[1] Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless C Fowlkes, Stefano Soatto, and Pietro Perona. Task2vec: Task embedding for meta-learning. In Proceedings of the IEEE/CVF international conference on computer vision, pages 6430-6439, 2019.
[2] Jimmy Lei Ba, Jamie Ryan Kiros, and Geoffrey E Hinton. Layer normalization. arXiv preprint arXiv:1607.06450, 2016.
[3] Ankur Bapna, Naveen Arivazhagan, and Orhan First. Controlling computation versus quality for neural sequence models. arXiv preprint arXiv:2002.07106, 2020.
[4] Yoshua Bengio, Nicholas Léonard, and Aaron Courville. Estimating or propagating gradients through stochastic neurons for conditional computation. arXiv preprint arXiv:1308.3432, 2013.
[5] Alice Capecchi, Daniel Probst, and Jean-Louis Reymond. One molecular fingerprint to rule them all: drugs, biomolecules, and the metabolome. Journal of cheminformatics, 12(1):1-15, 2020.
[6] Fabio Capela, Vincent Nouchi, Ruud Van Deursen, Igor V Tetko, and Guillaume Godin. Multitask learning on graph neural networks applied to molecular property predictions. arXiv preprint arXiv:1910.13124, 2019.
[7] Adrià Cereto-Massague, María José Ojeda, Cristina Valls, Miquel Mulero, Santiago Garcia-Vallé, and Gerard Pujadas. Molecular fingerprint similarity search in virtual screening. Methods, 71:58-63, 2015.
[8] Zhao Chen, Vijay Badrinarayanan, Chen-Yu Lee, and Andrew Rabinovich. Gradnorm: Gradient normalization for adaptive loss balancing in deep multitask networks. In International conference on machine learning, pages 794-803. PMLR, 2018.
[9] Carl Doersch. Tutorial on variational autoencoders. arXiv preprint arXiv:1606.05908, 2016.
[10] Angela Fan, Edouard Grave, and Armand Joulin. Reducing transformer depth on demand with structured dropout. arXiv preprint arXiv:1909.11556, 2019.
[11] Eli Fernández-de Gortari, César R García-Jacas, Karina Martinez-Mayorga, and José L Medina-Franco. Database fingerprint (dfp): an approach to represent molecular databases. Journal of cheminformatics, 9(1):1-9, 2017.
[12] Chris Fifty, Ehsan Amid, Zhe Zhao, Tianhe Yu, Rohan Anil, and Chelsea Finn. Efficiently identifying task groupings for multi-task learning. Advances in Neural Information Processing Systems, 34:27503-27516, 2021.
[13] Markus Freitag and Yaser Al-Onaizan. Beam search strategies for neural machine translation. arXiv preprint arXiv:1702.01806, 2017.
[14] Anna Gaulton, Louisa J Bellis, A Patricia Bento, Jon Chambers, Mark Davies, Anne Hersey, Yvonne Light, Shaun McGlinchey, David Michalovich, Bissan Al-Lazikani, et al. Chembl: a large-scale bioactivity database for drug discovery. Nucleic acids research, 40(D1):D1100-D1107, 2012.
[15] Felix A Gers, Jürgen Schmidhuber, and Fred Cummins. Learning to forget: Continual prediction with LSTM. Neural computation, 12(10):2451-2471, 2000.
[16] Zhenyu Hou, Xiao Liu, Yukuo Cen, Yuxiao Dong, Hongxia Yang, Chunjie Wang, and Jie Tang. Graphmae: Self-supervised masked graph autoencoders. In Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, pages 594-604, 2022.
[17] Weihua Hu, Matthias Fey, Hongyu Ren, Maho Nakata, Yuxiao Dong, and Jure Leskovec. Ogb-lsc: A large-scale challenge for machine learning on graphs. arXiv preprint arXiv:2103.09430, 2021.
[18] Weihua Hu, Matthias Fey, Marinka Zitnik, Yuxiao Dong, Hongyu Ren, Bowen Liu, Michele Catasta, and Jure Leskovec. Open graph benchmark: Datasets for machine learning on graphs. Advances in neural information processing systems, 33:22118-22133, 2020.
[19] Weihua Hu, Bowen Liu, Joseph Gomes, Marinka Zitnik, Percy Liang, Vijay Pande, and Jure Leskovec. Strategies for pre-training graph neural networks. arXiv preprint arXiv:1905.12265, 2019.
[20] Ziniu Hu, Zhe Zhao, Xinyang Yi, Tiansheng Yao, Lichan Hong, Yizhou Sun, and Ed Chi. Improving multi-task generalization via regularizing spurious correlation. Advances in Neural Information Processing Systems, 35:11450-11466, 2022.