Energy and Policy Considerations for Modern Deep Learning Research

Attention Is All You Need

Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018) · arXiv · doi:10.48550/arxiv.1810.04805 · Cited by 73,173

Xception: Deep Learning with Depthwise Separable Convolutions

Chollet, F. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.195 · Cited by 20,380

Deep Contextualized Word Representations

Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983

Mastering the game of Go without human knowledge

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., et al. (2017) · Nature · doi:10.1038/nature24270 · Cited by 9,084

Large Scale GAN Training for High Fidelity Natural Image Synthesis

Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717

Energy and Policy Considerations for Deep Learning in NLP

Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684

Linguistically-Informed Self-Attention for Semantic Role Labeling

Strubell, E., Verga, P. W., Andor, D., Weiss, D., & McCallum, A. (2018) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/d18-1548 · Cited by 426

Quantifying the Carbon Emissions of Machine Learning

Lacoste, A., Luccioni, A., Schmidt, V., & Dandres, T. (2019) · arXiv · doi:10.48550/arxiv.1910.09700 · Cited by 408

Massively Multilingual Neural Machine Translation

Aharoni, R., Johnson, M., & Firat, O. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1388 · Cited by 402

Exploring the Role of Robots and Artificial Intelligence in Advancing Renewable Energy Consumption

Badareu, G., Doran, M. D., Firu, M. A., Croitoru, I. M., & Doran, N. M. (2024) · Energies · doi:10.3390/en17174474 · Cited by 23

The Future of Digital Twin Research and Development

Bossuyt, D. L. V., Allaire, D., Bickford, J. F., Bozada, T. A., Chen, W., Cutitta, R. P., Cuzner, R. M., Fletcher, K. M., Giachetti, R. E., Hale, B., et al. (2025) · Journal of Computing and Information Science in Engineering · doi:10.1115/1.4068082 · Cited by 27

Dissecting FLOPs Along Input Dimensions for GreenAI Cost Estimations

Asperti, A., Evangelista, D., & Marzolla, M. (2022) · Lecture notes in computer science · doi:10.1007/978-3-030-95470-3_7 · Cited by 23

Optimization Schemes for In-Memory Linear Regression Circuit With Memristor Arrays

Wang, S., Sun, Z., Liu, Y., Bao, S., Cai, Y., Ielmini, D., & Huang, R. (2021) · IEEE Transactions on Circuits and Systems I Regular Papers · doi:10.1109/tcsi.2021.3122327 · Cited by 23

Energy costs of communicating with AI

Dauner, M., & Socher, G. (2025) · Frontiers in Communication · doi:10.3389/fcomm.2025.1572947 · Cited by 25

Can artificial intelligence mitigate environmental inequality? Evidence from leading robotic-driven economies using quantile-based methods

Bergougui, B. (2025) · Borsa Istanbul Review · doi:10.1016/j.bir.2025.07.012 · Cited by 25

Comparison of Pretraining Models and Strategies for Health-Related Social Media Text Classification

Guo, Y., Ge, Y., Yang, Y., Al-Garadi, M. A., & Sarker, A. (2022) · Healthcare · doi:10.3390/healthcare10081478 · Cited by 22

Acquisitions with random shim values enhance AI-driven NMR shimming

Becker, M., Lehmkuhl, S., Kesselheim, S., Korvink, J. G., & Jouda, M. (2022) · Journal of Magnetic Resonance · doi:10.1016/j.jmr.2022.107323 · Cited by 21

Neuromorphic Sentiment Analysis Using Spiking Neural Networks

Chunduri, R. K., & Perera, D. G. (2023) · Sensors · doi:10.3390/s23187701 · Cited by 22

Sustainable AI RegulationPreprint

Hacker, P. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4467684 · Cited by 22

Assessing the impact of artificial intelligence on the transition to renewable energy? Analysis of U.S. states under policy uncertainty

Fang, Y., Lee, C., & Li, X. (2025) · Renewable Energy · doi:10.1016/j.renene.2025.122969 · Cited by 23

Edge intelligence unleashed: a survey on deploying large language models in resource-constrained environments

Semerikov, S. O., Вакалюк, Т. А., Каневська, О. Б., Остроушко, О. А., & Kolhatin, A. O. (2025) · Journal of Edge Computing · doi:10.55056/jec.1000 · Cited by 25

Correcting Diacritics and Typos with a ByT5 Transformer Model

Stankevičius, L., Lukoševičius, M., Kapočiūtė-Dzikienė, J., Briedienė, M., & Krilavičius, T. (2022) · Applied Sciences · doi:10.3390/app12052636 · Cited by 27

A long road ahead: a review of the state of knowledge of the environmental effects of digitization

Roussilhe, G., Ligozat, A., & Quinton, S. (2023) · Current Opinion in Environmental Sustainability · doi:10.1016/j.cosust.2023.101296 · Cited by 23

Artificial intelligence for optimizing benefits and minimizing risks of pharmacological therapies: challenges and opportunities

Crisafulli, S., Ciccimarra, F., Bellitto, C., Carollo, M., Carrara, E., Stagi, L., Triola, R., Capuano, A., Chiamulera, C., Moretti, U., et al. (2024) · Frontiers in Drug Safety and Regulation · doi:10.3389/fdsfr.2024.1356405 · Cited by 29

Hyperdimensional computing: A fast, robust, and interpretable paradigm for biological data

Stock, M., Criekinge, W. V., Boeckaerts, D., Taelman, S., Haeverbeke, M. V., Dewulf, P., & Bernard De Baets, C. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012426 · Cited by 25

Reproducible machine learning research in mental workload classification using EEG

Demirezen, G., Temizel, T. T., & Brouwer, A.-M. (2024) · Frontiers in Neuroergonomics · doi:10.3389/fnrgo.2024.1346794 · Cited by 27

Benchmarking Emerging Deep Learning Quantization Methods for Energy Efficiency

Rajput, S., & Sharma, T. (2024) · IEEE International Conference on Software Architecture Companion (ICSA-C) · doi:10.1109/icsa-c63560.2024.00049 · Cited by 23

The potential role of small modular reactors (SMRs) in addressing the increasing power demand of the artificial intelligence industry: A scenario-based analysis

Cha, S. (2024) · Nuclear Engineering and Technology · doi:10.1016/j.net.2024.11.016 · Cited by 23

Generative AI: Current Status and Future Directions

Leong, L.-Y., Hew, T., Ooi, K., Tan, G., & Koohang, A. (2025) · Journal of Computational Information Systems · doi:10.1080/08874417.2025.2482571 · Cited by 18

Smart technology for public health: reshaping the future of food safety

Liberty, J. T., Bromage, S., Peter, E., Ihedioha, O. C., Alsalman, F. B., & Odogwu, T. S. (2025) · Food Control · doi:10.1016/j.foodcont.2025.111378 · Cited by 26

Energy Efficient or Exhaustive? Benchmarking Power Consumption of LLM Inference Engines

Niu, C., Zhang, W., Zhao, Y., & Chen, Y. (2025) · ACM SIGEnergy Energy Informatics Review · doi:10.1145/3757892.3757900 · Cited by 33

Mitigating carbon footprint for knowledge distillation based deep learning model compression

Rafat, K., Islam, S., Mahfug, A. A., Hossain, M. I., Rahman, F., Momen, S., Rahman, S., & Mohammed, N. (2023) · PLoS ONE · doi:10.1371/journal.pone.0285668 · Cited by 24

Carburacy: Summarization Models Tuning and Comparison in Eco-Sustainable Regimes with a Novel Carbon-Aware Accuracy

Moro, G., Ragazzi, L., & Valgimigli, L. (2023) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v37i12.26686 · Cited by 21

Timeshifting strategies for carbon-efficient long-running large language model training

Jagannadharao, A., Beckage, N., Nafus, D., & Chamberlin, S. (2023) · Innovations in Systems and Software Engineering · doi:10.1007/s11334-023-00546-x · Cited by 22

Making AI Ethical by Design: The UNESCO Perspective

Ramos, G., Squicciarini, M., & Lamm, E. (2024) · Computer · doi:10.1109/mc.2023.3325949 · Cited by 24

Challenges and Opportunities of Using Transformer-Based Multi-Task Learning in NLP Through ML Lifecycle: A Position Paper

Torbarina, L., Ferkovic, T., Roguski, Ł., Mihelčić, V., Šarlija, B., & Kraljević, Ž. (2024) · Natural Language Processing Journal · doi:10.1016/j.nlp.2024.100076 · Cited by 29

Carbon capture, utilization and storage in buildings: Analysis of performance, social acceptance, policy measures, and the role of artificial intelligence

Elaouzy, Y., & Zaabout, A. (2025) · Building and Environment · doi:10.1016/j.buildenv.2025.112817 · Cited by 24

Training Dynamical Binary Neural Networks with Equilibrium Propagation

Laydevant, J., Ernoult, M., Querlioz, D., & Grollier, J. (2021) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw53098.2021.00522 · Cited by 23

Evaluating Coupling Models for Cloud Datacenters and Power Grids

Lin, L., Zavala, V. M., & Chien, A. A. (2021) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3447555.3464868 · Cited by 19

Performance and Efficiency Evaluation of ASR Inference on the Edge

Gondi, S., & Pratap, V. (2021) · Sustainability · doi:10.3390/su132212392 · Cited by 24

Twins or False Friends? A Study on Energy Consumption and Performance of Configurable Software

Weber, M., Kaltenecker, C., Sattler, F., Apel, S., & Siegmund, N. (2023) · ACM/IEEE International Conference on Software Engineering: Companion Proceedings (ICSE-Companion) · doi:10.1109/icse48619.2023.00177 · Cited by 19

Open Source AI-based SE Tools: Opportunities and Challenges of Collaborative Software Learning

Lin, Z., Ma, W., Lin, T., Zheng, Y., Ge, J., Wang, J., Klein, J., Bissyandé, T. F., Liu, Y., & Li, L. (2024) · ACM Transactions on Software Engineering and Methodology · doi:10.1145/3708529 · Cited by 26

Tucano: Advancing neural text generation for Portuguese

Corrêa, N. K., Sen, A., Falk, S., & Fatimah, S. (2025) · Patterns · doi:10.1016/j.patter.2025.101325 · Cited by 36

Large Language Models for Psychological Assessment: A Comprehensive Overview

Brickman, J., Gupta, M., & Oltmanns, J. R. (2025) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459251343582 · Cited by 24

Deep Learning for Identification of Alcohol-Related Content on Social Media (Reddit and Twitter): Exploratory Analysis of Alcohol-Related Outcomes

Ricard, B. J., & Hassanpour, S. (2021) · Journal of Medical Internet Research · doi:10.2196/27314 · Cited by 20

Machine learning-based surrogate modelling for Urban Water Networks: Review and future research directionsPreprint

Garzón, A., Kapelan, Z. S., Langeveld, J., & Taormina, R. (2022) · doi:10.1002/essoar.10509791.2 · Cited by 18

“Democratizing AI” and the Concern of Algorithmic Injustice

Lin, T.-A. (2024) · Philosophy & Technology · doi:10.1007/s13347-024-00792-2 · Cited by 22

Intelligent data analysis in edge computing with large language models: applications, challenges, and future directions

Wang, X., Xu, Z., & Sui, X. (2025) · Frontiers in Computer Science · doi:10.3389/fcomp.2025.1538277 · Cited by 24

Scalable photonic reservoir computing for parallel machine learning tasks

Aadhi, A. R., Lauro, L. D., Fischer, B., Dmitriev, P., Alamgir, I., Mazoukh, C., Perron, N., Viktorov, E. A., Kovalev, A. V., Eshaghi, A., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-67983-z · Cited by 28

Do We Need Exotic Models? Engineering Metrics to Enable Green Machine Learning from Tackling Accuracy-Energy Trade-offs

Naser, M. Z. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.135334 · Cited by 17

Integrating Blockchain and Deep Learning Into Extremely Resource-Constrained IoT: An Energy-Saving Zero-Knowledge PoL Approach

Zhang, H., Wu, J., Lin, X., Bashir, A. K., & Al‐Otaibi, Y. D. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3280069 · Cited by 18

An Integrative Theoretical Framework for Responsible Artificial Intelligence

Haidar, A. (2023) · International Journal of Digital Strategy Governance and Business Transformation · doi:10.4018/ijdsgbt.334844 · Cited by 23

AI as a Sport: On the Competitive Epistemologies of Benchmarking

Orr, W., & Kang, E. B. (2024) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3630106.3659012 · Cited by 35

Long short-term cognitive networks

Nápoles, G., Grau, I., Jastrzębska, A., & Salgueiro, Y. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-07348-5 · Cited by 20

How TinyML Can be Leveraged to Solve Environmental Problems: A Survey

Bamoumen, H., Temouden, A., Benamar, N., & Chtouki, Y. (2022) · International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies (3ICT) · doi:10.1109/3ict56508.2022.9990661 · Cited by 16

Conceptualizing the Secure Machine Learning Operations (SecMLOps) Paradigm

Zhang, X., & Jaskolka, J. (2022) · IEEE International Conference on Software Quality, Reliability and Security (QRS) · doi:10.1109/qrs57517.2022.00023 · Cited by 30

Jack and Masters of all Trades: One-Pass Learning Sets of Model Sets From Large Pre-Trained Models

Choong, H. X., Ong, Y.-S., Gupta, A., Chen, C., & Lim, R. (2023) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2023.3277769 · Cited by 16

Carbon Containers

Thiede, J., Bashir, N., Irwin, D., & Shenoy, P. J. (2023) · ACM Symposium on Cloud Computing (SoCC) · doi:10.1145/3620678.3624644 · Cited by 16

Green IoT Event Detection for Carbon-Emission Monitoring in Sensor Networks

Fay, C., Corcoran, B., & Diamond, D. (2023) · Sensors · doi:10.3390/s24010162 · Cited by 17

A deep‐learning framework for enhancing habitat identification based on species composition

Leblanc, C., Bonnet, P., Servajean, M., Chytrý, M., Aćić, S., Argagnon, O., Bergamini, A., Biurrun, I., Bonari, G., Campos, J. A., et al. (2024) · Applied Vegetation Science · doi:10.1111/avsc.12802 · Cited by 16

Efficient greenhouse gas prediction using IoT data streams and a CNN-BiLSTM-KAN model

Zhang, J., & Zhao, L. (2025) · Alexandria Engineering Journal · doi:10.1016/j.aej.2025.01.019 · Cited by 16

Can AI Outsmart Fake News? Detecting Misinformation with AI Models in Real-Time

Gondwe, G. (2025) · Emerging Media · doi:10.1177/27523543251325902 · Cited by 20

Vertical Stacked 48V-1V LEGO-PoL CPU Voltage Regulator with 1A/mm2 Current Density

Elasser, Y., Baek, J.-I., Radhakrishnan, K., Gan, H., Douglas, J., Krishnamurthy, H. K., Li, X., Jiang, S., Sullivan, C. R., & Chen, M. (2022) · Conference proceedings/Conference proceedings - IEEE Applied Power Electronics Conference and Exposition · doi:10.1109/apec43599.2022.9773677 · Cited by 16

ChatGPT’s Impact Across Sectors: A Systematic Review of Key Themes and Challenges

Hussein, H., Gordon, M., Hodgkinson, C., Foreman, R., & Wagad, S. (2025) · Big Data and Cognitive Computing · doi:10.3390/bdcc9030056 · Cited by 32

A Systematic Review and Evaluation of Sustainable AI Algorithms and Techniques in Healthcare

Alzoubi, Y. I., Topcu, A. E., & Α, E. E. (2025) · IEEE Access · doi:10.1109/access.2025.3596189 · Cited by 15

Enhancing Smart and Zero-Carbon Cities Through a Hybrid CNN-LSTM Algorithm for Sustainable AI-Driven Solar Power Forecasting (SAI-SPF)

Elmousalami, H. H., Hui, F. K. P., & Alnaser, A. A. (2025) · Buildings · doi:10.3390/buildings15152785 · Cited by 15

Generative artificial intelligence for construction: Use cases, trends, challenges, and opportunities

Alwashah, Z., Xiao, B., Liu, H., Mueller, S. T., & Shao, X. (2025) · Journal of Building Engineering · doi:10.1016/j.jobe.2025.113802 · Cited by 26

Transformers for tabular data representation

Badaro, G., & Papotti, P. (2022) · Proceedings of the VLDB Endowment · doi:10.14778/3554821.3554890 · Cited by 15

Interface engineering of amorphous gallium oxide crossbar array memristors for neuromorphic computing

Masaoka, N., Hayashi, Y., Tohei, T., & Sakai, A. (2023) · Japanese Journal of Applied Physics · doi:10.35848/1347-4065/acb060 · Cited by 15

GreenPLM: Cross-Lingual Transfer of Monolingual Pre-Trained Language Models at Almost No Cost

Zeng, Q., Garay, L. G., Zhou, P., Chong, D., Hua, Y., Wu, J., Pan, Y., Zhou, H., Voigt, R., & Yang, J. (2023) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2023/698 · Cited by 21

LACS: Learning-Augmented Algorithms for Carbon-Aware Resource Scaling with Uncertain Demand

Bostandoost, R., Lechowicz, A., Hanafy, W. A., Bashir, N., Shenoy, P. J., & Hajiesmaili, M. (2024) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3632775.3661942 · Cited by 16

Implementing the analogous neural network using chaotic strange attractors

Kesgin, B. U., & Teğin, U. (2024) · Communications Engineering · doi:10.1038/s44172-024-00242-z · Cited by 15

Green MLOps to Green GenOps: An Empirical Study of Energy Consumption in Discriminative and Generative AI Operations

Sánchez-Mompó, A., Mavromatis, I., Li, P., Katsaros, K., & Khan, A. (2025) · Information · doi:10.3390/info16040281 · Cited by 15

Yes we care!-Certification for machine learning methods through the care label framework

Morik, K. J., Kotthaus, H., Fischer, R., Mücke, S., Jakobs, M., Piatkowski, N., Pauly, A., Heppe, L., & Heinrich, D. (2022) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2022.975029 · Cited by 15

“Won’t somebody please (actually) think of the children?” AI Ethics for Children: A Scoping Review

Collyer-Hoar, G., & Rubegni, E. (2025) · ACM Journal on Responsible Computing · doi:10.1145/3745031 · Cited by 16

The Cloud Next Door: Investigating the Environmental and Socioeconomic Strain of Datacenters on Local Communities

Ngata, W. M., Bashir, N., Westerlaken, M., Lioté, L., Chandio, Y., & Olivetti, E. (2025) · ACM SIGCAS/SIGCHI Conference on Computing and Sustainable Societies (COMPASS) · doi:10.1145/3715335.3736324 · Cited by 14

Bridging Domains: Advances in Explainable, Automated, and Privacy-Preserving AI for Computer Science and Cybersecurity

Harrath, Y., Adohinzin, O., Kaabi, J., & Saathoff, M. (2025) · Computers · doi:10.3390/computers14090374 · Cited by 18

Peace and Prosperity for the Digital Age? The Colonial Political Economy of European AI Governance

Carmel, E., & Paul, R. (2022) · IEEE Technology and Society Magazine · doi:10.1109/mts.2022.3173340 · Cited by 17

From Algorithm to Module: Adaptive and Energy-Efficient Quantization Method for Edge Artificial Intelligence in IoT Society

Li, T., Ma, Y., & Endoh, T. (2022) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3223222 · Cited by 17

Institutions, Voids, and Dependencies: Tracing the Designs and Robustness of Urban Water Systems

Deslatte, A., Koebele, E. A., Bartels, L., Wiechman, A., Vicario, S. A., Coughlin, C., & Rybolt, D. (2023) · International Review of Public Policy · doi:10.4000/irpp.3455 · Cited by 15

The Impact of AI on Sustainability

Mohammed, A. B., Naim, A., & Sabahath, A. (2024) · Advances in systems analysis, software engineering, and high performance computing book series · doi:10.4018/979-8-3693-1794-5.ch005 · Cited by 15

Photonic neural networks at the edge of spatiotemporal chaos in multimode fibers

Kesgin, B. U., & Teğin, U. (2025) · Nanophotonics · doi:10.1515/nanoph-2024-0593 · Cited by 14

A Unified Framework for Assessing Energy Efficiency of Machine Learning

Fischer, R., Jakobs, M., Mücke, S., & Morik, K. (2023) · Communications in Computer and Information Science · doi:10.1007/978-3-031-23618-1_3 · Cited by 16

Benchmarking Resource Usage for Efficient Distributed Deep Learning

Frey, N. C., Li, B., McDonald, J., Zhao, D., Jones, M. P., Bestor, D., Tiwari, D., Gadepally, V. N., & Samsi, S. (2022) · IEEE High Performance Extreme Computing Conference · doi:10.1109/hpec55821.2022.9926375 · Cited by 14

Data-Efficient Estimation of Remaining Useful Life for Machinery With a Limited Number of Run-to-Failure Training Sequences

Sternharz, G., Elhalwagy, A., & Kalganova, T. (2022) · IEEE Access · doi:10.1109/access.2022.3226780 · Cited by 14

Recent Machine-Learning-Driven Developments in E-Commerce: Current Challenges and Future Perspectives

State Audit Bureau (SAB), K., Al-Ebrahim, M. A., Bunian, S., & Nour, A. A. (2023) · Engineered Science · doi:10.30919/es1044 · Cited by 17

Measuring the Effectiveness of Carbon-Aware AI Training Strategies in Cloud Instances: A Confirmation Study

Vergallo, R., & Mainetti, L. (2024) · Future Internet · doi:10.3390/fi16090334 · Cited by 17

Fine-Tuning Retrieval-Augmented Generation with an Auto-Regressive Language Model for Sentiment Analysis in Financial Reviews

Mathebula, M., Modupe, A., & Marivate, V. N. (2024) · Applied Sciences · doi:10.3390/app142310782 · Cited by 15

Examining the interaction between artificial intelligence literacy and individual entrepreneurial orientation in teacher candidates: The mediating role of sustainable development

Polat, E., Zincirli, M., & ZENGİN, E. (2025) · The International Journal of Management Education · doi:10.1016/j.ijme.2025.101156 · Cited by 17

AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural NetworksPreprint

Peşte, A., Iofinova, E., Vladu, A., & Alistarh, D. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2106.12379 · Cited by 13

Large language models for frontline healthcare support in low-resource settings

Rutunda, S., Williams, G., Kabanda, K., Nkurunziza, F., Uwiduhaye, S., Rugegamanzi, E., Nshimiyimana, C., Menon, V., Emmanuel-Fabula, M., Denniston, A. K., et al. (2026) · Nature Health · doi:10.1038/s44360-025-00038-1 · Cited by 20

An Integrated Photorefractive Analog Matrix-Vector Multiplier for Machine Learning

Vlieg, E. A., Talandier, L., Dangel, R., Horst, F., & Offrein, B. J. (2022) · Applied Sciences · doi:10.3390/app12094226 · Cited by 14

Machine learning in renal pathology

Basso, M. N., Barua, M., Meyer, J. M., John, R., & Khademi, A. (2022) · Frontiers in Nephrology · doi:10.3389/fneph.2022.1007002 · Cited by 12

Provenance of Training without Training Data: Towards Privacy-Preserving DNN Model Ownership Verification

Liu, Y., Li, K., Liu, Z., Wen, B., Xu, K., Wang, W., Zhao, W., & Li, Q. L. (2023) · International World Wide Web Conference (WWW) · doi:10.1145/3543507.3583198 · Cited by 12

Towards more sustainable and trustworthy reporting in machine learning

Fischer, R., Liebig, T., & Morik, K. J. (2024) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-024-01020-3 · Cited by 12

Sustainable transparency on recommender systems: Bayesian ranking of images for explainability

Paz-Ruza, J., Alonso‐Betanzos, A., Guijarro‐Berdiñas, B., Cancela, B., & Eiras‐Franco, C. (2024) · Information Fusion · doi:10.1016/j.inffus.2024.102497 · Cited by 14

CORELOCKER: Neuron-level Usage Control

Wang, Z., Ma, Z., Feng, X., Sun, R., Hu, W., Xue, M., & Bai, G. (2024) · IEEE Symposium on Security and Privacy (S&P) · doi:10.1109/sp54263.2024.00233 · Cited by 13

Beyond AI as an environmental pharmakon: Principles for reopening the problem-space of machine learning's carbon footprint

Cellard, L., Parker, C., & Haines, F. (2025) · Environment and Planning E Nature and Space · doi:10.1177/25148486251332087 · Cited by 35

Towards a Sustainable Internet of Sounds

Gabrielli, L., & Turchet, L. (2022) · Audio Mostly Conference (AM) · doi:10.1145/3561212.3561246 · Cited by 12

Evaluation of Climate-Aware Metrics Tools for Radiology Informatics and Artificial Intelligence: Toward a Potential Radiology Ecolabel

Doo, F. X., Parekh, V. S., Kanhere, A., Savani, D., Tejani, A. S., Sapkota, A., & Yi, P. H. (2023) · Journal of the American College of Radiology · doi:10.1016/j.jacr.2023.11.019 · Cited by 12

On Twelve Shades of Green: Assessing the Levels of Environmental Protection in the Artificial Intelligence Act

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