Check computer science papers: citations, claims and retractions

Maxout Networks

Goodfellow, I. J., Warde-Farley, D., Mirza, M., Courville, A., & Bengio, Y. (2013) · arXiv

Neural Networks and ApplicationsAdvanced Neural Network Applications

Cited by 935

Learning Spatial-Temporal Regularized Correlation Filters for Visual Tracking

Li, F., Tian, C., Zuo, W., Zhang, L., & Yang, M.-H. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Video Surveillance and Tracking MethodsAdvanced Image and Video Retrieval Techniques

Cited by 935

Edge-centric Computing: Vision and Challenges

Garcia Lopez, P., Montresor, A., Epema, D., Datta, A., Higashino, T., Iamnitchi, A., Barcellos, M., Felber, P., & Riviere, E. (2015) · ACM SIGCOMM Computer Communication Review

IoT and Edge/Fog ComputingEnergy Efficiency in Computing

Cited by 934

Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines

Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I., & Lungren, M. P. (2020) · npj Digital Medicine

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 934

HyperNet: Towards Accurate Region Proposal Generation and Joint Object Detection

Kong, T., Yao, A., Chen, Y., & Sun, F. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 934

Detecting hallucinations in large language models using semantic entropy

Farquhar, S., Kossen, J., Kuhn, L., & Gal, Y. (2024) · Nature

Topic ModelingAdversarial Robustness in Machine Learning

Cited by 934

Sensor mania! the internet of things, wearable computing, objective metrics, and the quantified self 2.0

Swan, M. (2012) · Journal of Sensor and Actuator Networks

Internet of Things and AIIoT and Edge/Fog Computing

Cited by 934

The Little Engine That Could: Regularization by Denoising (RED)

Romano, Y., Elad, M., & Milanfar, P. (2017) · SIAM Journal on Imaging Sciences

Image and Signal Denoising MethodsNumerical methods in inverse problems

Cited by 934

Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer

Shazeer, N., Mirhoseini, A., Maziarz, K., Davis, A., Le, Q., Hinton, G., & Dean, J. (2017) · arXiv

Topic ModelingAdvanced Neural Network Applications

Cited by 933

Deep Supervised Hashing for Fast Image Retrieval

Liu, H., Wang, R., Shan, S., & Chen, X. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 933

Deep learning in cancer diagnosis, prognosis and treatment selection

Tran, K. A., Kondrashova, O., Bradley, A., Williams, E. D., Pearson, J. V., & Waddell, N. (2021) · Genome Medicine

AI in cancer detectionArtificial Intelligence in Healthcare and Education

Cited by 932

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · Advances in Neural Information Processing Systems 35

Topic ModelingNatural Language Processing Techniques

Cited by 932

Deep Anomaly Detection with Outlier Exposure

Hendrycks, D., Mazeika, M., & Dietterich, T. (2018) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 932

The Quantified Self: Fundamental Disruption in Big Data Science and Biological Discovery

Swan, M. (2013) · Big Data

Innovative Human-Technology InteractionBig Data Technologies and Applications

Cited by 931

Generalization of back-propagation to recurrent neural networks

Pineda, F. J. (1987) · Physical Review Letters

Neural Networks and ApplicationsNeural Networks and Reservoir Computing

Cited by 931

Deep learning algorithms for human activity recognition using mobile and wearable sensor networks: State of the art and research challenges

Nweke, H. F., Teh, Y. W., Al-garadi, M. A., & Alo, U. R. (2018) · Expert Systems with Applications

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 930

Effectiveness of Intelligent Tutoring Systems: A Meta-Analytic Review

Kulik, J. A., & Fletcher, J. D. (2016) · Review of Educational Research

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 930

Quantum Walks of Correlated Photons

Peruzzo, A., Lobino, M., Matthews, J. C. F., Matsuda, N., Politi, A., Poulios, K., Zhou, X.-Q., Lahini, Y., Ismail, N., Wörhoff, K., et al. (2010) · Science

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 929

Adversarially Regularized Graph Autoencoder for Graph Embedding

Pan, S., Hu, R., Long, G., Jiang, J., Yao, L., & Zhang, C. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 929

Training Compute-Optimal Large Language Models

Hoffmann, J., Borgeaud, S., Mensch, A., Buchatskaya, E., Cai, T., Rutherford, E., Casas, D. D. L., Hendricks, L. A., Welbl, J., Clark, A., et al. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 929

Edge AI: On-Demand Accelerating Deep Neural Network Inference via Edge Computing

Li, E., Zeng, L., Zhou, Z., & Chen, X. (2020) · IEEE Transactions on Wireless Communications

IoT and Edge/Fog ComputingAdvanced Neural Network Applications

Cited by 929

Using recurrent neural network models for early detection of heart failure onset

Choi, E., Schuetz, A., Stewart, W. F., & Sun, J. (2017) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareArtificial Intelligence in Healthcare

Cited by 928

Invariant Risk Minimization

Arjovsky, M., Bottou, L., Gulrajani, I., & Lopez-Paz, D. (2019) · arXiv

Domain Adaptation and Few-Shot LearningMachine Learning and Algorithms

Cited by 927

Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization

Wang, J., Liu, Q., Liang, H., Joshi, G., & Poor, H. V. (2020) · arXiv

Privacy-Preserving Technologies in DataStochastic Gradient Optimization Techniques

Cited by 927