Check computer science papers: citations, claims and retractions

Blockchain for smart cities: A review of architectures, integration trends and future research directions

Bhushan, B., Khamparia, A., Sagayam, K. M., Sharma, S. K., Ahad, M. A., & Debnath, N. C. (2020) · Sustainable Cities and Society

Blockchain Technology Applications and SecuritySmart Cities and Technologies

Cited by 353

The hateful memes challenge: Detecting hate speech in multimodal memes

Kiela, D., Firooz, H., Mohan, A., Goswami, V., Singh, A., Ringshia, P., & Testuggine, D. (2020) · arXiv

Hate Speech and Cyberbullying DetectionSentiment Analysis and Opinion Mining

Cited by 352

The unreasonable effectiveness of deep learning in artificial intelligence

Sejnowski, T. J. (2020) · Proceedings of the National Academy of Sciences

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 352

TapNet: Multivariate Time Series Classification with Attentional Prototypical Network

Zhang, X., Gao, Y., Lin, J., & Lu, C.-T. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence

Time Series Analysis and ForecastingMusic and Audio Processing

Cited by 352

Simulating Chemistry Using Quantum Computers

Kassal, I., Whitfield, J. D., Perdomo-Ortiz, A., Yung, M.-H., & Aspuru-Guzik, A. (2011) · Annual Review of Physical Chemistry

Quantum Computing Algorithms and ArchitectureAdvanced Chemical Physics Studies

Cited by 352

Deep Learning in Mammography: Diagnostic Accuracy of a Multipurpose Image Analysis Software in the Detection of Breast Cancer

Becker, A. S., Marcon, M., Ghafoor, S., Wurnig, M. C., Frauenfelder, T., & Boss, A. (2017) · Investigative Radiology

AI in cancer detectionDigital Radiography and Breast Imaging

Cited by 352

Decision strategies and susceptibility to phishing

Downs, J. S., Holbrook, M. B., & Cranor, L. F. (2006) · Proceedings of the second symposium on Usable privacy and security - SOUPS '06

Spam and Phishing DetectionUser Authentication and Security Systems

Cited by 352

Biologically plausible error-driven learning using local activation differences: the generalized recirculation algorithm

O'Reilly, R. C. (1996) · Neural Computation

Neural Networks and ApplicationsNeural dynamics and brain function

Cited by 352

5G-based smart healthcare network: architecture, taxonomy, challenges and future research directions

Ahad, A., Tahir, M., & Yau, K.-L. A. (2019) · IEEE Access

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 352

Vision meets drones: A challenge

Zhu, P., Wen, L., Bian, X., Ling, H., & Hu, Q. (2018) · arXiv

Advanced Neural Network ApplicationsVideo Surveillance and Tracking Methods

Cited by 352

A Review of Trustworthy and Explainable Artificial Intelligence (XAI)

Chamola, V., Hassija, V., Sulthana, A. R., Ghosh, D., Dhingra, D., & Sikdar, B. (2023) · IEEE Access

Explainable Artificial Intelligence (XAI)Ethics and Social Impacts of AI

Cited by 352

Sensor models and multisensor integration

Durrant-Whyte, H. F. (1988) · The International Journal of Robotics Research

Target Tracking and Data Fusion in Sensor NetworksRobotics and Sensor-Based Localization

Cited by 352

Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection

Kim, T., Jeong, M., Kim, S., Choi, S., & Kim, C. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 352

Embodied intelligence via learning and evolution

Gupta, A., Savarese, S., Ganguli, S., & Fei-Fei, L. (2021) · Nature Communications

Reinforcement Learning in RoboticsEvolutionary Algorithms and Applications

Cited by 352

Towards multi-modal causability with graph neural networks enabling information fusion for explainable ai

Holzinger, A., Malle, B., Saranti, A., & Pfeifer, B. (2021) · Information Fusion

Explainable Artificial Intelligence (XAI)Multimodal Machine Learning Applications

Cited by 352

A universal qudit quantum processor with trapped ions

Ringbauer, M., Meth, M., Postler, L., Stricker, R., Blatt, R., Schindler, P., & Monz, T. (2022) · Nature Physics

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 351

A Theoretical Analysis of NDCG Type Ranking Measures

Wang, Y., Wang, L., Li, Y., He, D., Liu, T.-Y., & Chen, W. (2013) · arXiv

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 351

How Amazon web services uses formal methods

Newcombe, C., Rath, T., Zhang, F., Munteanu, B., Brooker, M., & Deardeuff, M. (2015) · Communications of the ACM

Formal Methods in VerificationDistributed systems and fault tolerance

Cited by 351

R3: Reinforced Ranker-Reader for Open-Domain Question Answering

Wang, S., Yu, M., Guo, X., Wang, Z., Klinger, T., Zhang, W., Chang, S., Tesauro, G., Zhou, B., & Jiang, J. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingInformation Retrieval and Search Behavior

Cited by 351

When to Trust Your Model: Model-Based Policy Optimization

Janner, M., Fu, J., Zhang, M., & Levine, S. (2019) · arXiv

Reinforcement Learning in RoboticsAdvanced Bandit Algorithms Research

Cited by 351

Privacy-Enhanced Federated Learning Against Poisoning Adversaries

Liu, X., Li, H., Xu, G., Chen, Z., Huang, X., & Lu, R. (2021) · IEEE Transactions on Information Forensics and Security

Privacy-Preserving Technologies in DataAdversarial Robustness in Machine Learning

Cited by 351

CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances

Tack, J., Mo, S., Jeong, J., & Shin, J. (2020) · arXiv

Anomaly Detection Techniques and ApplicationsAdversarial Robustness in Machine Learning

Cited by 351

Discourse Segmentation of Multi-Party Conversation

Galley, M., McKeown, K., Fosler-Lussier, E., & Jing, H. (2003) · Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - ACL '03

Speech Recognition and SynthesisSpeech and dialogue systems

Cited by 351

GeniePath: Graph Neural Networks with Adaptive Receptive Paths

Liu, Z., Chen, C., Li, L., Zhou, J., Li, X., Song, L., & Qi, Y. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Graph Neural NetworksGraph Theory and Algorithms

Cited by 350