[1] Hassan Akbari, Liangzhe Yuan, Rui Qian, Wei-Hong Chuang, Shih-Fu Chang, Yin Cui, and Boqing Gong. Vatt: Transformers for multimodal self-supervised learning from raw video, audio and text. Advances in Neural Information Processing Systems, 34:24206-24221, 2021.
[2] Jean-Baptiste Alayrac, Adria Recasens, Rosalia Schneider, Relja Arandjelović, Jason Ramapuram, Jeffrey De Fauw, Lucas Smaira, Sander Dieleman, and Andrew Zisserman. Self-supervised multimodal versatile networks. Advances in Neural Information Processing Systems, 33:25-37, 2020.
[3] John Arevalo, Thamar Solorio, Manuel Montes-y Gomez, and Fabio A González. Gated multimodal units for information fusion. In 5th International conference on learning representations workshop., 2017.
[4] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.
[5] Anas Belouali, Samir Gupta, Vaibhav Sourirajan, Jiawei Yu, Nathaniel Allen, Adil Alaoui, Mary Ann Dutton, and Matthew J Reinhard. Acoustic and language analysis of speech for suicidal ideation among us veterans. BioData mining, 14(1):1-17, 2021.
[6] Ioana Bica and Mihaela van der Schaar. Transfer learning on heterogeneous feature spaces for treatment effects estimation. arXiv preprint arXiv:2210.06183, 2022.
[7] Brandon M Booth, Louis Hickman, Shree Krishna Subburaj, Louis Tay, Sang Eun Woo, and Sidney K D'Mello. Bias and fairness in multimodal machine learning: A case study of automated video interviews. In Proceedings of the 2021 International Conference on Multimodal Interaction, pages 268-277, 2021.
[8] Carlos Busso, Murtaza Bulut, Chi-Chun Lee, Abe Kazemzadeh, Emily Mower, Samuel Kim, Jeannette N Chang, Sungbok Lee, and Shrikanth S Narayanan. Iemocap: Interactive emotional dyadic motion capture database. Language resources and evaluation, 42:335-359, 2008.
[9] D Calvetti, S Morigi, L Reichel, and F Sgallari. Tikhonov regularization and the l-curve for large discrete ill-posed problems. Journal of computational and applied mathematics, 123(1-2): 423-446, 2000.
[10] Joseph DelPreto, Chao Liu, Yiyue Luo, Michael Foshey, Yunzhu Li, Antonio Torralba, Wojciech Matusik, and Daniela Rus. Actionsense: A multimodal dataset and recording framework for human activities using wearable sensors in a kitchen environment. Advances in Neural Information Processing Systems, 35:13800-13813, 2022.
[11] Aymeric Dieuleveut, Gersende Fort, Eric Moulines, and Geneviève Robin. Federatedem with heterogeneity mitigation and variance reduction. In M. Ranzato, A. Beygelzimer, Y. Dauphin, P.S. Liang, and J. Wortman Vaughan, editors, Advances in Neural Information Processing Systems, volume 34, pages 29553-29566. Curran Associates, Inc., 2021. URL https://proceedings.neurips.cc/paper_files/paper/2021/file/f740c8d9c193f16d8a07d3a8a751d13f-Paper.pdf.
[12] Paul-Ambroise Duquenne, Hongyu Gong, and Holger Schwenk. Multimodal and multilingual embeddings for large-scale speech mining. Advances in Neural Information Processing Systems, 34:15748-15761, 2021.
[13] Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, Mannat Singh, Kalyan Vasudev Alwala, Armand Joulin, and Ishan Misra. Imagebind: One embedding space to bind them all. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 15180-15190, 2023.
[14] Paul Hager, Martin J Menten, and Daniel Rueckert. Best of both worlds: Multimodal contrastive learning with tabular and imaging data. accepted for CVPR, 2023.