| 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 | 622 | 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, 1, 2022 | 298 | 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 | 166 | 2022 |
| 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 | 150 | 2024 |
| 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 | 143 | 2024 |
| 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 | 116 | 2022 |
| Algorithmic progress in language models A Ho, T Besiroglu, E Erdil, D Owen, R Rahman, ZC Guo, D Atkinson, ... Advances in Neural Information Processing Systems 37, 58245-58283, 2024 | 63 | 2024 |
| Forecasting timelines of quantum computing J Sevilla, CJ Riedel arXiv preprint arXiv:2009.05045, 2020 | 56 | 2020 |
| Training compute of frontier ai models grows by 4–5x per year J Sevilla, E Roldán Epoch AI, May 28, 2024 | 35 | 2024 |
| Can ai scaling continue through 2030? J Sevilla, T Besiroglu, B Cottier, J You, E Roldán, P Villaloboa, E Erdil Analecta 2023, 1, 2024 | 32 | 2024 |
| Estimating training compute of deep learning models J Sevilla, L Heim, M Hobbhahn, T Besiroglu, A Ho, P Villalobos Epoch, January 20, 2022 | 31 | 2022 |
| Efectos de la musicoterapia sobre la ansiedad generada durante la atención dental, en las mujeres embarazadas en el Servicio de Estomatología del Instituto Nacional de … MVGB Cuesta, RMD Romero, JL Sevilla, JS Sotres, EP Romero, ... Revista ADM Órgano Oficial de la Asociación Dental Mexicana 61 (2), 59-64, 2004 | 30 | 2004 |
| Key trends and figures in Machine Learning Epoch https://epochai.org/trends, 2023 | 29 | 2023 |
| What’s the backward-forward flop ratio for neural networks? M Hobbhahn, J Sevilla Published online at epochai. org, 2021 | 25 | 2021 |
| 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 | 24 | 2019 |
| Will we run out of data? Limits of LLM scaling based on human-generated data. arXiv P Villalobos, A Ho, J Sevilla, T Besiroglu, L Heim, M Hobbhahn arXiv preprint arXiv:2211.04325, 2024 | 21 | 2024 |
| 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 …, 2022 | 17 | 2022 |
| Will we run out of data? limits of llm scaling based on human-generated data, 2024 P Villalobos, A Ho, J Sevilla, T Besiroglu, L Heim, M Hobbhahn URL https://arxiv. org/abs/2211.04325 138, 0 | 16 | |
| Parameter counts in machine learning J Sevilla, P Villalobos, J Cerón AI Alignment Forum, 2021 | 15 | 2021 |
| 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 | 13 | 2022 |
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