Rolnick, David

Aligning artificial intelligence with climate change mitigation

Kaack, L. H., Donti, P. L., Strubell, E., Kamiya, G., Creutzig, F., & Rolnick, D. (2022) · Nature Climate Change

Green IT and SustainabilityClimate Change Policy and Economics

Cited by 570

Experience Replay for Continual Learning

Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T. P., & Wayne, G. (2018) · arXiv

Reinforcement Learning in RoboticsDomain Adaptation and Few-Shot Learning

Cited by 529

Deep Learning is Robust to Massive Label Noise

Rolnick, D., Veit, A., Belongie, S., & Shavit, N. (2017) · arXiv

Machine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

Cited by 402

Why Does Deep and Cheap Learning Work So Well?

Lin, H. W., Tegmark, M., & Rolnick, D. (2017) · Journal of Statistical Physics

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 393

Tackling climate change with machine learning

Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys

Climate Change and Environmental ImpactArtificial Intelligence Applications

Cited by 336

Digitalization and the Anthropocene

Creutzig, F., Acemoglu, D., Bai, X., Edwards, P. N., Hintz, M. J., Kaack, L. H., Kilkis, S., Kunkel, S., Luers, A., Milojevic-Dupont, N., et al. (2022) · Annual Review of Environment and Resources

Green IT and SustainabilityInnovation, Sustainability, Human-Machine Systems

Cited by 112

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries

Tseng, G., Cartuyvels, R., Zvonkov, I., Purohit, M., Rolnick, D., & Kerner, H. (2023) · arXiv

Remote-Sensing Image ClassificationRemote Sensing in Agriculture

Cited by 37