Quantifying the carbon emissions of machine learning A Lacoste, A Luccioni, V Schmidt, T Dandres arXiv preprint arXiv:1910.09700, 2019 | 661 | 2019 |
CodeCarbon: estimate and track carbon emissions from machine learning computing V Schmidt, K Goyal, A Joshi, B Feld, L Conell, N Laskaris, D Blank, ... Cited on 20, 2021 | 63 | 2021 |
Visualizing the consequences of climate change using cycle-consistent adversarial networks V Schmidt, A Luccioni, SK Mukkavilli, N Balasooriya, K Sankaran, ... arXiv preprint arXiv:1905.03709, 2019 | 50 | 2019 |
Using artificial intelligence to visualize the impacts of climate change A Luccioni, V Schmidt, V Vardanyan, Y Bengio IEEE Computer Graphics and Applications 41 (1), 8-14, 2021 | 31 | 2021 |
Faenet: Frame averaging equivariant gnn for materials modeling AA Duval, V Schmidt, A Hernández-Garcıa, S Miret, FD Malliaros, ... International Conference on Machine Learning, 9013-9033, 2023 | 28 | 2023 |
Quantifying the carbon emissions of machine learning. arXiv A Lacoste, A Luccioni, V Schmidt, T Dandres arXiv preprint arXiv:1910.09700, 2019 | 24 | 2019 |
A Hitchhiker's Guide to Geometric GNNs for 3D Atomic Systems A Duval, SV Mathis, CK Joshi, V Schmidt, S Miret, FD Malliaros, T Cohen, ... arXiv preprint arXiv:2312.07511, 2023 | 21 | 2023 |
Predicting infectiousness for proactive contact tracing Y Bengio, P Gupta, T Maharaj, N Rahaman, M Weiss, T Deleu, E Muller, ... arXiv preprint arXiv:2010.12536, 2020 | 21 | 2020 |
Quantifying the carbon emissions of machine learning. arXiv 2019 A Lacoste, A Luccioni, V Schmidt, T Dandres arXiv preprint arXiv:1910.09700, 2019 | 19 | 2019 |
Estimating carbon emissions of artificial intelligence [opinion] A Luccioni, A Lacoste, V Schmidt IEEE Technology and Society Magazine 39 (2), 48-51, 2020 | 18 | 2020 |
CodeCarbon: estimate and track carbon emissions from machine learning computing (2021) V Schmidt, K Goyal, A Joshi, B Feld, L Conell, N Laskaris, D Blank, ... DOI: https://doi. org/10.5281/zenodo 4658424, 2021 | 17 | 2021 |
Handreiking schoolexamen informatica havo/vwo V Schmidt SLO, Enschede, 2006 | 15 | 2006 |
Climategan: Raising climate change awareness by generating images of floods V Schmidt, AS Luccioni, M Teng, T Zhang, A Reynaud, S Raghupathi, ... arXiv preprint arXiv:2110.02871, 2021 | 14 | 2021 |
Quantifying the carbon emissions of machine learning S Luccioni, V Schmidt, A Lacoste, T Dandres NeurIPS 2019 Workshop on Tackling Climate Change with Machine Learning, 2019 | 13 | 2019 |
COVI-AgentSim: an agent-based model for evaluating methods of digital contact tracing P Gupta, T Maharaj, M Weiss, N Rahaman, H Alsdurf, A Sharma, ... arXiv preprint arXiv:2010.16004, 2020 | 11 | 2020 |
Using simulated data to generate images of climate change G Cosne, A Juraver, M Teng, V Schmidt, V Vardanyan, A Luccioni, ... arXiv preprint arXiv:2001.09531, 2020 | 8 | 2020 |
Crystal-gfn: sampling crystals with desirable properties and constraints M AI4Science, A Hernandez-Garcia, A Duval, A Volokhova, Y Bengio, ... arXiv preprint arXiv:2310.04925, 2023 | 6 | 2023 |
PhAST: Physics-aware, scalable, and task-specific GNNs for accelerated catalyst design A Duval, V Schmidt, S Miret, Y Bengio, A Hernández-García, D Rolnick | 6 | 2023 |
Modeling cloud reflectance fields using conditional generative adversarial networks V Schmidt, M Alghali, K Sankaran, T Yuan, Y Bengio arXiv preprint arXiv:2002.07579, 2020 | 6 | 2020 |
torchgfn: A pytorch gflownet library S Lahlou, JD Viviano, V Schmidt, Y Bengio arXiv preprint arXiv:2305.14594, 2023 | 5 | 2023 |