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

An Innovative Aircraft Skin Damage Assessment Using You Only Look Once-Version9: A Real-Time Material Evaluation System for Remote Inspection

Liao, K.-C., Lau, J., & Hidayat, M. (2025) · Aerospace · doi:10.3390/aerospace12010031 · Cited by 12

The ethical evaluation of large language models and its optimization

Lyu, Y., & Du, Y. (2025) · AI and Ethics · doi:10.1007/s43681-024-00654-9 · Cited by 11

Advancements and Applications of Multimodal Large Language Models: Integration, Challenges, and Future Directions

Fahad, S. K. A., Zhengkui, D. W., Ng, P. C., Wong, N., Ng, A. B., & See, S. C. W. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-80334-5_19 · Cited by 13

Artificial Intelligence-Assisted Case Management in Social Work Services: A Systematic Review

Li, L., Wang, M., & Jian, M. (2025) · Research on Social Work Practice · doi:10.1177/10497315251329531 · Cited by 11

Energy-efficient deep learning model for fruit freshness detection

Valentino, F., Cenggoro, T. W., Elwirehardja, G. N., & Pardamean, B. (2023) · IAES International Journal of Artificial Intelligence · doi:10.11591/ijai.v12.i3.pp1386-1395 · Cited by 12

CLEMSON: An Automated Machine-Learning Virtual Assistant for Accelerated, Simulation-Free, Transparent, Reduced-Order, and Inference-Based Reconstruction of Fire Response of Structural Members

Naser, M. Z. (2022) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0003399 · Cited by 12

A Systematic Review of Intermediate Fusion in Multimodal Deep Learning for Biomedical ApplicationsPreprint

Guarrasi, V., Aksu, F., Caruso, C. M., Feola, F. D., Rofena, A., Ruffini, F., & Soda, P. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4952813 · Cited by 13

ATNAS: Automatic Termination for Neural Architecture Search

Sakamoto, K., Ishibashi, H., Sato, R., Shirakawa, S., Akimoto, Y., & Hino, H. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.07.011 · Cited by 10

Explaining Deep Q-Learning Experience Replay with SHapley Additive exPlanations

Sullivan, R. S., & Longo, L. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5040072 · Cited by 10

Definitions Matter: Guiding GPT for Multi-label Classification

Peskine, Y., Korenčić, D., Grubišić, I., Papotti, P., Raphaël Troncy, A., & Rosso, P. (2023) · Findings of the Association for Computational Linguistics: EMNLP (Findings of EMNLP) · doi:10.18653/v1/2023.findings-emnlp.267 · Cited by 14

Lightweight Semantic Segmentation Network Leveraging Class-Aware Contextual Information

Xu, X., Huang, S., & Lai, H. (2023) · IEEE Access · doi:10.1109/access.2023.3345790 · Cited by 10

A Situated-Infrastructuring of WhatsApp for Business in India

De, A. (2024) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3613905.3651034 · Cited by 10

AnyFace++: Deep Multi-Task, Multi-Domain Learning for Efficient Face AI

Rakhimzhanova, T., Kuzdeuov, A., & Varol, H. A. (2024) · Sensors · doi:10.3390/s24185993 · Cited by 15

Digital transformation in banking: Curbing procyclical leverage to strengthen financial stability

Wang, L., Huang, Z., Wang, Y., & Yang, Y. (2025) · International Review of Financial Analysis · doi:10.1016/j.irfa.2025.104205 · Cited by 11

A Critical Analysis of Machine Learning Eco-feedback Tools through the Lens of Sustainable HCI

Görücü, S., Morais, L., & Panagiotidou, G. (2025) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713198 · Cited by 10

FFLUNet: Feature Fused Lightweight UNet for brain tumor segmentation

Kundu, S., Dutta, S., Mukhopadhyay, J., & Chakravorty, N. (2025) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2025.110460 · Cited by 14

ChatGPT and Digital Transformation: A Narrative Review of Its Role in Health, Education, and the Economy

Madsen, D. Ø., & Toston, D. M. (2025) · Digital · doi:10.3390/digital5030024 · Cited by 16

Hyperautomation

Hasan, M. K., Latif, Z., Hlali, A., Xun-ping, L., & Aka, S. A. B. (2025) · IGI Global eBooks · doi:10.4018/979-8-3373-2434-0.ch001 · Cited by 10

Core principles of responsible generative AI usage in research

Knöchel, T.-D., Schweizer, K. J., Acar, O. A., Akıl, A. M., Al‐Hoorie, A. H., Buehler, F., Elsherif, M. M., Giannini, A., Heyselaar, E., Hosseini, M., et al. (2025) · AI and Ethics · doi:10.1007/s43681-025-00768-8 · Cited by 22

Organizational sustainable artificial intelligence capabilities scale development, validation, and implications

Qalati, S. A., & Siddiqui, F. (2025) · Journal of Innovation & Knowledge · doi:10.1016/j.jik.2025.100863 · Cited by 10

Optical‐Vortex‐Convolution‐Enhanced Neural Network

Guo, H., Xu, C. Q., Xiong, Q., Zhang, X., Wang, J., & Qiu, X. (2025) · Laser & Photonics Review · doi:10.1002/lpor.202500927 · Cited by 10

Data-Efficient Finetuning Using Cross-Task Nearest Neighbors

Ivison, H., Smith, N. A., Hajishirzi, H., & Dasigi, P. (2023) · Findings of the Association for Computational Linguistics: ACL (Findings of ACL) · doi:10.18653/v1/2023.findings-acl.576 · Cited by 23

Artificial intelligence, sustainability and environmental impact. A narrative and bibliometric study

Camastra, F. D., & Vallejo, R. G. (2025) · Región Científica · doi:10.58763/rc2025355 · Cited by 20

Optical neuromorphic computing via temporal up‐sampling and trainable encoding on a telecom device platform

Manuylovich, E. S., Stoliarov, D. A., Saad, D., & Turitsyn, S. K. (2025) · Nanophotonics · doi:10.1515/nanoph-2024-0614 · Cited by 9

Fiber-based diffractive deep neural network

Kesgin, B. U., Yüce, F., & Teğin, U. (2025) · Optics Letters · doi:10.1364/ol.570135 · Cited by 12

Sustainable adoption of artificial intelligence and the Metaverse in higher education: an environmental, social, and governance–based analysis of pedagogical innovation and perceived student learning outcomes

Alqurni, J. S. (2026) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2026.1738730 · Cited by 11

Machine Learning methods in climate finance: a systematic review

Alonso-Robisco, A., Carbó, J. M., & Carbó, J. M. (2023) · Documentos de trabajo/Documento de trabajo - Banco de España, Servicio de Estudios · doi:10.53479/29594 · Cited by 9

Deployment of Machine Learning Algorithms on Resource-Constrained Hardware Platforms for Prosthetics

Just, F., Ghinami, C., Zbinden, J., & Ortiz-Catalan, M. (2024) · IEEE Access · doi:10.1109/access.2024.3371251 · Cited by 8

Performance and sustainability of BERT derivatives in dyadic data

Escarda-Fernández, M., Eiras‐Franco, C., Cancela, B., Guijarro‐Berdiñas, B., & Alonso‐Betanzos, A. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2024.125647 · Cited by 11

GreenWhisk: Emission-Aware Computing for Serverless Platform

Serenari, J., Sreekumar, S., Zhao, K., Sarkar, S., & Lee, S. (2024) · IEEE International Conference on Cloud Engineering · doi:10.1109/ic2e61754.2024.00012 · Cited by 8

Pioneering Eco-Efficiency in Cloud Computing: The Carbon-Conscious Federated Reinforcement Learning (CCFRL) Approach

Seo, E., & Elmroth, E. (2025) · IEEE Internet of Things Journal · doi:10.1109/jiot.2024.3504260 · Cited by 8

Synergizing DeepSeek's artificial intelligence innovations with brain–computer interfaces

Wu, C., Chen, N., Sun, T., Tan, P., Wang, P., & Li, G. (2025) · Brain‐X · doi:10.1002/brx2.70035 · Cited by 8

PT-BitNet: Scaling up the 1-Bit large language model with post-training quantization

Guo, Y., Hao, Z., Shao, J., Zhou, J., Liu, X., Tong, X., Zhang, Y., Chen, Y., Peng, W., & Ma, Z. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107855 · Cited by 10

A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions

Naser, M. Z. (2025) · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · doi:10.1002/widm.70038 · Cited by 11

What does current genAI actually mean for student learning?

Dinsmore, D. L., & Fryer, L. K. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102834 · Cited by 10

Implementation, Performance, and Energy Efficiency Analysis for Text Classification of Deep Learning Architectures

Khullar, S., Magdalene, A. H. S., Sandhya, P., Naik, B. R., N, H. K. B., & Raju, M. V. (2025) · International Conference on Communication and Electronics Systems (ICCES) · doi:10.1109/icces67310.2025.11336350 · Cited by 9

Accelerating Large-Scale Graph Neural Network Training on Crossbar Diet

Ogbogu, C., Arka, A. I., Joardar, B. K., Doppa, J. R., Li, H. H., Chakrabarty, K., & Pande, P. P. (2022) · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems · doi:10.1109/tcad.2022.3197342 · Cited by 8

Sentiment analysis in Portuguese tweets: an evaluation of diverse word representation models

Vianna, D., Carneiro, F., Carvalho, J., Plastino, A., & Paes, A. (2023) · Language Resources and Evaluation · doi:10.1007/s10579-023-09661-4 · Cited by 8

Effective Machine Learning-based Access Control Administration through Unlearning

Llamas, J. M., Preuveneers, D., & Joosen, W. (2023) · IEEE European Symposium on Security and Privacy Workshops (EuroS&PW) · doi:10.1109/eurospw59978.2023.00011 · Cited by 8

The carbon footprint of predicting CO2 storage capacity in metal-organic frameworks within neural networks

Korolev, V., & Mitrofanov, A. A. (2024) · iScience · doi:10.1016/j.isci.2024.109644 · Cited by 10

Green AI in the finance industry: Exploring the impact of feature engineering on the accuracy and computational time of Machine Learning models

Machado, M., Asadi, A., Souza, R., & Ugulino, W. C. (2024) · Applied Soft Computing · doi:10.1016/j.asoc.2024.112343 · Cited by 9

Prompt engineering and its implications on the energy consumption of Large Language Models

Rubei, R., Moussaid, A., Sipio, C. D., & Ruscio, D. D. (2025) · IEEE/ACM International Workshop on Green and Sustainable Software (GREENS) · doi:10.1109/greens66463.2025.00014 · Cited by 30

SpeakerStew: Scaling to Many Languages with a Triaged Multilingual Text-Dependent and Text-Independent Speaker Verification System

Chojnacka, R., Pelecanos, J. W., Wang, Q., & Moreno, I. L. (2021) · Interspeech (USB) · doi:10.21437/interspeech.2021-646 · Cited by 9

Gradient Decomposition Methods for Training Neural Networks With Non-ideal Synaptic Devices

Zhao, J., Huang, S., Yousuf, O., Gao, Y., Hoskins, B. D., & Adam, G. C. (2021) · Frontiers in Neuroscience · doi:10.3389/fnins.2021.749811 · Cited by 8

Machine Learning Methods in Climate Finance: A Systematic ReviewPreprint

Alonso, A., Carbó, J. M., & Marqués, J. M. (2023) · SSRN Electronic Journal · doi:10.2139/ssrn.4352569 · Cited by 9

Beyond Fairness in Computer Vision: A Holistic Approach to Mitigating Harms and Fostering Community-Rooted Computer Vision Research

Gebru, T., & Denton, E. (2024) · Foundations and Trends® in Computer Graphics and Vision · doi:10.1561/0600000102 · Cited by 8

Spintronic Neuron Using a Magnetic Tunnel Junction for Low-Power Neuromorphic Computing

Louis, S., Bradley, H., Trevillian, C., Slavin, A. N., & Tiberkevich, V. S. (2024) · IEEE Magnetics Letters · doi:10.1109/lmag.2024.3484957 · Cited by 7

Hyperdimensional Representation Learning for Node Classification and Link Prediction

Dalvi, A., & Honavar, V. (2025) · ACM International Conference on Web Search and Data Mining (WSDM) · doi:10.1145/3701551.3703492 · Cited by 10

Compensating PI Controller’s Transients with Tiny Neural Network for Vector Control of Permanent Magnet Synchronous Motors

Elele, M. J. M., Pau, D. P., Zhuang, S., & Facchinetti, T. (2025) · World Electric Vehicle Journal · doi:10.3390/wevj16040236 · Cited by 7

Improving User Engagement and Learning Outcomes in LLM-Based Python Tutor: A Study of PACE

Shochcho, M. I., Rahman, M. A. U., Rohan, S., Islam, A., Heickal, H., Rahman, A. K. M. M., Amin, M. A., & Ali, A. A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3720240 · Cited by 14

GPTFootprint: Increasing Consumer Awareness of the Environmental Impacts of LLMs

Graves, N., Larrieu, V., Zhang, Y., Peng, J., Rao, V. N., Liu, Y., & Monroy‐Hernández, A. (2025) · CHI Conference Extended Abstracts on Human Factors in Computing Systems (CHI EA) · doi:10.1145/3706599.3719708 · Cited by 7

Data Ecofeminism

Valdivia, A. (2025) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3715275.3732027 · Cited by 11

Optical image processing and applications empowered by vision-language models

Xiao, J., Sun, Z., An, H., Zhao, H., Qiu, M., & Li, X. (2025) · iOptics · doi:10.1016/j.iopt.2025.100003 · Cited by 7

Unlocking the potential of artificial intelligence in wastewater treatment: Innovations, opportunities, and challenges

Jaganathan, J. S., Ali, J. M., & Abdullah, S. R. S. (2025) · Journal of environmental chemical engineering · doi:10.1016/j.jece.2025.119671 · Cited by 8

Exploring Prospective Teachers’ Intentions for Artificial Intelligence Integration in Education: The Role of Motivation

Yurt, E., & Kaşarcı, İ. (2025) · AERA Open · doi:10.1177/23328584251403990 · Cited by 9

A multidisciplinary approach to understanding the technology, opportunities, challenges, and implications for society of the metaverse

Slater, M., Dalmazi, M. D., Friedman, D. A., Galissaire, J., Isaac, H., Kobusińska, A., Lopez-Tarruella-Martinez, A., Lamberti, L., Lucas, J.-F., Niamut, O. A., et al. (2025) · Frontiers in Virtual Reality · doi:10.3389/frvir.2025.1566419 · Cited by 11

Ground-truthing AI energy consumption: validating CodeCarbon against external measurements

Fischer, R. (2026) · it - Information Technology · doi:10.1515/itit-2025-0031 · Cited by 26

A comprehensive review of task understanding of command-triggered execution of tasks for service robots

Xi, X., & Zhu, S. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10347-6 · Cited by 7

A maximum entropy approach for the modelling of car-sharing parking dynamics

Daniotti, S., Monechi, B., & Ubaldi, E. (2023) · Scientific Reports · doi:10.1038/s41598-023-30134-9 · Cited by 8

Accountable Deep-Learning-Based Vision Systems for Preterm Infant Monitoring

Migliorelli, L., Tiribelli, S., Cacciatore, A., Giovanola, B., Frontoni, E., & Moccia, S. (2023) · Computer · doi:10.1109/mc.2023.3235987 · Cited by 7

Efficient galaxy classification through pretraining

Schneider, J., Stenning, D. C., & Elliott, L. T. (2023) · Frontiers in Astronomy and Space Sciences · doi:10.3389/fspas.2023.1197358 · Cited by 11

A Model for Estimating the Economic Costs of Computer Vision Systems That Use Deep Learning

Thompson, N., Fleming, M., Tang, B. J., Pastwa, A. M., Borge, N., Goehring, B., & Das, S. (2024) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v38i21.30343 · Cited by 7

e-Health and environmental sustainability in vascular surgery

Stevenin, G., Canonge, J., Gervais, M., Fiore, A., Lareyre, F., Touma, J., Desgranges, P., Raffort, J., & Sénémaud, J. N. (2024) · Seminars in Vascular Surgery · doi:10.1053/j.semvascsurg.2024.08.005 · Cited by 7

Unity Is Intelligence: A Collective Intelligence Experiment on ECG Reading to Improve Diagnostic Performance in Cardiology

Ronzio, L., Campagner, A., Cabitza, F., & Gensini, G. F. (2021) · Journal of Intelligence · doi:10.3390/jintelligence9020017 · Cited by 7

Efficient, continual, and generalized learning in the brain – neural mechanism of Mental Schema 2.0 –

Ohki, T., Kunii, N., & Chao, Z. C. (2023) · Reviews in the Neurosciences · doi:10.1515/revneuro-2022-0137 · Cited by 7

Is Quality Enoughƒ Integrating Energy Consumption in a Large-Scale Evaluation of Neural Audio Synthesis Models

Douwes, C., Bindi, G., Caillon, A., Esling, P., & Briot, J.-P. (2023) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49357.2023.10096975 · Cited by 8

ranxhub: An Online Repository for Information Retrieval Runs

Bassani, E. (2023) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3539618.3591823 · Cited by 7

DLPformer: A Hybrid Mathematical Model for State of Charge Prediction in Electric Vehicles Using Machine Learning Approaches

Wang, Y., Chen, N., Fan, G., Yang, D., Rao, L., Cheng, S., & Song, X. (2023) · Mathematics · doi:10.3390/math11224635 · Cited by 7

Enhancing in-situ updates of quantized memristor neural networks: a Siamese network learning approach

Tan, J., Zhang, F., Wu, J., Luo, L., Duan, S., & Wang, L. (2024) · Cognitive Neurodynamics · doi:10.1007/s11571-024-10069-1 · Cited by 8

Multi-task Deep Learning for Glaucoma Detection From Color Fundus ImagesPreprint

Pascal, L., Perdómo, O., Bost, X., Huet, B., Otálora, S., & Zuluaga, M. A. (2022) · Research Square · doi:10.21203/rs.3.rs-1357713/v1 · Cited by 7

Sustainable Outsourcing in an AI-Driven World

Kumar, M., Rani, P., Mangla, N., Nguyen, N. T., & Kaur, K. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-1270-5.ch001 · Cited by 8

Advancing the Biological Plausibility and Efficacy of Hebbian Convolutional Neural Networks

Nimmo, J., & Mondragón, E. (2025) · Neural Networks · doi:10.1016/j.neunet.2025.107628 · Cited by 8

Sustainability powered by digitalization? (Re-)politicizing the debate

Steig, F., König, P. D., Marquardt, J., Oels, A., Radtke, J., Rehak, R., & Weiland, S. (2025) · Sustainability Science Practice and Policy · doi:10.1080/15487733.2025.2521181 · Cited by 9

Benchmarking and Characterization of Large Language Model Inference on Apple Silicon

Benazir, A., & Lin, F. X. (2025) · ACM SIGMETRICS Performance Evaluation Review · doi:10.1145/3771563 · Cited by 18

Trustworthy, responsible and ethical artificial intelligence in manufacturing and supply chains: synthesis and emerging research questions

Brintrup, A. M., Baryannis, G., Tiwari, A., Ratchev, S. M., Martínez-Arellano, G., & Singh, J. P. (2025) · Data-Centric Engineering · doi:10.1017/dce.2025.10032 · Cited by 44

Expanding Human-in-the-Loop: Critical Sensemaking for Technical and Professional Communication With Generative AI

Verhulsdonck, G., & Jiang, J. (2026) · Journal of Technical Writing and Communication · doi:10.1177/00472816251405787 · Cited by 6

Designing green artificial intelligence (Green AI) models for finance: a novel approach for sustainable and responsible adoption

Elbouknify, I., Machado, M., & Iannario, M. (2026) · Financial Innovation · doi:10.1186/s40854-026-00915-y · Cited by 6

Real-Time Event Detection Using Self-Evolving Contextual Analysis (SECA) Approach

Sulaimani, S. A., & Starkey, A. J. (2023) · IEEE Access · doi:10.1109/access.2023.3331219 · Cited by 6

Sustainability in the Age of AI

Singh, J., Chugh, A., & Singh, A. K. (2023) · Advances in environmental engineering and green technologies book series · doi:10.4018/979-8-3693-0338-2.ch001 · Cited by 6

Bi-Directional Gaze-Based Communication: A Review

Severitt, B., Castner, N., & Wahl, S. (2024) · Multimodal Technologies and Interaction · doi:10.3390/mti8120108 · Cited by 6

AI-based carbon peak prediction and energy transition optimization for thermal power industry in energy-intensive regions of China

Huang, C., Lin, Z., Wu, J. F., Li, P., Zhang, C., Liu, Y., Chen, W., Xu, X., & Deng, J. (2025) · Energy Conversion and Management X · doi:10.1016/j.ecmx.2025.100884 · Cited by 6

Agricultural Text Classification Method Based on ERNIE 2.0 and Multi-Feature Dynamic Fusion

Duan, X., Liu, Y., You, Z., & Li, Z. (2025) · IEEE Access · doi:10.1109/access.2025.3537277 · Cited by 6

Information theoretic perspective on sample complexity

Pereg, D. (2023) · Neural Networks · doi:10.1016/j.neunet.2023.08.032 · Cited by 7

Augmenting Zero-Shot Dense Retrievers with Plug-in Mixture-of-Memories

Ge, S., Xiong, C., Rosset, C., Overwijk, A., Han, J., & Bennett, P. N. (2023) · Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.18653/v1/2023.emnlp-main.111 · Cited by 10

BNN-Flip: Enhancing the Fault Tolerance and Security of Compute-in-Memory Enabled Binary Neural Network Accelerators

Malhotra, A., Wang, C., & Gupta, S. K. (2024) · Asia and South Pacific Design Automation Conference (ASP-DAC) · doi:10.1109/asp-dac58780.2024.10473947 · Cited by 6

Carbon-Aware and Fault-Tolerant Migration of Deep Learning Workloads in the Geo-Distributed Cloud

Park, J., Kim, D., Kim, J., Han, J., & Chun, S. (2024) · 2024 IEEE 17th International Conference on Cloud Computing (CLOUD) · doi:10.1109/cloud62652.2024.00062 · Cited by 5

Forecasting electricity demand in Türkiye: A comprehensive review of methods, determinants, and policy implications

Elbaş, H., & Bilgin, T. T. (2025) · Journal of Energy Systems · doi:10.30521/jes.1549293 · Cited by 5

Enabling Autonomous and Adaptive Social Robots in Education: A Vision for the Application of Generative AI

Verhelst, E., Janssens, R., & Belpaeme, T. (2025) · Studies in computational intelligence · doi:10.1007/978-3-031-82915-4_2 · Cited by 7

Exploring Smartphone-Based Edge AI Inferences Using Real Testbeds

Hirsch, M., Mateos, C., & Majchrzak, T. A. (2025) · Sensors · doi:10.3390/s25092875 · Cited by 7

A Practical Guide for Ethical AI Product Development

Gupta, P., Kashiramka, S., & Barman, S. (2024) · IEEE Uttar Pradesh Section International Conference on Electrical, Electronics and Computer Engineering (UPCON) · doi:10.1109/upcon62832.2024.10983504 · Cited by 9

Comparative analysis of convolutional neural networks and traditional machine learning models for IVF live birth prediction: a retrospective analysis of 48514 IVF cycles and an evaluation of deployment feasibility in resource-constrained settings

Liu, Y., Wang, Y., Huang, K., Shi, H., Xin, H., Dai, S., Liu, J., Yang, X., Song, J., Zhang, F., et al. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1556681 · Cited by 5

“It's almost like Frankenstein”: Investigating the Complexities of Scientific Collaboration and Privilege Management within Research Computing Infrastructures

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