[26] Ghorbani, B., First, O., Freitag, M., Bapna, A., Krikun, M., Garcia, X., Chelba, C., and Cherry, C. (2021). Scaling laws for neural machine translation. arXiv preprint arXiv:2109.07740. 1
[27] Gordon, M. A., Duh, K., and Kaplan, J. (2021). Data and parameter scaling laws for neural machine translation. In Conference on Empirical Methods in Natural Language Processing. 1, 4
[28] Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., and Parikh, D. (2017). Making the v in vqa matter: Elevating the role of image understanding in visual question answering. In CVPR. 7, 9, 10
[29] He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep residual learning for image recognition. In Conference on Computer Vision and Pattern Recognition (CVPR). 2
[30] Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., Song, D., Steinhardt, J., and Gilmer, J. (2021a). The many faces of robustness: A critical analysis of out-of-distribution generalization. ICCV. 9
[31] Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., and Song, D. (2021b). Natural adversarial examples. CVPR. 9
[32] Henighan, T., Kaplan, J., Katz, M., Chen, M., Hesse, C., Jackson, J., Jun, H., Brown, T. B., Dhariwal, P., Gray, S., et al. (2020). Scaling laws for autoregressive generative modeling. arXiv preprint arXiv:2010.14701. 1
[33] Hernandez, D., Kaplan, J., Henighan, T., and McCandlish, S. (2021). Scaling laws for transfer. arXiv preprint arXiv:2102.01293. 1
[34] Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M., Ali, M., Yang, Y., and Zhou, Y. (2017). Deep learning scaling is predictable, empirically. arXiv preprint arXiv:1712.00409. 1, 4
[35] Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. d. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022). Training compute-optimal large language models. In Advances in neural information processing systems (NeurIPS). 1, 2, 3, 4, 5
[36] Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., and Adam, H. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861. 2, 3
[37] Hudson, D. A. and Manning, C. D. (2019). GQA: a new dataset for compositional question answering over real-world images. arXiv preprint arXiv:1902.09506. 7, 9, 10
[38] Hutter, M. (2021). Learning curve theory. arXiv preprint arXiv:2102.04074. 4, 6
[39] Kaplan, J., McCandlish, S., Henighan, T., Brown, T. B., Chess, B., Child, R., Gray, S., Radford, A., Wu, J., and Amodei, D. (2020). Scaling laws for neural language models. arXiv preprint arXiv:2001.08361. 1, 3, 4, 5
[40] Kather, J. N., Weis, C.-A., Bianconi, F., Melchers, S. M., Schad, L. R., Gaiser, T., Marx, A., and Z"ollner, F. G. (2016). Multi-class texture analysis in colorectal cancer histology. Scientific reports, 6:27988. 9
[41] Kingma, D. P. and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. 5
[42] Kirillov, A., He, K., Girshick, R., Rother, C., and Dollar, P. (2019). Panoptic segmentation. In Conference on Computer Vision and Pattern Recognition (CVPR). 9
[43] Kolesnikov, A., Beyer, L., Zhai, X., Puigcerver, J., Yung, J., Gelly, S., and Houlsby, N. (2020). Big transfer (BiT): General visual representation learning. In European Conference on Computer Vision (ECCV). 1, 2, 5, 8
[44] Kolesnikov, A., Susano Pinto, A., Beyer, L., Zhai, X., Harmsen, J., and Houlsby, N. (2022). UViM: A unified modeling approach for vision with learned guiding codes. Advances in neural information processing systems (NeurIPS). 9