Check computer science papers: citations, claims and retractions

Supervised neural networks for the classification of structures

Sperduti, A., & Starita, A. (1997) · IEEE Transactions on Neural Networks

Neural Networks and ApplicationsMachine Learning and ELM

Cited by 666

The SEMAINE Database: Annotated Multimodal Records of Emotionally Colored Conversations between a Person and a Limited Agent

McKeown, G., Valstar, M., Cowie, R., Pantic, M., & Schroder, M. (2012) · IEEE Transactions on Affective Computing

Emotion and Mood RecognitionSocial Robot Interaction and HRI

Cited by 665

An Open-Source Benchmark Suite for Microservices and Their Hardware-Software Implications for Cloud & Edge Systems

Gan, Y., Zhang, Y., Cheng, D., Shetty, A., Rathi, P., Katarki, N., Bruno, A., Hu, J., Ritchken, B., Jackson, B., et al. (2019) · Proceedings of the Twenty-Fourth International Conference on Architectural Support for Programming Languages and Operating Systems

Cloud Computing and Resource ManagementSoftware System Performance and Reliability

Cited by 665

A Machine Learning Approach to Visual Perception of Forest Trails for Mobile Robots

Giusti, A., Guzzi, J., Ciresan, D. C., He, F.-L., Rodriguez, J. P., Fontana, F., Faessler, M., Forster, C., Schmidhuber, J., Caro, G. D., et al. (2016) · IEEE Robotics and Automation Letters

Advanced Neural Network ApplicationsRobotics and Sensor-Based Localization

Cited by 665

Three types of incremental learning

van de Ven, G. M., Tuytelaars, T., & Tolias, A. S. (2022) · Nature Machine Intelligence

Domain Adaptation and Few-Shot LearningData Stream Mining Techniques

Cited by 665

An efficient implementation of Reid's multiple hypothesis tracking algorithm and its evaluation for the purpose of visual tracking

Cox, I. J., & Hingorani, S. L. (1996) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Video Surveillance and Tracking MethodsTarget Tracking and Data Fusion in Sensor Networks

Cited by 665

Deep Hashing Network for Efficient Similarity Retrieval

Zhu, H., Long, M., Wang, J., & Cao, Y. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 664

Internet of things (IoT) applications to fight against COVID-19 pandemic

Singh, R. P., Javaid, M., Haleem, A., & Suman, R. (2020) · Diabetes & Metabolic Syndrome: Clinical Research & Reviews

Internet of Things and AIHealthcare during COVID-19 Pandemic

Cited by 664

Domain Generalization for Object Recognition with Multi-task Autoencoders

Ghifary, M., Kleijn, W. B., Zhang, M., & Balduzzi, D. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Domain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Cited by 664

Pareto Multi Objective Optimization

Ngatchou, P., Zarei, A., & El-Sharkawi, A. (2006) · Proceedings of the 13th International Conference on, Intelligent Systems Application to Power Systems

Advanced Multi-Objective Optimization AlgorithmsMetaheuristic Optimization Algorithms Research

Cited by 664

DÏoT: A Federated Self-learning Anomaly Detection System for IoT

Nguyen, T. D., Marchal, S., Miettinen, M., Fereidooni, H., Asokan, N., & Sadeghi, A.-R. (2019) · 2019 IEEE 39th International Conference on Distributed Computing Systems (ICDCS)

Network Security and Intrusion DetectionInternet Traffic Analysis and Secure E-voting

Cited by 663

Deep Learning with Convolutional Neural Network for Differentiation of Liver Masses at Dynamic Contrast-enhanced CT: A Preliminary Study

Yasaka, K., Akai, H., Abe, O., & Kiryu, S. (2018) · Radiology

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 663

Do Vision Transformers See Like Convolutional Neural Networks?

Raghu, M., Unterthiner, T., Kornblith, S., Zhang, C., & Dosovitskiy, A. (2021) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 663

Resilient Quantum Computation

Knill, E., Laflamme, R., & Zurek, W. H. (1998) · Science

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 663

Human-level performance in 3D multiplayer games with population-based reinforcement learning

Jaderberg, M., Czarnecki, W. M., Dunning, I., Marris, L., Lever, G., Castañeda, A. G., Beattie, C., Rabinowitz, N. C., Morcos, A. S., Ruderman, A., et al. (2019) · Science

Artificial Intelligence in GamesReinforcement Learning in Robotics

Cited by 662

Fused-layer CNN accelerators

Alwani, M., Chen, H., Ferdman, M., & Milder, P. (2016) · 2016 49th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

Advanced Neural Network ApplicationsEmbedded Systems Design Techniques

Cited by 662

Loss odyssey in medical image segmentation

Ma, J., Chen, J., Ng, M., Huang, R., Li, Y., Li, C., Yang, X., & Martel, A. L. (2021) · Medical Image Analysis

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 662

Deep learning in medical image registration: a review

Fu, Y., Lei, Y., Wang, T., Curran, W. J., Liu, T., & Yang, X. (2020) · Physics in Medicine & Biology

Medical Image Segmentation TechniquesArtificial Intelligence in Healthcare and Education

Cited by 661

Federated learning meets blockchain in edge computing: Opportunities and challenges

Nguyen, D. C., Ding, M., Pham, Q.-V., Pathirana, P. N., Le, L. B., Seneviratne, A., Li, J., Niyato, D., & Poor, H. V. (2021) · IEEE Internet of Things Journal

Privacy-Preserving Technologies in DataIoT and Edge/Fog Computing

Cited by 661

Knowledge Enhanced Contextual Word Representations

Peters, M. E., Neumann, M., Logan, R., Schwartz, R., Joshi, V., Singh, S., & Smith, N. A. (2019) · Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)

Topic ModelingNatural Language Processing Techniques

Cited by 661

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Chen, R. J., Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Lipkova, J., Noor, Z., Shaban, M., Shady, M., Williams, M., Joo, B., et al. (2022) · Cancer Cell

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 660

Expectation vs. Experience: Evaluating the Usability of Code Generation Tools Powered by Large Language Models

Vaithilingam, P., Zhang, T., & Glassman, E. L. (2022) · CHI Conference on Human Factors in Computing Systems Extended Abstracts

Software Engineering ResearchSoftware Engineering Techniques and Practices

Cited by 660

Old wine in new bottles or novel challenges: a critical analysis of empirical studies of user experience

Bargas-Avila, J. A., & Hornbæk, K. (2011) · Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionUsability and User Interface Design

Cited by 660

Dense Contrastive Learning for Self-Supervised Visual Pre-Training

Wang, X., Zhang, R., Shen, C., Kong, T., & Li, L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 660