The MNIST Database of Handwritten Digit Images for Machine Learning Research [Best of the Web]

Gradient-based learning applied to document recognition

Lecun, Y., Bottou, L., Bengio, Y., & Haffner, P. (1998) · Proceedings of the IEEE · doi:10.1109/5.726791 · Cited by 58,805

What is the best multi-stage architecture for object recognition?

Jarrett, K., Kavukcuoglu, K., Ranzato, M. A., & LeCun, Y. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459469 · Cited by 2,153

Untitled work

(2011) · doi:10.5591/978-1-57735-516-8/ijcai11-210 · Cited by 1,229

Recent advances in convolutional neural networks

Gu, J., Wang, Z., Kuen, J., Ma, L., Shahroudy, A., Shuai, B., Liu, T., Wang, X., Wang, G., Cai, J., et al. (2018) · Pattern Recognition · doi:10.1016/j.patcog.2017.10.013 · Cited by 6,576

A survey of the recent architectures of deep convolutional neural networks

Khan, A., Sohail, A., Zahoora, U., & Qureshi, A. S. (2020) · Artificial Intelligence Review · doi:10.1007/s10462-020-09825-6 · Cited by 3,137

Decentralized federated learning: Fundamentals, state of the art, frameworks, trends, and challenges

Martínez Beltrán, E. T., Pérez, M. Q., Sánchez, P. M. S., Bernal, S. L., Bovet, G., Pérez, M. G., Pérez, G. M., & Celdrán, A. H. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3315746 · Cited by 625

The Specious Art of Single-Cell Genomics

Chari, T., & Pachter, L. (2021) · doi:10.1101/2021.08.25.457696 · Cited by 379

Towards Visually Explaining Variational Autoencoders

Liu, W., Li, R., Zheng, M., Karanam, S., Wu, Z., Bhanu, B., Radke, R. J., & Camps, O. (2020) · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr42600.2020.00867 · Cited by 294

AI Art and its Impact on Artists

Jiang, H. H., Brown, L., Cheng, J., Khan, M., Gupta, A., Workman, D., Hanna, A., Flowers, J., & Gebru, T. (2023) · Proceedings of the 2023 AAAI/ACM Conference on AI, Ethics, and Society · doi:10.1145/3600211.3604681 · Cited by 265

Privacy-Preserving Federated Learning for Internet of Medical Things Under Edge Computing

Wang, R., Lai, J., Zhang, Z., Li, X., Vijayakumar, P., & Karuppiah, M. (2023) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2022.3157725 · Cited by 203

The METRIC-framework for assessing data quality for trustworthy AI in medicine: A systematic review

Schwabe, D., Becker, K., Seyferth, M., Klaß, A., & Schaeffter, T. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01196-4 · Cited by 169

Evaluation of parameterized quantum circuits: on the relation between classification accuracy, expressibility, and entangling capability

Hubregtsen, T., Pichlmeier, J., Stecher, P., & Bertels, K. (2021) · Quantum Machine Intelligence · doi:10.1007/s42484-021-00038-w · Cited by 135

A survey on optimization techniques for edge artificial intelligence (ai)

Surianarayanan, C., Lawrence, J. J., Chelliah, P. R., Prakash, E., & Hewage, C. (2023) · Sensors · doi:10.3390/s23031279 · Cited by 131

Backdoor attacks and defenses in federated learning: Survey, challenges and future research directions

Nguyen, T. D., Nguyen, T., Nguyen, P. L., Pham, H. H., Doan, K. D., & Wong, K.-S. (2024) · Engineering Applications of Artificial Intelligence · doi:10.1016/j.engappai.2023.107166 · Cited by 130

When deep learning meets edge computing

Huang, Y., Ma, X., Fan, X., Liu, J., & Gong, W. (2017) · 2017 IEEE 25th International Conference on Network Protocols (ICNP) · doi:10.1109/icnp.2017.8117585 · Cited by 89

Record and Reward Federated Learning Contributions with Blockchain

Martinez, I., Francis, S., & Hafid, A. S. (2019) · 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC) · doi:10.1109/cyberc.2019.00018 · Cited by 92

Self-consuming generative models go MAD

Casco-Rodriguez, J., Alemohammad, S., Luzi, L., Imtiaz, A., Babaei, H., LeJeune, D., Siahkoohi, A., & Baraniuk, R. (2023) · LatinX in AI at Neural Information Processing Systems Conference 2023 · doi:10.52591/lxai202312101 · Cited by 83

Free-riders in Federated Learning: Attacks and Defenses

Lin, J., Du, M., & Liu, J. (2019) · arXiv · doi:10.48550/arxiv.1911.12560 · Cited by 76

Stacked one-class broad learning system for intrusion detection in industry 4.0

Yang, K., Shi, Y., Yu, Z., Yang, Q., Sangaiah, A. K., & Zeng, H. (2023) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2022.3157727 · Cited by 75

Averaging Is Probably Not the Optimum Way of Aggregating Parameters in Federated Learning

Xiao, P., Cheng, S., Stankovic, V., & Vukobratovic, D. (2020) · Entropy · doi:10.3390/e22030314 · Cited by 54

Efficient edge-ai application deployment for fpgas

Kalapothas, S., Flamis, G., & Kitsos, P. (2022) · Information · doi:10.3390/info13060279 · Cited by 53

DeSMP: Differential Privacy-exploited Stealthy Model Poisoning Attacks in Federated Learning

Hossain, M. T., Islam, S., Badsha, S., & Shen, H. (2021) · 2021 17th International Conference on Mobility, Sensing and Networking (MSN) · doi:10.1109/msn53354.2021.00038 · Cited by 44

Quantum-train: Rethinking hybrid quantum-classical machine learning in the model compression perspective

Liu, C.-Y., Kuo, E.-J., Abraham Lin, C.-H., Gemsun Young, J., Chang, Y.-J., Hsieh, M.-H., & Goan, H.-S. (2025) · Quantum Machine Intelligence · doi:10.1007/s42484-025-00305-0 · Cited by 40

Data-Agnostic Model Poisoning Against Federated Learning: A Graph Autoencoder Approach

Li, K., Zheng, J., Yuan, X., Ni, W., Akan, O. B., & Poor, H. V. (2024) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2024.3362147 · Cited by 41

A Deep Look into Logarithmic Quantization of Model Parameters in Neural Networks

Cai, J., Takemoto, M., & Nakajo, H. (2018) · Proceedings of the 10th International Conference on Advances in Information Technology · doi:10.1145/3291280.3291800 · Cited by 31

Representations and generalization in artificial and brain neural networks

Li, Q., Sorscher, B., & Sompolinsky, H. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2311805121 · Cited by 27

Dispersed Pixel Perturbation-Based Imperceptible Backdoor Trigger for Image Classifier Models

Wang, Y., Zhao, M., Li, S., Yuan, X., & Ni, W. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3202687 · Cited by 26

Color for Characters - Effects of Visual Explanations of AI on Trust and Observability

Schrills, T., & Franke, T. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-50334-5_8 · Cited by 17

Automating Analogue AI Chip Design with Genetic Search

Krestinskaya, O., Salama, K. N., & James, A. P. (2020) · Advanced Intelligent Systems · doi:10.1002/aisy.202000075 · Cited by 14

Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks

Li, X., Xiang, Z., Miller, D. J., & Kesidis, G. (2022) · ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp43922.2022.9746573 · Cited by 11

Defending local poisoning attacks in multi-party learning via immune system

Xie, F., Gao, Y., Wang, J., & Zhao, W. (2022) · Knowledge-Based Systems · doi:10.1016/j.knosys.2021.107850 · Cited by 9

Efficient and persistent backdoor attack by boundary trigger set constructing against federated learning

Yang, D., Luo, S., Zhou, J., Pan, L., Yang, X., & Xing, J. (2023) · Information Sciences · doi:10.1016/j.ins.2023.119743 · Cited by 9

DeepGuard: Backdoor Attack Detection and Identification Schemes in Privacy-Preserving Deep Neural Networks

Chen, C., Wei, L., Zhang, L., Peng, Y., & Ning, J. (2022) · Security and Communication Networks · doi:10.1155/2022/2985308 · Cited by 8

The Cambridge Law Corpus: A Dataset for Legal AI Research

Östling, A., Sargeant, H., Xie, H., Bull, L., Terenin, A., Jonsson, L., Magnusson, M., & Steffek, F. (2024) · doi:10.2139/ssrn.4763429 · Cited by 8

A lightweight backdoor defense framework based on image inpainting

Wei, Y., Gao, H., Wang, Y., Gao, Y., & Liu, H. (2023) · Neurocomputing · doi:10.1016/j.neucom.2023.03.052 · Cited by 6