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In recent years, machine learning techniques utilizing large-scale datasets have achieved remarkable performance.
Energy and policy considerations for deep learning in NLP
Strubell, E., Ganesh, A., and McCallum, A · 1906
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Neuronlike adaptive elements that can solve difficult learning control problems
Barto, A. G., Sutton, R. S., and Anderson, C. W · 1983
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Calculating the carbon footprint of a google search
Sterling, G · 2009
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MNIST handwritten digit database
LeCun, Y. and Cortes, C · 2010
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Differentially private empirical risk minimization
Chaudhuri, K., Monteleoni, C., and Sarwate, A. D · 2011
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The algorithmic foundations of differential privacy
Dwork, C. and Roth, A · 2014
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Deep learning with differential privacy
Abadi, M., Chu, A., Goodfellow, I., McMahan, H. B., Mironov, I., Talwar, K., and Zhang, L · 2016
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Adversarial machine learning at scale
Kurakin, A., Goodfellow, I., and Bengio, S · 2016
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Distributional reinforcement learning with quantile regression, 2017
Dabney, W., Rowland, M., Bellemare, M. G., and Munos, R · 2017
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Communication-efficient learning of deep networks from decentralized data, 2017
McMahan, H. B., Moore, E., Ramage, D., Hampson, S., and y Arcas, B. A · 2017
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Privacy at scale: Local differential privacy in practice
Cormode, G., Jha, S., Kulkarni, T., Li, N., Srivastava, D., and Wang, T · 2018
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General data protection regulation — protection of personal data in an organisation
Skendžić, A., Kovačić, B., and Tijan, E · 2018
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Energy usage reports: Environmental awareness as part of algorithmic accountability
Lottick, K., Susai, S., Friedler, S. A., and Wilson, J. P · 2019
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Privacy-preserving q-learning with functional noise in continuous state spaces, 2019
Wang, B. and Hegde, N · 2019
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Ag news classification, 2020
Anand, A · 2020
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Deep learning’s carbon emissions problem
Toews, R · 2020
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One (vehicle efficiency) table to rule them all
Bandivadekar, A · 2021
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A first look into the carbon footprint of federated learning
Qiu, X., Parcollet, T., Fernández-Marqués, J., de Gusmão, P. P. B., Beutel, D. J., Topal, T., Mathur, A., and Lane, N. D · 2021
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Devlin, J., Chang, M.-W., Lee, K., and Toutanova, K · 2019
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Quantifying the carbon emissions of machine learning
Lacoste, A., Luccioni, A., Schmidt, V., and Dandres, T · 2019
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CodeCarbon: Estimate and Track Carbon Emissions from Machine Learning Computing
Schmidt, V., Goyal, K., Joshi, A., Feld, B., Conell, L., Laskaris, N., Blank, D., Wilson, J., Friedler, S., and Luccioni, S · 2021
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