References
- Bareinboim, E., & Pearl, J. (2016). Causal inference and the data-fusion problem. Proceedings of the National Academy of Sciences of the United States of America, 113(27), 7345–7352. https://doi.org/10.1073/pnas.1510507113
- Belkin, M., Hsu, D., Ma, S., & Mandal, S. (2019). Reconciling modern machine-learning practice and the classical bias–variance trade-off. Proceedings of the National Academy of Sciences of the United States of America, 116(32), 15849–15854. https://doi.org/10.1073/pnas.1903070116
- Bommasani, R., Hudson, D. A., Adeli, E., Altman, R., Arora, S., von Arx, S., Bernstein, M. S., Bohg, J., Bosselut, A., Brunskill, E., & Brynjolfsson, E. (2021). On the opportunities and risks of foundation models. arXiv Preprint arXiv, 2108.07258.
- Brick, T. R., Koffer, R. E., Gerstorf, D., & Ram, N. (2017). Feature selection methods for optimal design of studies for developmental inquiry. The Journals of Gerontology. Series B, Psychological Sciences and Social Sciences, 73(1), 113–123. https://doi.org/10.1093/geronb/gbx008
- Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., & Agarwal, S. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33, 1877–1901.
- Chen, L., Lu, K., Rajeswaran, A., Lee, K., Grover, A., Laskin, M., Abbeel, P., Srinivas, A., & Mordatch, I. (2021). Decision transformer: Reinforcement learning via sequence modeling. Advances in Neural Information Processing Systems, 34, 15084–15097.
- Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv Preprint arXiv:1810.04805.
- Dollar, O., Joshi, N., Beck, D. A., & Pfaendtner, J. (2021). Attention-based generative models for de novo molecular design. Chemical Science, 12(24), 8362–8372. https://doi.org/10.1039/d1sc01050f
- Dube, S. (2021). Intuitive exploration of artificial intelligence. Springer International Publishing.
- Economist. (2022). Huge "foundation models" are turbo-charging AI progress. The Economist. 10 June 2022, ISSN 0013-0613. Retrieved 10 June 2022.
- Gates, K. M., & Molenaar, P. C. (2012). Group search algorithm recovers effective connectivity maps for individuals in homogeneous and heterogeneous samples. NeuroImage, 63(1), 310–319. https://doi.org/10.1016/j.neuroimage.2012.06.026
- Gates, K. M., Molenaar, P. C. M., Hillary, F., Ram, N., & Rovine, M. (2010). Automatic search for fMRI connectivity mapping: An alternative to Granger causality testing using formal equivalences between SEM path modeling, VAR, and unified SEM. NeuroImage, 50(3), 1118–1125. https://doi.org/10.1016/j.neuroimage.2009.12.117
- Judd, C. M., Westfall, J., & Kenny, D. A. (2012). Treating stimuli as a random factor in social psychology: A new and comprehensive solution to a pervasive but largely ignored problem. Journal of Personality and Social Psychology, 103(1), 54–69. https://doi.org/10.1037/a0028347
- Khemakhem, I., Kingma, D., Monti, R., & Hyvarinen, A. (2020, June). Variational autoencoders and nonlinear ica: A unifying framework. In International Conference on Artificial Intelligence and Statistics (pp. 2207–2217). Proceedings of Machine Learning Research, 108.
- Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv Preprint arXiv:1907.11692.
- Martinez-Martin, N., Luo, Z., Kaushal, A., Adeli, E., Haque, A., Kelly, S. S., Wieten, S., Cho, M. K., Magnus, D., Fei-Fei, L., Schulman, K., & Milstein, A. (2021). Ethical issues in using ambient intelligence in health-care settings. The Lancet. Digital Health, 3(2), e115–e123. https://doi.org/10.1016/S2589-7500(20)30275-2
- Molenaar, P. C. (2004). A manifesto on psychology as idiographic science: Bringing the person back into scientific psychology, this time forever. Measurement: Interdisciplinary Research & Perspective, 2(4), 201–218. https://doi.org/10.1207/s15366359mea0204_1
- Molenaar, P., Boomsma, D. I., & Dolan, C. V. (1993). A third source of developmental differences. Behavior Genetics, 23(6), 519–524. https://doi.org/10.1007/BF01068142
- Molenaar, P. C. M., & Ram, N. (2010). Dynamic modeling and optimal control of intraindividual variation: A computational paradigm for nonergodic psychological processes. In S.-M. Chow, E. Ferrer, & F. Hsieh (Eds.), Statistical methods for modeling human dynamics: An interdisciplinary dialogue (pp. 13–37). Routledge/Taylor & Francis Group.
- Molenaar, P. C. M., *Sinclair, K., Rovine, M. J., Ram, N., & Corneal, S. E. (2009). Analysis of developmental processes based on intra-individual variation by means of non-stationary time series modeling. Developmental Psychology, 45, 260–271.
- Palatucci, M., Pomerleau, D., Hinton, G. E., & Mitchell, T. M. (2009). Zero-shot learning with semantic output codes. Advances in Neural Information Processing Systems, 22
- Pearl, J., & Bareinboim, E. (2014). External validity: From do-calculus to transportability across populations. Statistical Science, 29(4), 579–595. https://doi.org/10.1214/14-STS486
- Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., & Krueger, G. (2021). Learning transferable visual models from natural language supervision. In International Conference on Machine Learning (pp. 8748–8763). PMLR.
- Raji, I. D., Bender, E. M., Paullada, A., Denton, E., & Hanna, A. (2021). AI and the everything in the whole wide world benchmark. arXiv Preprint arXiv:2111.15366.
- Ram, N., Brose, A., & Molenaar, P. C. M. (2013). Dynamic factor analysis: Modeling person-specific process. In T. Little (Ed.), Oxford handbook of quantitative methods Volume 2 Statistical Analysis (pp. 441–457). New York: Oxford University Press.
- Ram, N., Conroy, D., Pincus, A. L., Lorek, A., Rebar, A. H., Roche, M. J., Morack, J., Coccia, M., Feldman, J., & Gerstorf, D. (2014). Examining the interplay of processes across multiple time-scales: Illustration with the Intraindividual Study of Affect, Health, and Interpersonal Behavior (iSAHIB). Research in Human Development, 11, 142–160.
- Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., & Chen, M. (2022). Hierarchical text-conditional image generation with clip latents. arXiv Preprint arXiv:2204.06125.
- Reeves, B., Ram, N., Robinson, T. N., Cummings, J. J., Giles, C. L., Pan, J., Chiatti, A., Cho, M. J., Roehrick, K., Yang, X., Gagneja, A., Brinberg, M., Muise, D., Lu, Y., Luo, M., Fitzgerald, A., & Yeykelis, L. (2021). Screenomics: A framework to capture and analyze personal life experiences and the ways that technology shapes them. Human-Computer Interaction, 36(2), 150–201. https://doi.org/10.1080/07370024.2019.1578652
- Reeves, B., Robinson, T. R., & Ram, N. (2020). Time for the human Screenome project. Nature, 577(7790), 314–317. https://doi.org/10.1038/d41586-020-00032-5
- Schrimpf, M., Kubilius, J., Hong, H., Majaj, N. J., Rajalingham, R., Issa, E. B., Kar, K., Bashivan, P., Prescott-Roy, J., Geiger, F., & Schmidt, K. (2020). Brain-score: Which artificial neural network for object recognition is most brain-like? BioRxiv, 407007.
- Srivastava, S., Li, C., Lingelbach, M., Martín-Martín, R., Xia, F., Vainio, K. E., Lian, Z., Gokmen, C., Buch, S., Liu, K., & Savarese, S. (2022). BEHAVIOR: Benchmark for everyday household activities in virtual, interactive, and ecological environments. In Conference on Robot Learning (pp. 477–490). PMLR.
- Tuarob, S., Tucker, C. S., Kumara, S., Giles, C. L., Pincus, A. L., Conroy, D. E., & Ram, N. (2017). How are you feeling? A personalized methodology for predicting mental states from temporally observable physical and behavioral information. Journal of Biomedical Informatics, 68, 1–19. https://doi.org/10.1016/j.jbi.2017.02.010
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30).
- Yamins, D. L., Hong, H., Cadieu, C. F., Solomon, E. A., Seibert, D., & DiCarlo, J. J. (2014). Performance-optimized hierarchical models predict neural responses in higher visual cortex. Proceedings of the National Academy of Sciences of the United States of America, 111(23), 8619–8624. https://doi.org/10.1073/pnas.1403112111
- Yang, L., Yang, G., Bing, Z., Tian, Y., Niu, Y., Huang, L., & Yang, L. (2021). Transformer-based generative model accelerating the development of novel BRAF inhibitors. ACS Omega, 6(49), 33864–33873. https://doi.org/10.1021/acsomega.1c05145
- Yarkoni, T. (2020). The generalizability crisis. The Behavioral and Brain Sciences, 45, 1–78. https://doi.org/10.1017/S0140525X20001685
- Yu, H., Xie, T., Paszczyñski, S., & Wilamowski, B. M. (2011). Advantages of radial basis function networks for dynamic system design. IEEE Transactions on Industrial Electronics, 58(12), 5438–5450. https://doi.org/10.1109/TIE.2011.2164773