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Quantifying the Carbon Emissions of Machine Learning
v1v2 (latest)

Quantifying the Carbon Emissions of Machine Learning

21 October 2019
Alexandre Lacoste
A. Luccioni
Victor Schmidt
Thomas Dandres
ArXiv (abs)PDFHTML

Papers citing "Quantifying the Carbon Emissions of Machine Learning"

50 / 441 papers shown
Title
WeedCLR: Weed Contrastive Learning through Visual Representations with
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Alzayat Saleh
A. Olsen
Jake Wood
B. Philippa
M. R. Azghadi
194
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A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for
  Fairer Instruction-Tuned Machine Translation
A Tale of Pronouns: Interpretability Informs Gender Bias Mitigation for Fairer Instruction-Tuned Machine TranslationConference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Giuseppe Attanasio
Flor Miriam Plaza del Arco
Debora Nozza
Anne Lauscher
207
26
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18 Oct 2023
A Carbon Tracking Model for Federated Learning: Impact of Quantization
  and Sparsification
A Carbon Tracking Model for Federated Learning: Impact of Quantization and SparsificationIEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (MDCLN), 2023
Luca Barbieri
S. Savazzi
Sanaz Kianoush
M. Nicoli
Luigi Serio
174
7
0
12 Oct 2023
Pit One Against Many: Leveraging Attention-head Embeddings for
  Parameter-efficient Multi-head Attention
Pit One Against Many: Leveraging Attention-head Embeddings for Parameter-efficient Multi-head AttentionConference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Huiyin Xue
Nikolaos Aletras
277
1
0
11 Oct 2023
Watt For What: Rethinking Deep Learning's Energy-Performance
  Relationship
Watt For What: Rethinking Deep Learning's Energy-Performance Relationship
Shreyank N. Gowda
Xinyue Hao
Gen Li
Laura Sevilla-Lara
Shashank Narayana Gowda
HAI
153
17
0
10 Oct 2023
Rethinking Model Selection and Decoding for Keyphrase Generation with
  Pre-trained Sequence-to-Sequence Models
Rethinking Model Selection and Decoding for Keyphrase Generation with Pre-trained Sequence-to-Sequence ModelsConference on Empirical Methods in Natural Language Processing (EMNLP), 2023
Di Wu
Wasi Uddin Ahmad
Kai-Wei Chang
183
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10 Oct 2023
Climate-sensitive Urban Planning through Optimization of Tree Placements
Climate-sensitive Urban Planning through Optimization of Tree Placements
Simon Schrodi
Ferdinand Briegel
Max Argus
Andreas Christen
Thomas Brox
AI4CE
186
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09 Oct 2023
IDTraffickers: An Authorship Attribution Dataset to link and connect
  Potential Human-Trafficking Operations on Text Escort Advertisements
IDTraffickers: An Authorship Attribution Dataset to link and connect Potential Human-Trafficking Operations on Text Escort AdvertisementsConference on Empirical Methods in Natural Language Processing (EMNLP), 2023
V. Saxena
Benjamin Bashpole
Gijs Van Dijck
Gerasimos Spanakis
233
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0
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Training a Large Video Model on a Single Machine in a Day
Training a Large Video Model on a Single Machine in a Day
Yue Zhao
Philipp Krahenbuhl
VLM
221
22
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Learning the Efficient Frontier
Learning the Efficient FrontierNeural Information Processing Systems (NeurIPS), 2023
Philippe Chatigny
Ivan Sergienko
Ryan Ferguson
Jordan Weir
Maxime Bergeron
122
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LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language
  Models
LLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language ModelsInternational Conference on Learning Representations (ICLR), 2023
Ahmad Faiz
S. Kaneda
Ruhan Wang
Rita Osi
Parteek Sharma
Fan Chen
Lei Jiang
256
104
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S3TC: Spiking Separated Spatial and Temporal Convolutions with
  Unsupervised STDP-based Learning for Action Recognition
S3TC: Spiking Separated Spatial and Temporal Convolutions with Unsupervised STDP-based Learning for Action RecognitionInternational Conference on Pattern Recognition (ICPR), 2023
Mireille el Assal
Pierre Tirilly
Ioan Marius Bilasco
160
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Explaining Speech Classification Models via Word-Level Audio Segments
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Eliana Pastor
Alkis Koudounas
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EarthPT: a time series foundation model for Earth Observation
Michael J. Smith
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James E. Geach
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201
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Efficiency is Not Enough: A Critical Perspective of Environmentally Sustainable AI
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Studying the impacts of pre-training using ChatGPT-generated text on
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Energy Concerns with HPC Systems and Applications
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Youssef Mesri
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Training BERT Models to Carry Over a Coding System Developed on One
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Dalma Galambos
Pál Zsámboki
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Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature
  Extraction Techniques, Ensembling, and Deep Learning Models
Efficient Sentiment Analysis: A Resource-Aware Evaluation of Feature Extraction Techniques, Ensembling, and Deep Learning ModelsInternational Workshop on Natural Language Processing for Social Media (SocialNLP), 2023
M. Kamruzzaman
Gene Louis Kim
103
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ALE: A Simulation-Based Active Learning Evaluation Framework for the
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197
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Prot2Text: Multimodal Protein's Function Generation with GNNs and
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GUIDO: A Hybrid Approach to Guideline Discovery & Ordering from Natural
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Nils Freyer
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Matthias Meinecke
58
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Impatient Bandits: Optimizing Recommendations for the Long-Term Without
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Thomas M. McDonald
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Efficiency Pentathlon: A Standardized Arena for Efficiency Evaluation
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Hao Peng
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142
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Prawn Morphometrics and Weight Estimation from Images using Deep
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EnergAt: Fine-Grained Energy Attribution for Multi-Tenancy
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An Exploratory Literature Study on Sharing and Energy Use of Language
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Max Hort
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228
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On the Constrained Time-Series Generation Problem
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Andrea Coletta
Sriram Gopalakrishnan
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AdAM: Few-Shot Image Generation via Adaptation-Aware Kernel Modulation
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Abdollahzadeh Milad
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359
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Theater Aid System for the Visually Impaired Through Transfer Learning
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Theater Aid System for the Visually Impaired Through Transfer Learning of Spatio-Temporal Graph Convolution Networks
Leyla Benhamida
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CamemBERT-bio: Leveraging Continual Pre-training for Cost-Effective
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Survey of Trustworthy AI: A Meta Decision of AI
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