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In this paper, we present a groundbreaking paradigm for human-computer interaction that revolutionizes the traditional notion of an operating system.
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A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pretraining,” 2018
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2019
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A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, and I. Sutskever, “Language models are unsupervised multitask learners,” OpenAI Blog
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T. B. Brown, B. Mann, N. Ryder, M. Subbiah, J. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, S. Agarwal, A. Herbert-Voss, G. Krueger, T. Henighan, R. Child, A. Ramesh, D. M. Ziegler, J. Wu, C. Winter, C. Hesse, M. Chen, E. Sigler, M. Litwin, S. Gray, B. Chess, J. Clark, C. Berner, S. McCandlish, A. Radford, I. Sutskever, and D. Amodei, “Language models are few-shot learners,” 2020
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O. Halimi, I. Imanuel, O. Litany, G. Trappolini, E. Rodolà, L. Guibas, and R. Kimmel, “Towards precise completion of deformable shapes,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16
2020
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D. Zügner, O. Borchert, A. Akbarnejad, and S. Günnemann, “Adversarial attacks on graph neural networks: Perturbations and their patterns,” ACM Transactions on Knowledge Discovery from Data (TKDD)
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OpenAI, “Chatgpt by openai,” 2021
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2021
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J. Thorne, M. Yazdani, M. Saeidi, F. Silvestri, S. Riedel, and A. Halevy, “Database reasoning over text,” 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)
2021
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2023
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M. Zhang, O. Press, W. Merrill, A. Liu, and N. A. Smith, “How language model hallucinations can snowball,” 2023
2023
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2023
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G. Trappolini, A. Santilli, E. Rodolà, A. Halevy, and F. Silvestri, “Multimodal neural databases,” in Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval
2023
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G. Trappolini, L. Cosmo, L. Moschella, R. Marin, S. Melzi, and E. Rodolà, “Shape registration in the time of transformers,” Advances in Neural Information Processing Systems
2021
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C. Campagnano, S. Conia, and R. Navigli, “SRL4E – Semantic Role Labeling for Emotions: A unified evaluation framework,” in Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
2022
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M. Welsh, “The end of programming,” Commun. ACM
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” 2022
2022
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Y. Li, C.-Y. Wu, H. Fan, K. Mangalam, B. Xiong, J. Malik, and C. Feichtenhofer, “Mvitv2: Improved multiscale vision transformers for classification and detection,” 2022
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2022
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Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, L. Li, and Z. Sui, “A survey on in-context learning,” 2023
2023
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2023
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S. G. Patil, T. Zhang, X. Wang, and J. E. Gonzalez, “Gorilla: Large language model connected with massive apis,” 2023
2023
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J. Wei, A.-L. Courbis, T. Lambolais, B. Xu, P. L. Bernard, and G. Dray, “Boosting gui prototyping with diffusion models,” 2023
2023
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Z. Borsos, R. Marinier, D. Vincent, E. Kharitonov, O. Pietquin, M. Sharifi, D. Roblek, O. Teboul, D. Grangier, M. Tagliasacchi, et al
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G. Barnabò, G. Trappolini, L. Lastilla, C. Campagnano, A. Fan, F. Petroni, and F. Silvestri, “Cycledrums: automatic drum arrangement for bass lines using cyclegan,” Discover Artificial Intelligence
2023
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Z. Luo, D. Chen, Y. Zhang, Y. Huang, L. Wang, Y. Shen, D. Zhao, J. Zhou, and T. Tan, “Videofusion: Decomposed diffusion models for high-quality video generation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
2023
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Z. Chen, G. Wang, and Z. Liu, “Scenedreamer: Unbounded 3d scene generation from 2d image collections,” 2023
2023
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