., Aghajanyan, A.: Memorization without overfitting: Analyzing the training dynamics of large language models. Advances in Neural Information Processing Systems 35
2022
Later among the works it cites.
Wainakh, A., Ventola, F., Müßig, T., Keim, J., Cordero, C.G., Zimmer, E., Grube, T., Kersting, K., Mühlhäuser, M.: User-level label leakage from gradients in federated learning. Proceedings on Privacy Enhancing Technologies 2022
2022
Later among the works it cites.
Web: Industry Documents Library. https://www.industrydocuments.ucsf.edu/ , accessed: 2022-10-20
2022
Later among the works it cites.
Web: Public Inspection Files. https://publicfiles.fcc.gov/ , accessed: 2022-10-20
2022
Later among the works it cites.
Yu, D., Naik, S., Backurs, A., Gopi, S., Inan, H.A., Kamath, G., Kulkarni, J., Lee, Y.T., Manoel, A., Wutschitz, L., Yekhanin, S., Zhang, H.: Differentially private fine-tuning of language models. In: The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net (2022), https://openreview.net/forum?id=Q42f0dfjECO
2022
Later among the works it cites.
Amazon: Amazon textract. https://aws.amazon.com/textract/ (2021), accessed on Oct. 10, 2023
2023
Closest in time.
Galli, F., Biswas, S., Jung, K., Cucinotta, T., Palamidessi, C.: Group privacy for personalized federated learning. In: Proceedings of the 9th International Conference on Information Systems Security and Privacy. SCITEPRESS - Science and Technology Publications (2023)
2023
Closest in time.
Ko, M., Jin, M., Wang, C., Jia, R.: Practical membership inference attacks against large-scale multi-modal models: A pilot study. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 4871–4881 (2023)
2023
Closest in time.
Lee, K., Joshi, M., Turc, I., Hu, H., Liu, F., Eisenschlos, J., Khandelwal, U., Shaw, P., Chang, M.W., Toutanova, K.: Pix2struct: screenshot parsing as pretraining for visual language understanding. In: Proceedings of the 40th International Conference on Machine Learning. ICML’23, JMLR.org (2023)
2023
Closest in time.
Mehta, H., Thakurta, A.G., Kurakin, A., Cutkosky, A.: Towards large scale transfer learning for differentially private image classification. Trans. Mach. Learn. Res. 2023
2023
Closest in time.
OpenAI: GPT-4 technical report. CoRR abs/2303.08774
2023
Closest in time.
2023
Closest in time.
Tito, R., Karatzas, D., Valveny, E.: Hierarchical multimodal transformers for multipage docvqa. Pattern Recognition 144
2023
Closest in time.
Tobaben, M., Shysheya, A., Bronskill, J., Paverd, A., Tople, S., Béguelin, S.Z., Turner, R.E., Honkela, A.: On the efficacy of differentially private few-shot image classification. Transactions on Machine Learning Research (2023), https://openreview.net/forum?id=hFsr59Imzm
2023
Closest in time.
Van Landeghem, J., Tito, R., Borchmann, Ł., Pietruszka, M., Joziak, P., Powalski, R., Jurkiewicz, D., Coustaty, M., Anckaert, B., Valveny, E., et al.: Document understanding dataset and evaluation (dude). In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19528–19540 (2023)
2023
Closest in time.
Van Landeghem, J., Tito, R., Borchmann, Ł., Pietruszka, M., Jurkiewicz, D., Powalski, R., Józiak, P., Biswas, S., Coustaty, M., Stanisławek, T.: Icdar 2023 competition on document understanding of everything (dude). In: International Conference on Document Analysis and Recognition. pp. 420–434. Springer (2023)
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.