| Carbon emissions and large neural network training D Patterson, J Gonzalez, Q Le, C Liang, LM Munguia, D Rothchild, D So, ... arXiv preprint arXiv:2104.10350, 2021 | 916 | 2021 |
| Fetchsgd: Communication-efficient federated learning with sketching D Rothchild, A Panda, E Ullah, N Ivkin, I Stoica, V Braverman, J Gonzalez, ... International Conference on Machine Learning, 8253-8265, 2020 | 445 | 2020 |
| The carbon footprint of machine learning training will plateau, then shrink D Patterson, J Gonzalez, U Hölzle, Q Le, C Liang, LM Munguia, ... Computer 55 (7), 18-28, 2022 | 348 | 2022 |
| Communication-efficient distributed SGD with sketching N Ivkin, D Rothchild, E Ullah, I Stoica, R Arora Advances in Neural Information Processing Systems 32, 2019 | 238 | 2019 |
| Strongly lensed SNe Ia in the era of LSST: observing cadence for lens discoveries and time-delay measurements S Huber, SH Suyu, UM Noebauer, V Bonvin, D Rothchild, JHH Chan, ... Astronomy & Astrophysics 631, A161, 2019 | 63 | 2019 |
| Squeezewave: Extremely lightweight vocoders for on-device speech synthesis B Zhai, T Gao, F Xue, D Rothchild, B Wu, JE Gonzalez, K Keutzer arXiv preprint arXiv:2001.05685, 2020 | 39 | 2020 |
| C5t5: Controllable generation of organic molecules with transformers D Rothchild, A Tamkin, J Yu, U Misra, J Gonzalez arXiv preprint arXiv:2108.10307, 2021 | 37 | 2021 |
| Carbon emissions and large neural network training. arXiv D Patterson, J Gonzalez, Q Le, C Liang, LM Munguia, D Rothchild, D So, ... arXiv preprint arXiv:2104.10350, 2021 | 35 | 2021 |
| Carbon emissions and large neural network training. arXiv 2021 D Patterson, J Gonzalez, Q Le, C Liang, LM Munguia, D Rothchild, D So, ... arXiv preprint arXiv:2104.10350, 2021 | 30 | 2021 |
| The impact of observing strategy on cosmological constraints with LSST M Lochner, D Scolnic, H Almoubayyed, T Anguita, H Awan, E Gawiser, ... The Astrophysical Journal Supplement Series 259 (2), 58, 2022 | 29 | 2022 |
| Optimizing the lsst observing strategy for dark energy science: Desc recommendations for the wide-fast-deep survey M Lochner, DM Scolnic, H Awan, N Regnault, P Gris, R Mandelbaum, ... arXiv preprint arXiv:1812.00515, 2018 | 11 | 2018 |
| Optimizing the LSST observing strategy for dark energy science: DESC recommendations for the deep drilling fields and other special programs DM Scolnic, M Lochner, P Gris, N Regnault, R Hložek, G Aldering, ... arXiv preprint arXiv:1812.00516, 2018 | 6 | 2018 |
| ALTSched: Improved Scheduling for Time-domain Science with LSST D Rothchild, C Stubbs, P Yoachim Publications of the Astronomical Society of the Pacific 131 (1005), 115002, 2019 | 5 | 2019 |
| Copyright Implications of the Use of Code Repositories to Train a Machine Learning Model J Rothchild, D Rothchild Free Software Foundation, 2022 | 4 | 2022 |
| Carbon emissions and large neural network training (pp. 1–22) D Patterson, J Gonzalez, Q Le, C Liang, LM Munguia, D Rothchild, D So, ... arXiv preprint arXiv:2104.10350, 2021 | 4 | 2021 |
| Investigating the behavior of diffusion models for accelerating electronic structure calculations D Rothchild, AS Rosen, E Taw, C Robinson, JE Gonzalez, ... Chemical Science 15 (33), 13506-13522, 2024 | 1 | 2024 |
| Using automated vehicle (av) technology to smooth traffic flow and reduce greenhouse gas emissions S Almatrudi, K Parvate, D Rothchild, U Vijay | 1 | 2022 |
| lsst/rubin_sim: 0.12. 1 P Yoachim, RL Jones, EH Neilsen, T Ribeiro, S Daniel, N Abrams, ... Zenodo, 2022 | 1 | 2022 |
| The LSST Dark Energy Science Collaboration Cadence Note M Lochner, D Scolnic, H Almoubayyed, T Anguita, H Awan, E Gawiser, ... LSST Survey Cadence Notes, 57, 2021 | 1 | 2021 |
| Accelerating Electronic Structure Calculations with Machine Learning D Rothchild University of California, Berkeley, 2023 | | 2023 |
