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In recent years, diffusion models have demonstrated remarkable potential across diverse domains, from vision generation to language modeling.
D. P. Kingma, M. Welling, and et. al., “Auto-encoding variational bayes,” 2013
2013
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Jun 2016. [Online]. Available: http://dx.doi.org/10.1109/cvpr.2016.90
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin, “Attention is all you need,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
R. A. Amjad and B. C. Geiger, “Learning representations for neural network-based classification using the information bottleneck principle,” IEEE transactions on pattern analysis and machine intelligence , vol. 42, no. 9, pp. 2225–2239, 2019
2019
Earlier work this paper cites.
W. A. Falcon, “Pytorch lightning,” GitHub , vol. 3, 2019
2019
Earlier work this paper cites.
Y. Lee, J.-w. Hwang, S. Lee, Y. Bae, and J. Park, “An energy and gpu-computation efficient backbone network for real-time object detection,” in 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , Jun 2019. [Online]. Available: http://dx.doi.org/10.1109/cvprw.2019.00103
2019
Earlier work this paper cites.
A. Sadat, S. Casas, M. Ren, X. Wu, P. Dhawan, and R. Urtasun, Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations , Jan 2020, p. 414–430. [Online]. Available: http://dx.doi.org/10.1007/978-3-030-58592-1_25
2020
Earlier work this paper cites.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” Advances in neural information processing systems , vol. 33, pp. 6840–6851, 2020
2020
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial networks,” Communications of the ACM , vol. 63, no. 11, pp. 139–144, 2020
2020
Earlier work this paper cites.
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton, “A simple framework for contrastive learning of visual representations,” in International conference on machine learning . PmLR, 2020, pp. 1597–1607
2020
Earlier work this paper cites.
T. Hua, W. Wang, Z. Xue, S. Ren, Y. Wang, and H. Zhao, “On feature decorrelation in self-supervised learning,” in Proceedings of the IEEE/CVF international conference on computer vision , 2021, pp. 9598–9608
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
K. Chitta, A. Prakash, B. Jaeger, Z. Yu, K. Renz, and A. Geiger, “Transfuser: Imitation with transformer-based sensor fusion for autonomous driving,” IEEE transactions on pattern analysis and machine intelligence , vol. 45, no. 11, pp. 12 878–12 895, 2022
2022
Earlier work this paper cites.
Y. Xue, K. Whitecross, and B. Mirzasoleiman, “Investigating why contrastive learning benefits robustness against label noise,” in Proceedings of the 39th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 162. PMLR, 17–23 Jul 2022, pp. 24 851–24 871. [Online]. Available: https://proceedings.mlr.press/v162/xue22a.html
2022
Earlier work this paper cites.
H. Wang, H. Tang, S. Shi, A. Li, Z. Li, B. Schiele, and L. Wang, “Unitr: A unified and efficient multi-modal transformer for bird’s-eye-view representation,” in Proceedings of the IEEE/CVF international conference on computer vision , 2023, pp. 6792–6802
2023
Earlier work this paper cites.
Y. Hu, J. Yang, L. Chen, K. Li, C. Sima, and et. al., “Planning-oriented autonomous driving,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 17 853–17 862
2023
Earlier work this paper cites.
B. Jiang, S. Chen, Q. Xu, B. Liao, J. Chen, H. Zhou, Q. Zhang, W. Liu, C. Huang, and X. Wang, “Vad: Vectorized scene representation for efficient autonomous driving,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2023, pp. 8340–8350
2023
Cited alongside, same era.
2023
Cited alongside, same era.
2023
Cited alongside, same era.
Y. Shi, J. Liang, and et. al., “Understanding and mitigating dimensional collapse in federated learning,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 46, no. 5, pp. 2936–2949, 2024. [Online]. Available: https://doi.org/10.1109/TPAMI.2023.3338063
X. Jiang, J. Li, N. Wu, Z. Wu, X. Li, S. Sun, G. Xu, Y. Wang, Q. Li, and M. Liu, “Fnbench: Benchmarking robust federated learning against noisy labels,” Authorea Preprints , 2024
2024
Later among the works it cites.
D. Dauner, M. Hallgarten, T. Li, X. Weng, Z. Huang, Z. Yang, H. Li, I. Gilitschenski, B. Ivanovic, M. Pavone, and et. al., “Navsim: Data-driven non-reactive autonomous vehicle simulation and benchmarking,” Advances in Neural Information Processing Systems , vol. 37, pp. 28 706–28 719, 2024
2024
Later among the works it cites.
2024
Later among the works it cites.
2024
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2023
Cited alongside, same era.
H. Lee, K. Lee, D. Hwang, H. Lee, B. Lee, and J. Choo, “On the importance of feature decorrelation for unsupervised representation learning in reinforcement learning,” in International Conference on Machine Learning . PMLR, 2023, pp. 18 988–19 009
2023
Cited alongside, same era.
S. Peng, K. Genova, C. Jiang, A. Tagliasacchi, M. Pollefeys, T. Funkhouser, and et. al., “Openscene: 3d scene understanding with open vocabularies,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2023, pp. 815–824
2023
Cited alongside, same era.
D. Dauner, M. Hallgarten, A. Geiger, and K. Chitta, “Parting with misconceptions about learning-based vehicle motion planning,” Jun 2023
2023
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
2024
Cited alongside, same era.
Later among the works it cites.
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X. Lu, P. Li, and X. Jiang, “Fedlf: Adaptive logit adjustment and feature optimization in federated long-tailed learning,” in Asian Conference on Machine Learning . PMLR, 2025, pp. 303–318
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X. Jiang, P. Li, S. Sun, J. Li, L. Wu, Y. Wang, X. Lu, X. Ma, and M. Liu, “Refining distributed noisy clients: An end-to-end dual optimization framework,” Authorea Preprints , 2025
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