From ab93a0f3192b3053e6e7a62a8e5958a675500f02 Mon Sep 17 00:00:00 2001 From: Justus Bogner Date: Thu, 14 Dec 2023 13:55:01 +0100 Subject: [PATCH] Fixed two tactics not displaying the image properly --- .../2023-12-13-reduce-number-of-data-features.md | 2 +- .../2023-12-13-remove-redundant-data.md | 4 ++-- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/_posts/green-ml-enabled-systems/2023-12-13-reduce-number-of-data-features.md b/docs/_posts/green-ml-enabled-systems/2023-12-13-reduce-number-of-data-features.md index 86fb3e2..55ef1de 100644 --- a/docs/_posts/green-ml-enabled-systems/2023-12-13-reduce-number-of-data-features.md +++ b/docs/_posts/green-ml-enabled-systems/2023-12-13-reduce-number-of-data-features.md @@ -21,5 +21,5 @@ t-relatedQA: "Accuracy, Data Representativeness" t-measuredimpact: "Reducing number of input features can result in a reduction of energy consumption while still maintaining accuracy." t-source: "Roberto Verdecchia, Luis Cruz, June Sallou, Michelle Lin, James Wickenden, and Estelle Hotellier. 2022. Data-Centric Green AI: An Exploratory Empirical Study. (2022). In 2022 International Conference on ICT for Sustainability (ICT4S). IEEE, 35–45" t-source-doi: "https://doi.org/10.1002/widm.1507" -T-diagram: "reduce-number-of-data-features.png" +t-diagram: "reduce-number-of-data-features.png" --- diff --git a/docs/_posts/green-ml-enabled-systems/2023-12-13-remove-redundant-data.md b/docs/_posts/green-ml-enabled-systems/2023-12-13-remove-redundant-data.md index c88aa0f..05d589f 100644 --- a/docs/_posts/green-ml-enabled-systems/2023-12-13-remove-redundant-data.md +++ b/docs/_posts/green-ml-enabled-systems/2023-12-13-remove-redundant-data.md @@ -1,7 +1,7 @@ --- layout: tactic -title: "Remove redundant data" +title: "Remove Redundant Data" tags: machine-learning data-centric measured t-sort: "Awesome Tactic" t-type: "Architectural Tactic" @@ -16,6 +16,6 @@ t-targetQA: "Energy Efficiency" t-relatedQA: "Accuracy, Data Representativeness" t-measuredimpact: "Removing redundant data from the dataset leads to a smaller input data that further decreases computation, computational time, energy consumption, and memory space" t-source: "Priyadarshan Dhabe, Param Mirani, Rahul Chugwani, and Sadanand Gandewar. 2021. Data Set Reduction to Improve Computing Efficiency and Energy Consumption in Healthcare Domain. In Digital Literacy and Socio-Cultural Acceptance of ICT in Developing Countries. Springer, 53–64. [DOI](https://doi.org/10.1007/978-3-030-61089-0_4); Phyllis Ang, Bhuwan Dhingra, and Lisa Wu Wills. 2022. Characterizing the Efficiency vs. Accuracy Trade-off for Long-Context NLP Models. In Proceedings of NLP Power! The First Workshop on Efficient Benchmarking in NLP. Association for Computational Linguistics, Dublin, Ireland, 113–121. [DOI](https://aclanthology.org/2022.nlppower-1.12)" -T-diagram: "remove-redundant-data.png" +t-diagram: "remove-redundant-data.png" t-source-doi: ---