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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 · see citing works

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 412 · see citing works

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 · see citing works

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 · see citing works

The evolved transformer

So, D. R., Liang, C., & Le, Q. V. (2019) · arXiv · doi:10.48550/arxiv.1901.11117 · Cited by 277 · see citing works

Show Your Work: Improved Reporting of Experimental Results

Dodge, J., Gururangan, S., Card, D., Schwartz, R., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP) · doi:10.18653/v1/d19-1224 · Cited by 233 · see citing works

Solving the Rubik’s cube with deep reinforcement learning and search

Agostinelli, F., McAleer, S., Shmakov, A., & Baldi, P. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0070-z · Cited by 136 · see citing works

Green AI

Schwartz, R., Dodge, J., Smith, N. A., & Etzioni, O. (2019) · arXiv · doi:10.48550/arxiv.1907.10597 · Cited by 73 · see citing works

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

High-Resolution Image Synthesis with Latent Diffusion Models

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works

Physics for neuromorphic computing

Marković, D., Mizrahi, F. A., Querlioz, D., & Grollier, J. (2020) · Nature Reviews Physics · doi:10.1038/s42254-020-0208-2 · Cited by 1,200 · see citing works

Challenges in deploying machine learning: A survey of case studies

Paleyes, A., Urma, R.-G., & Lawrence, N. D. (2023) · ACM Computing Surveys · doi:10.1145/3533378 · Cited by 638 · see citing works

Edge learning using a fully integrated neuro-inspired memristor chip

Zhang, W., Yao, P., Gao, B., Liu, Q., Wu, D. M., Zhang, Q., Li, Y., Qin, Q., Li, J., Zhu, Z., et al. (2023) · Science · doi:10.1126/science.ade3483 · Cited by 442 · see citing works

Deep Learning and Artificial Intelligence in Sustainability: A Review of SDGs, Renewable Energy, and Environmental Health

Fan, Z., Yan, Z., & Wen, S. (2023) · Sustainability · doi:10.3390/su151813493 · Cited by 393 · see citing works

A Comprehensive Survey on TinyML

Abadade, Y., Temouden, A., Bamoumen, H., Benamar, N., Chtouki, Y., & Hafid, A. S. (2023) · IEEE Access · doi:10.1109/access.2023.3294111 · Cited by 366 · see citing works

A comprehensive review of model compression techniques in machine learning

Dantas, P. V., da Silva, W. S., Cordeiro, L. C., & Carvalho, C. B. (2024) · Applied Intelligence · doi:10.1007/s10489-024-05747-w · Cited by 287 · see citing works

Deep learning for air pollutant concentration prediction: A review

Zhang, B., Rong, Y., Yong, R., Qin, D., Li, M., Zou, G., & Pan, J. (2022) · Atmospheric Environment · doi:10.1016/j.atmosenv.2022.119347 · Cited by 216 · see citing works

Machine Learning‐Based Surrogate Modeling for Urban Water Networks: Review and Future Research Directions

Garzón, A., Kapelan, Z. S., Langeveld, J., & Taormina, R. (2022) · Water Resources Research · doi:10.1029/2021wr031808 · Cited by 194 · see citing works

Intelligent disassembly of electric-vehicle batteries: a forward-looking overview

Meng, K., Xu, G., Peng, X., Youcef‐Toumi, K., & Li, J. (2022) · Resources Conservation and Recycling · doi:10.1016/j.resconrec.2022.106207 · Cited by 174 · see citing works

Vertical Stacked LEGO-PoL CPU Voltage Regulator

Baek, J.-I., Elasser, Y., Radhakrishnan, K., Gan, H., Douglas, J. P., Krishnamurthy, H. K., Li, X., Jiang, S., Sullivan, C. R., & Chen, M. (2021) · IEEE Transactions on Power Electronics · doi:10.1109/tpel.2021.3135386 · Cited by 166 · see citing works

Recent Advances in Generative AI and Large Language Models: Current Status, Challenges, and Perspectives

Hagos, D. H., Battle, R., & Rawat, D. B. (2024) · IEEE Transactions on Artificial Intelligence · doi:10.1109/tai.2024.3444742 · Cited by 192 · see citing works

Method and evaluations of the effective gain of artificial intelligence models for reducing CO2 emissions

Delanoë, P., Tchuente, D., & Colin, G. (2023) · Journal of Environmental Management · doi:10.1016/j.jenvman.2023.117261 · Cited by 153 · see citing works

Green and sustainable AI research: an integrated thematic and topic modeling analysis

Raghu Raman, R., Pattnaik, D., Lathabai, H. H., Kumar, C., Govindan, K., & Nedungadi, P. (2024) · Journal Of Big Data · doi:10.1186/s40537-024-00920-x · Cited by 171 · see citing works

A Practical Survey on Faster and Lighter Transformers

Fournier, Q., Caron, G. M., & Aloise, D. (2023) · ACM Computing Surveys · doi:10.1145/3586074 · Cited by 145 · see citing works

The Rise of Artificial Intelligence in Educational Measurement: Opportunities and Ethical Challenges

Bulut, O., Beiting-Parrish, M., Casabianca, J., Slater, S. C., Jiao, H., Song, D., Ormerod, C., Fabiyi, D. G., Ivan, R., Walsh, C., et al. (2024) · Chinese/English Journal of Educational Measurement and Evaluation · doi:10.59863/miql7785 · Cited by 83 · see citing works

Artificial intelligence and work: a critical review of recent research from the social sciences

Deranty, J.-P., & Corbin, T. (2024) · AI & SOCIETY · doi:10.1007/s00146-022-01496-x · Cited by 118 · see citing works

Ethics Sheet for Automatic Emotion Recognition and Sentiment Analysis

Mohammad, S. M. (2022) · Computational Linguistics · doi:10.1162/coli_a_00433 · Cited by 112 · see citing works

Optoelectronic metadevices

Ha, S. T., Li, Q., Yang, J. K. W., Demir, H. V., Brongersma, M. L., & Kuznetsov, A. I. (2024) · Science · doi:10.1126/science.adm7442 · Cited by 112 · see citing works

A systematic review of intermediate fusion in multimodal deep learning for biomedical applications

Guarrasi, V., Aksu, F., Caruso, C. M., Di Feola, F., Rofena, A., Ruffini, F., & Soda, P. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105509 · Cited by 95 · see citing works

Two‐Dimensional Memtransistors for Non‐Von Neumann Computing: Progress and Challenges

Wali, A., & Das, S. (2023) · Advanced Functional Materials · doi:10.1002/adfm.202308129 · Cited by 92 · see citing works

Transformers for Tabular Data Representation: A Survey of Models and Applications

Badaro, G., Saeed, M., & Papotti, P. (2023) · Transactions of the Association for Computational Linguistics · doi:10.1162/tacl_a_00544 · Cited by 96 · see citing works

Diffusion model-driven smart design and manufacturing: Prospects and challenges

Leng, J., Su, X., Liu, Z., Zhou, L., Chen, C., Guo, X., Wang, Y., Wang, R., Zhang, C., Liu, Q., et al. (2025) · Journal of Manufacturing Systems · doi:10.1016/j.jmsy.2025.07.011 · Cited by 89 · see citing works

Measuring and Improving the Energy Efficiency of Large Language Models Inference

Argerich, M. F., & Patiño-Martı́nez, M. (2024) · IEEE Access · doi:10.1109/access.2024.3409745 · Cited by 89 · see citing works

Toward Green AI: A Methodological Survey of the Scientific Literature

Barbierato, E., & Gatti, A. (2024) · IEEE Access · doi:10.1109/access.2024.3360705 · Cited by 86 · see citing works

Artificial intelligence in sustainable development research

Gohr, C., Aboytes, J. G. R., Belomestnykh, S., Berg-Moelleken, D., Chauhan, N., Engler, J., Heydebreck, L. V., Hintz, M. J., Kretschmer, M.-F., Krügermeier, C., et al. (2025) · Nature Sustainability · doi:10.1038/s41893-025-01598-6 · Cited by 82 · see citing works

Soybean disease identification using original field images and transfer learning with convolutional neural networks

Bevers, N., Sikora, E. J., & Hardy, N. B. (2022) · Computers and Electronics in Agriculture · doi:10.1016/j.compag.2022.107449 · Cited by 80 · see citing works

Application of artificial intelligence in air pollution monitoring and forecasting: A systematic review

Chadalavada, S., Faust, O., Salvi, M., Seoni, S., Raj, N., Raghavendra, U., Gudigar, A., Barua, P. D., Molinari, F., & Acharya, U. R. (2024) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2024.106312 · Cited by 78 · see citing works

Sustainability implications of artificial intelligence in the chemical industry: A conceptual framework

Liao, M., Lan, K., & Yao, Y. (2021) · Journal of Industrial Ecology · doi:10.1111/jiec.13214 · Cited by 80 · see citing works

Machine learning for passive mental health symptom prediction: Generalization across different longitudinal mobile sensing studies

Adler, D. A., Wang, F., Mohr, D. C., & Choudhury, T. (2022) · PLoS ONE · doi:10.1371/journal.pone.0266516 · Cited by 79 · see citing works

High-performance deep spiking neural networks with 0.3 spikes per neuron

Stanojevic, A., Woźniak, S., Bellec, G., Cherubini, G., Pantazi, A., & Gerstner, W. (2024) · Nature Communications · doi:10.1038/s41467-024-51110-5 · Cited by 74 · see citing works

Quantum Machine Learning: Recent Advances, Challenges, and Perspectives

Lamichhane, P., & Rawat, D. B. (2025) · IEEE Access · doi:10.1109/access.2025.3573244 · Cited by 79 · see citing works

Multi-task deep learning for glaucoma detection from color fundus images

Pascal, L., Perdómo, O., Bost, X., Huet, B., Otálora, S., & Zuluaga, M. A. (2022) · Scientific Reports · doi:10.1038/s41598-022-16262-8 · Cited by 74 · see citing works

Green Federated Learning: A New Era of Green Aware AI

Thakur, D., Guzzo, A., Fortino, G., & Piccialli, F. (2025) · ACM Computing Surveys · doi:10.1145/3718363 · Cited by 77 · see citing works

Reconciling the contrasting narratives on the environmental impact of large language models

Ren, S., Tomlinson, B., Black, R. W., & Torrance, A. W. (2024) · Scientific Reports · doi:10.1038/s41598-024-76682-6 · Cited by 68 · see citing works

Training data influence analysis and estimation: a survey

Hammoudeh, Z., & Lowd, D. (2024) · Machine Learning · doi:10.1007/s10994-023-06495-7 · Cited by 80 · see citing works

Artificial Intelligence in the NHS: Climate and Emissions✰,✰✰

Bloomfield, P. S., Clutton-Brock, P., Pencheon, E., Magnusson, J., & Karpathakis, K. (2021) · The Journal of Climate Change and Health · doi:10.1016/j.joclim.2021.100056 · Cited by 71 · see citing works

Ecovisor: A Virtual Energy System for Carbon-Efficient Applications

Souza, A., Bashir, N., Murillo, J., Hanafy, W. A., Liang, Q., Irwin, D., & Shenoy, P. J. (2023) · International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS) · doi:10.1145/3575693.3575709 · Cited by 66 · see citing works

Understanding the Practices, Perceptions, and (Dis)Trust of Generative AI among Instructors: A Mixed-methods Study in the U.S. Higher Education

Lyu, W., Zhang, S., Chung, T., Sun, Y., & Zhang, Y. (2025) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2025.100383 · Cited by 68 · see citing works

An Outlook for Deep Learning in Ecosystem Science

Perry, G. L. W., Seidl, R., Bellvé, A. M., & Rammer, W. (2022) · Ecosystems · doi:10.1007/s10021-022-00789-y · Cited by 69 · see citing works

Artificial intelligence for calculating and predicting building carbon emissions: a review

Hua, J., Wang, R., Hu, Y. C., Chen, Z., Chen, L., Osman, A. I., Farghali, M. R. F., Huang, L., Feng, J., Wang, J., et al. (2025) · Environmental Chemistry Letters · doi:10.1007/s10311-024-01799-z · Cited by 72 · see citing works

Survey on AI Sustainability: Emerging Trends on Learning Algorithms and Research Challenges [Review Article]

Chen, Z., Wu, M., Chan, A., Li, X., & Ong, Y.-S. (2023) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2023.3245733 · Cited by 73 · see citing works

Artificial Intelligence and Sustainability—A Review

Dhiman, R., Miteff, S., Wang, Y., Ma, S.-C., Amirikas, R., & Fabian, B. (2024) · Analytics · doi:10.3390/analytics3010008 · Cited by 36 · see citing works

Deep transfer learning for image classification: a survey

Plested, J., Phiri, M., & Gedeon, T. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-026-11491-z · Cited by 74 · see citing works

Toward Sustainable Serverless Computing

Patros, P., Spillner, J., Papadopoulos, A. V., Varghese, B., RANA, O. F., & Dustdar, S. (2021) · IEEE Internet Computing · doi:10.1109/mic.2021.3093105 · Cited by 53 · see citing works

Building a Sustainable Future: The Nexus Between Artificial Intelligence, Renewable Energy, Green Human Capital, Geopolitical Risk, and Carbon Emissions Through the Moderating Role of Institutional Quality

Iqbal, A., Zhang, W., & Jahangir, S. (2025) · Sustainability · doi:10.3390/su17030990 · Cited by 65 · see citing works

Parameter-efficient deep probabilistic forecasting

Sprangers, O., Schelter, S., & de Rijke, M. (2022) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2021.11.011 · Cited by 52 · see citing works

Fusing CNNs and statistical indicators to improve image classification

Huertas Tato, J., Martín, A. G., Camacho, D., & Fiérrez Aguilar, J. (2021) · Hispana · doi:10.1016/j.inffus.2021.09.012 · Cited by 51 · see citing works

Artificial Intelligence for Alleviating Energy Poverty: Pathways Toward Sustainable and Renewable Energy Transitions

Wang, Q., Sun, T., & Li, R. (2025) · Sustainable Development · doi:10.1002/sd.70388 · Cited by 56 · see citing works

Review of Memristors for In‐Memory Computing and Spiking Neural Networks

Shooshtari, M., Serrano‐Gotarredona, T., & Linares‐Barranco, B. (2025) · Advanced Intelligent Systems · doi:10.1002/aisy.202500806 · Cited by 59 · see citing works

Neural Architecture Search and Hardware Accelerator Co-Search: A Survey

Sekanina, L. (2021) · IEEE Access · doi:10.1109/access.2021.3126685 · Cited by 50 · see citing works

The impact of artificial intelligence on the energy consumption of corporations: The role of human capital

Lee, C., Zou, J., & Chen, P. (2025) · Energy Economics · doi:10.1016/j.eneco.2025.108231 · Cited by 49 · see citing works

Large Language Models: A Structured Taxonomy and Review of Challenges, Limitations, Solutions, and Future Directions

Peykani, P., Ramezanlou, F., Tănăsescu, C. R., & Ghanidel, S. (2025) · Applied Sciences · doi:10.3390/app15148103 · Cited by 57 · see citing works

Recent Advances in Machine Learning Variant Effect Prediction Tools for Protein Engineering

Horne, J., & Shukla, D. (2022) · Industrial & Engineering Chemistry Research · doi:10.1021/acs.iecr.1c04943 · Cited by 46 · see citing works

New trends in photonic switching and optical networking architectures for data centers and computing systems [Invited]

Yoo, S. J. B. (2023) · Journal of Optical Communications and Networking · doi:10.1364/jocn.484577 · Cited by 45 · see citing works

Memcapacitor Crossbar Array with Charge Trap NAND Flash Structure for Neuromorphic Computing

Hwang, S., Yu, J., Song, M. S., Hwang, H., & Kim, H. (2023) · Advanced Science · doi:10.1002/advs.202303817 · Cited by 43 · see citing works

Towards Efficient Fine-Tuning of Pre-trained Code Models: An Experimental Study and Beyond

Shi, E., Wang, Y., Zhang, H., Du, L., Han, S., Zhang, D., & Sun, H. (2023) · ACM SIGSOFT International Symposium on Software Testing and Analysis (ISSTA) · doi:10.1145/3597926.3598036 · Cited by 43 · see citing works

On-chip phonon-magnon reservoir for neuromorphic computing

Yaremkevich, D. D., Scherbakov, A. V., Clerk, L. D., Kukhtaruk, S. M., Nadzeyka, A., Campion, R. P., Rushforth, A. W., Savel'ev, S. E., Balanov, A. G., & Bayer, M. (2023) · Nature Communications · doi:10.1038/s41467-023-43891-y · Cited by 42 · see citing works

Deep learning and satellite remote sensing for biodiversity monitoring and conservation

Pettorelli, N., Williams, J., Bühne, H. S. T., & Crowson, M. (2024) · Remote Sensing in Ecology and Conservation · doi:10.1002/rse2.415 · Cited by 47 · see citing works

Randomized Explainable Machine Learning Models for Efficient Medical Diagnosis

Muhammad, D., Ahmed, I., Ahmad, M. O., & Bendechache, M. (2024) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3491593 · Cited by 39 · see citing works

Artificial intelligence and sustainable development: Public concerns and governance in developed and developing nations

Maghsoudi, M., Mohammadi, N., & Bakhtiari, M. (2025) · Cleaner Environmental Systems · doi:10.1016/j.cesys.2025.100340 · Cited by 37 · see citing works

Measuring the environmental impacts of artificial intelligence compute and applications

OECD (2022) · OECD digital economy papers · doi:10.1787/7babf571-en · Cited by 72 · see citing works

Photonic advantage of optical encoders

Huang, L., Tanguy, Q. A. A., Fröch, J. E., Mukherjee, S., Böhringer, K. F., & Majumdar, A. (2023) · Nanophotonics · doi:10.1515/nanoph-2023-0579 · Cited by 36 · see citing works

Cost-aware retraining for machine learning

Mahadevan, A., & Mathioudakis, M. (2024) · Knowledge-Based Systems · doi:10.1016/j.knosys.2024.111610 · Cited by 36 · see citing works

Harnessing Artificial Intelligence

Durai, S., Manoharan, G., & Ashtikar, S. P. (2024) · Advances in finance, accounting, and economics book series · doi:10.4018/979-8-3693-2881-1.ch003 · Cited by 38 · see citing works

The Worst of Both Worlds: A Comparative Analysis of Errors in Learning from Data in Psychology and Machine Learning

Hullman, J. R., Kapoor, S., Nanayakkara, P., Gelman, A., & Narayanan, A. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534196 · Cited by 37 · see citing works

FedZero: Leveraging Renewable Excess Energy in Federated Learning

Wiesner, P., Khalili, R., Grinwald, D., Agrawal, P., Thamsen, L., & Kao, O. (2024) · ACM International Conference on Future Energy Systems (e-Energy) · doi:10.1145/3632775.3639589 · Cited by 34 · see citing works

Embracing Green Computing in Molecular Phylogenetics

Kumar, S. (2022) · Molecular Biology and Evolution · doi:10.1093/molbev/msac043 · Cited by 34 · see citing works

An exact mapping from ReLU networks to spiking neural networks

Stanojevic, A., Woźniak, S., Bellec, G., Cherubini, G., Pantazi, A., & Gerstner, W. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.09.011 · Cited by 34 · see citing works

A cognitive platform for collecting cyber threat intelligence and real-time detection using cloud computing

Balasubramanian, P., Nazari, S., Kholgh, D. K., Mahmoodi, A., Seby, J., & Kostakos, P. (2025) · Decision Analytics Journal · doi:10.1016/j.dajour.2025.100545 · Cited by 33 · see citing works

Impact of data for forecasting on performance of model predictive control in buildings with smart energy storage

Langtry, M., Wichitwechkarn, V., Ward, R. M., Zhuang, C., Kreitmair, M. J., Makasis, N., Conti, Z. X., & Choudhary, R. (2024) · Energy and Buildings · doi:10.1016/j.enbuild.2024.114605 · Cited by 32 · see citing works

Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs

Wimmer, P., Mehnert, J., & Condurache, A. P. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.01220 · Cited by 32 · see citing works

Future Shock: Generative AI and the International AI Policy and Governance Crisis

Leslie, D., & Perini, A. M. (2024) · Harvard Data Science Review · doi:10.1162/99608f92.88b4cc98 · Cited by 33 · see citing works

Frugal Optimization for Cost-related Hyperparameters

Wu, Q., Wang, C., & Huang, S. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v35i12.17239 · Cited by 31 · see citing works

Material to system-level benchmarking of CMOS-integrated RRAM with ultra-fast switching for low power on-chip learning

Abedin, M., Gong, N., Beckmann, K., Liehr, M., Saraf, I., der Straten, O. V., Ando, T., & Cady, N. C. (2023) · Scientific Reports · doi:10.1038/s41598-023-42214-x · Cited by 31 · see citing works

Multimodal large language models for inclusive collaboration learning tasks

Lewis, A. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Student Research Workshop · doi:10.18653/v1/2022.naacl-srw.26 · Cited by 30 · see citing works

Energy-Efficient Neural Network Inference with Microcavity Exciton Polaritons

Matuszewski, M., Opala, A., Mirek, R., Furman, M., Król, M., Tyszka, K., Liew, T. C. H., Ballarini, D., Sanvitto, D., Szczytko, J., et al. (2021) · Physical Review Applied · doi:10.1103/physrevapplied.16.024045 · Cited by 30 · see citing works

CatAlyst: Domain-Extensible Intervention for Preventing Task Procrastination Using Large Generative Models

Arakawa, R., Yakura, H., & Goto, M. (2023) · Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581133 · Cited by 29 · see citing works

Dimensionality reduced training by pruning and freezing parts of a deep neural network: a survey

Wimmer, P., Mehnert, J., & Condurache, A. P. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10489-1 · Cited by 29 · see citing works

Reconfigurable versatile integrated photonic computing chip

Wang, Y., Liao, K., Zhang, K., Du, Z., Wang, Z., Ni, B., Xu, T., Feng, S., Yang, Y., Yang, Q., et al. (2025) · eLight · doi:10.1186/s43593-025-00098-6 · Cited by 29 · see citing works

The implementation of SDG12 in and from higher education institutions: universities as laboratories for generating sustainable cities

Martínez-Acosta, M., Vázquez‐Villegas, P., Mejía‐Manzano, L. A., Soto-Inzunza, G. V., Ruiz-Aguilar, K. M., Cuellar, L. K., Caratozzolo, P., & Membrillo‐Hernández, J. (2023) · Frontiers in Sustainable Cities · doi:10.3389/frsc.2023.1158464 · Cited by 28 · see citing works

RAI Guidelines: Method for Generating Responsible AI Guidelines Grounded in Regulations and Usable by (Non-)Technical Roles

Constantinides, M., Bogucka, E. P., Quercia, D., Kallio, S., & Tahaei, M. (2024) · Proceedings of the ACM on Human-Computer Interaction · doi:10.1145/3686927 · Cited by 34 · see citing works

Large Language Models in Transportation: A Comprehensive Bibliometric Analysis of Emerging Trends, Challenges, and Future Research

Hassan, M., Kabir, M. E., Jusoh, M., An, H. K., Negnevitsky, M., & Li, C. (2025) · IEEE Access · doi:10.1109/access.2025.3589319 · Cited by 30 · see citing works

Large-scale photonic natural language processing

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