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- LLM.md is the canonical AI context file - Added research papers and proposals - CLAUDE.md symlink gitignored
39 lines
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39 lines
1.5 KiB
Plaintext
\begin{thebibliography}{1}
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\bibitem{assran2023self}
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Mahmoud Assran, Quentin Duval, Ishan Misra, Piotr Bojanowski, Pascal Vincent,
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Michael Rabbat, Yann LeCun, and Nicolas Ballas.
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\newblock Self-supervised learning from images with a joint-embedding
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predictive architecture.
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\newblock {\em arXiv preprint arXiv:2301.08243}, 2023.
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\bibitem{bardes2024v}
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Adrien Bardes, Quentin Garrido, Jean Ponce, Xinlei Chen, Michael Rabbat, Yann
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LeCun, Mahmoud Assran, and Nicolas Ballas.
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\newblock V-jepa: Latent video prediction for visual representation learning.
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\newblock {\em arXiv preprint arXiv:2404.02626}, 2024.
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\bibitem{chen2020simple}
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Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton.
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\newblock A simple framework for contrastive learning of visual
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representations.
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\newblock {\em International conference on machine learning}, pages 1597--1607,
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2020.
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\bibitem{he2020momentum}
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Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick.
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\newblock Momentum contrast for unsupervised visual representation learning.
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\newblock {\em Proceedings of the IEEE/CVF conference on computer vision and
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pattern recognition}, pages 9729--9738, 2020.
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\bibitem{radford2021learning}
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Alec Radford, Jong~Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh,
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Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark,
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et~al.
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\newblock Learning transferable visual models from natural language
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supervision.
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\newblock {\em International conference on machine learning}, pages 8748--8763,
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2021.
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\end{thebibliography}
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