[1] Luisa Bentivogli, Peter Clark, Ido Dagan, and Danilo Giampiccolo. The fifth pascal recognizing textual entailment challenge. In TAC, 2009.
[2] Daniel Cer, Mona Diab, Eneko Agirre, Inigo Lopez-Gazpio, and Lucia Specia. Semeval2017 task 1: Semantic textual similarity-multilingual and cross-lingual focused evaluation. arXiv preprint arXiv::1708.00055, 2017.
[3] Minghao Chen, Houwen Peng, Jianlong Fu, and Haibin Ling. Autoformer: Searching transformers for visual recognition. In ICCV, 2021.
[4] T. Chen, E. B. Fox, and C. Guestrin. Stochastic gradient Hamiltonian Monte Carlo. International Conference on Machine Learning, 2014.
[5] Ido Dagan, Oren Glickman, and Bernardo Magnini. The pascal recognising textual entailment challenge. In Machine Learning Challenges Workshop, 2005.
[6] Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
[7] N. Ding, Y. Fang, R. Babbush, C. Chen, R. D. Skeel, and H. Neven. Bayesian sampling using stochastic gradient thermostats. In Advances in neural information processing systems, 2014.
[8] Bill Dolan and Chris Brockett. Automatically constructing a corpus of sentential paraphrases. In Third International Workshop on Paraphrasing (IWP2005), 2005.
[9] Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby. An image is worth 16x16 words: Transformers for image recognition at scale. ICLR, 2021.
[10] Zihao Fu, Haoran Yang, Anthony Man-Cho So, Wai Lam, Lidong Bing, and Nigel Collier. On the Effectiveness of Parameter-Efficient Fine-Tuning. AAAI, 2023.
[11] Danilo Giampiccolo, Bernardo Magnini, Ido Dagan, and William B Dolan. The third pascal recognizing textual entailment challenge. In Proceedings of the ACL-PASCAL workshop on textual entailment and paraphrasing, 2007.
[12] Henry Gouk, Timothy M Hospedales, and Massimiliano Pontil. Distance-Based Regularisation of Deep Networks for Fine-Tuning. In ICLR, 2021.
[13] Demi Guo, Alexander M. Rush, and Yoon Kim. Parameter-efficient transfer learning with diff pruning. In ACL, 2021.
[14] Song Han, Huizi Mao, and William J Dally. Deep compression: Compressing deep neural networks with pruning, trained quantization and Huffman coding. *ICLR*, 2016.
[15] Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. Parameter-efficient transfer learning for NLP. In International Conference on Machine Learning, 2019.
[16] Edward J Hu, Yelong Shen, Phillip Wallis, Zeyuan Allen-Zhu, Yuanzhi Li, Shean Wang, Lu Wang, and Weizhu Chen. LoRA: Low-rank adaptation of large language models. *ICLR*, 2022.
[17] Pavel Izmailov, Sharad Vikram, Matthew D. Hoffman, and Andrew Gordon Wilson. What are Bayesian neural network posteriors really like? International Conference on Machine Learning, 2021.
[18] Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim. Visual prompt tuning. In ECCV, 2022.
[19] Sehwan Kim, Qifan Song, and Faming Liang. Stochastic Gradient Langevin Dynamics Algorithms with Adaptive Drifts. arXiv preprint arXiv:2009.09535, 2020.
[20] D. P. Kingma and L. J. Ba. Adam: A Method for Stochastic Optimization. In ICLR, 2015.
[21] Insung Kong, Dongyoon Yang, Jongjin Lee, Ilsang Ohn, Gyuseung Baek, and Yongdai Kim. Masked Bayesian Neural Networks : Theoretical Guarantee and its Posterior Inference. International Conference on Machine Learning, 2023.