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From an environmental standpoint, there are a few crucial aspects of training a neural network that have a major impact on the quantity of carbon that it emits.
2006 IPCC guidelines for national greenhouse gas inventories
Simon Eggleston, Leandro Buendia, Kyoko Miwa, Todd Ngara, and Kiyoto Tanabe · 2006
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Electricity-specific emission factors for grid electricity
Matthew Brander, Aman Sood, Charlotte Wylie, Amy Haughton, and Jessica Lovell · 2011
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Computational physics on graphics processing units
Ari Harju, Topi Siro, Filippo Federici Canova, Samuli Hakala, and Teemu Rantalaiho · 2012
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Machine learning applications for data center optimization, 2014
Jim Gao · 2014
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2014
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Food image recognition using deep convolutional network with pre-training and fine-tuning
Keiji Yanai and Yoshiyuki Kawano · 2015
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Convolutional neural networks for medical image analysis: Full training or fine tuning?
Nima Tajbakhsh, Jae Y Shin, Suryakanth R Gurudu, R Todd Hurst, Christopher B Kendall, Michael B Gotway, and Jianming Liang · 2016
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2016
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Ghg emissions from electricity consumption: A case study of hong kong from 2002 to 2015 and trends to 2030
WM To and Peter KC Lee · 2017
Cited alongside, same era.
Neuralpower: Predict and deploy energy-efficient convolutional neural networks
Ermao Cai, Da-Cheng Juan, Dimitrios Stamoulis, and Diana Marculescu · 2017
Cited alongside, same era.
Universal language model fine-tuning for text classification
Jeremy Howard and Sebastian Ruder · 2018
Cited alongside, same era.
Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
Google environmental report 2018, 2018
Google · 2018
Cited alongside, same era.
Tearing apart google’s tpu 3.0 ai coprocessor
Paul Teich · 2018
Later among the works it cites.
Energy and policy considerations for deep learning in nlp
Emma Strubell, Ananya Ganesh, and Andrew McCallum · 2019
Closest in time.
Roy Schwartz, Jesse Dodge, Noah A Smith, and Oren Etzioni · 2019
Closest in time.
Hyperparameter optimization
Matthias Feurer and Frank Hutter · 2019
Closest in time.
Aws & sustainability, 2019
Amazon Web Services · 2019
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https://www.google.com/about/datacenters/efficiency/internal/ , 2019
Google Data Centers efficiency: How we do it · 2019
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Beyond carbon neutral. white paper, 2018
Microsoft · 2018
Cited alongside, same era.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Cited alongside, same era.
Massively parallel hyperparameter tuning
Liam Li, Kevin Jamieson, Afshin Rostamizadeh, Ekaterina Gonina, Moritz Hardt, Benjamin Recht, and Ameet Talwalkar · 2018
Cited alongside, same era.
Bohb: Robust and efficient hyperparameter optimization at scale
Stefan Falkner, Aaron Klein, and Frank Hutter · 2018
Cited alongside, same era.
Tackling climate change with machine learning
David Rolnick, Priya L Donti, Lynn H Kaack, Kelly Kochanski, Alexandre Lacoste, Kris Sankaran, Andrew Slavin Ross, Nikola Milojevic-Dupont, Natasha Jaques, Anna Waldman-Brown, et al · 2019
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Visualizing the consequences of climate change using cycle-consistent adversarial networks
Victor Schmidt, Alexandra Luccioni, S. Karthik Mukkavilli, Narmada Balasooriya, Kris Sankaran, Jennifer Chayes, and Yoshua Bengio · 2019
Closest in time.