| Compute trends across three eras of machine learning J Sevilla, L Heim, A Ho, T Besiroglu, M Hobbhahn, P Villalobos 2022 International Joint Conference on Neural Networks (IJCNN), 1-8, 2022 | 381 | 2022 |
| Will we run out of data? an analysis of the limits of scaling datasets in machine learning P Villalobos, J Sevilla, L Heim, T Besiroglu, M Hobbhahn, A Ho arXiv preprint arXiv:2211.04325 1, 2022 | 172 | 2022 |
| Machine learning model sizes and the parameter gap P Villalobos, J Sevilla, T Besiroglu, L Heim, A Ho, M Hobbhahn arXiv preprint arXiv:2207.02852, 2022 | 76 | 2022 |
| Will we run out of data? Limits of LLM scaling based on human-generated data P Villalobos, A Ho, J Sevilla, T Besiroglu, L Heim, M Hobbhahn arXiv preprint arXiv:2211.04325, 2022 | 59 | 2022 |
| Forecasting timelines of quantum computing J Sevilla, CJ Riedel arXiv preprint arXiv:2009.05045, 2020 | 43 | 2020 |
| Position: Will we run out of data? Limits of LLM scaling based on human-generated data P Villalobos, A Ho, J Sevilla, T Besiroglu, L Heim, M Hobbhahn Forty-first International Conference on Machine Learning, 2024 | 25 | 2024 |
| Algorithmic progress in language models A Ho, T Besiroglu, E Erdil, D Owen, R Rahman, ZC Guo, D Atkinson, ... arXiv preprint arXiv:2403.05812, 2024 | 24 | 2024 |
| CTLearn: Deep learning for gamma-ray astronomy D Nieto, A Brill, Q Feng, TB Humensky, B Kim, T Miener, R Mukherjee, ... arXiv preprint arXiv:1912.09877, 2019 | 21 | 2019 |
| Frontiermath: A benchmark for evaluating advanced mathematical reasoning in ai E Glazer, E Erdil, T Besiroglu, D Chicharro, E Chen, A Gunning, ... arXiv preprint arXiv:2411.04872, 2024 | 20 | 2024 |
| Estimating training compute of deep learning models J Sevilla, L Heim, M Hobbhahn, T Besiroglu, A Ho, P Villalobos Tech. Rep., 2022 | 20 | 2022 |
| Parameter counts in machine learning J Sevilla, P Villalobos, J Cerón AI Alignment Forum, 2021 | 16 | 2021 |
| Parameter, compute and data trends in machine learning J Sevilla, P Villalobos, JF Cerón, M Burtell, L Heim, AB Nanjajjar, A Ho, ... 2022-05-30]. https://docs. google. com/spreadsheets/d/1AAIebj …, 2021 | 15 | 2021 |
| Key trends and figures in Machine Learning Epoch https://epochai.org/trends, 2023 | 14 | 2023 |
| Compute trends across three eras of machine learning. arXiv J Sevilla, L Heim, A Ho, T Besiroglu, M Hobbhahn, P Villalobos arXiv preprint arXiv:2202.05924, 2022 | 11 | 2022 |
| What’s the backward-forward flop ratio for neural networks? M Hobbhahn, J Sevilla Retrieved May 16, 2023, 2021 | 10 | 2021 |
| Can AI scaling continue through 2030 J Sevilla, T Besiroglu, B Cottier, J You, E Roldán Analecta 2023, 1, 2024 | 9 | 2024 |
| Will we run out of data? An analysis of the limits of scaling datasets in Machine Learning, arXiv P Villalobos, J Sevilla, L Heim, T Besiroglu, M Hobbhahn, A Ho arXiv preprint arXiv:2211.04325, 2022 | 9 | 2022 |
| Compute Trends Across Three Eras of Machine Learning.(2022) J Sevilla, L Heim, A Ho, T Besiroglu, M Hobbhahn, P Villalobos URL: https://arxiv. org/abs/2202.05924. doi 10, 2022 | 7 | 2022 |
| Explaining data using causal Bayesian Networks J Sevilla 2nd Workshop on Interactive Natural Language Technology for Explainable …, 2020 | 7 | 2020 |
| Please report your compute J Sevilla, A Ho, T Besiroglu Communications of the ACM 66 (5), 30-32, 2023 | 6 | 2023 |
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