Check computer science papers: citations, claims and retractions

Deep learning in computer vision: A critical review of emerging techniques and application scenarios

Chai, J., Zeng, H., Li, A., & Ngai, E. W. T. (2021) · Machine Learning with Applications

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 754

Neural Speech Synthesis with Transformer Network

Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 754

Automated medical image segmentation techniques

Sharma, N., & Aggarwal, L. (2010) · Journal of Medical Physics

Medical Image Segmentation TechniquesMedical Imaging and Analysis

Cited by 754

Nonparametric maximum likelihood estimation of a mixing distribution

Laird, N. (1978) · Journal of the American Statistical Association

Bayesian Methods and Mixture ModelsStatistical Distribution Estimation and Applications

Cited by 753

Computation Over Multiple-Access Channels

Nazer, B., & Gastpar, M. (2007) · IEEE Transactions on Information Theory

Cooperative Communication and Network CodingError Correcting Code Techniques

Cited by 753

Quantizing deep convolutional networks for efficient inference: A whitepaper

Krishnamoorthi, R. (2018) · arXiv

Advanced Neural Network ApplicationsLow-power high-performance VLSI design

Cited by 753

Stabilizer Codes and Quantum Error Correction

Gottesman, D. (1997) · arXiv

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 753

Machine Learning: The Art and Science of Algorithms that Make Sense of Data

Flach, P. (2012) · Cambridge University Press eBooks

Machine Learning and AlgorithmsData Mining and Machine Learning Applications

Cited by 753

A survey on software-defined network and openflow: From concept to implementation

Hu, F., Hao, Q., & Bao, K. (2014) · IEEE Communications Surveys & Tutorials

Software-Defined Networks and 5GAdvanced Optical Network Technologies

Cited by 752

Rep ViT: Revisiting Mobile CNN From ViT Perspective

Wang, A., Chen, H., Lin, Z., Han, J., & Ding, G. (2024) · 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 752

Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?

Min, S., Lyu, X., Holtzman, A., Artetxe, M., Lewis, M., Hajishirzi, H., & Zettlemoyer, L. (2022) · arXiv

Topic ModelingDomain Adaptation and Few-Shot Learning

Cited by 752

Towards an Optimized BlockChain for IoT

Dorri, A., Kanhere, S. S., & Jurdak, R. (2017) · Proceedings of the Second International Conference on Internet-of-Things Design and Implementation

Blockchain Technology Applications and SecurityIoT and Edge/Fog Computing

Cited by 752

New Approaches to Robotics

Brooks, R. A. (1991) · Science

AI-based Problem Solving and PlanningReinforcement Learning in Robotics

Cited by 752

End-to-End Multimodal Emotion Recognition using Deep Neural Networks

Tzirakis, P., Trigeorgis, G., Nicolaou, M. A., Schuller, B., & Zafeiriou, S. (2017) · arXiv

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 752

Transformer-based unsupervised contrastive learning for histopathological image classification

Wang, X., Yang, S., Zhang, J., Wang, M., Zhang, J., Yang, W., Huang, J., & Han, X. (2022) · Medical Image Analysis

AI in cancer detectionDomain Adaptation and Few-Shot Learning

Cited by 751

CTRL: A Conditional Transformer Language Model for Controllable Generation

Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., & Socher, R. (2019) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 751

Anatomically constrained neural networks (ACNNS): application to cardiac image enhancement and segmentation

Oktay, O., Ferrante, E., Kamnitsas, K., Heinrich, M., Bai, W., Caballero, J., Cook, S. A., de Marvao, A., Dawes, T., O'Regan, D. P., et al. (2018) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 751

The dataflow model: A practical approach to balancing correctness, latency, and cost in massive-scale, unbounded, out-of-order data processing

Akidau, T., Bradshaw, R., Chambers, C., Chernyak, S., Fernández-Moctezuma, R. J., Lax, R., McVeety, S., Mills, D., Perry, F., Schmidt, E., et al. (2015) · Proceedings of the VLDB Endowment

Advanced Database Systems and QueriesCloud Computing and Resource Management

Cited by 750

Algorithmic fairness in artificial intelligence for medicine and healthcare

Chen, R. J., Wang, J. J., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Lu, M. Y., Sahai, S., & Mahmood, F. (2023) · Nature Biomedical Engineering

Ethics and Social Impacts of AIArtificial Intelligence in Healthcare and Education

Cited by 750

AutoTutor: an intelligent tutoring system with mixed-initiative dialogue.

Graesser, A. C., Chipman, P., Haynes, B. C., & Olney, A. (2005) · IEEE Transactions on Education

Intelligent Tutoring Systems and Adaptive LearningInnovative Teaching and Learning Methods

Cited by 750

Certified Robustness to Adversarial Examples with Differential Privacy

Lecuyer, M., Atlidakis, V., Geambasu, R., Hsu, D., & Jana, S. (2019) · 2019 IEEE Symposium on Security and Privacy (SP)

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 750

Deep neural network models for computational histopathology: A survey

Srinidhi, C. L., Ciga, O., & Martel, A. L. (2021) · Medical Image Analysis

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 749

From Eliza to XiaoIce: Challenges and opportunities with social chatbots

Shum, H.-Y., He, X.-D., & Li, D. (2018) · Frontiers of Information Technology & Electronic Engineering

Speech and dialogue systemsAI in Service Interactions

Cited by 749

Machine Learning for Anomaly Detection: A Systematic Review

Nassif, A. B., Talib, M. A., Nasir, Q., & Dakalbab, F. M. (2021) · IEEE Access

Anomaly Detection Techniques and ApplicationsSoftware System Performance and Reliability

Cited by 748