Check computer science papers: citations, claims and retractions

LXMERT: Learning Cross-Modality Encoder Representations from Transformers

Tan, H., & Bansal, M. (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)

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 2,319

Where Is Current Research on Blockchain Technology?—A Systematic Review

Yli-Huumo, J., Ko, D., Choi, S., Park, S., & Smolander, K. (2016) · PLOS ONE

Blockchain Technology Applications and SecurityBlockchain Technology in Education and Learning

Cited by 2,317

Weighted Nuclear Norm Minimization with Application to Image Denoising

Gu, S., Zhang, L., Zuo, W., & Feng, X. (2014) · 2014 IEEE Conference on Computer Vision and Pattern Recognition

Image and Signal Denoising MethodsSparse and Compressive Sensing Techniques

Cited by 2,316

On lattices, learning with errors, random linear codes, and cryptography

Regev, O. (2009) · Journal of the ACM

Cryptography and Data SecurityQuantum Information and Cryptography

Cited by 2,315

Brave new world: service robots in the frontline

Wirtz, J., Patterson, P. G., Kunz, W. H., Gruber, T., Lu, V. N., Paluch, S., & Martins, A. (2018) · Journal of Service Management

AI in Service InteractionsRobotics and Automated Systems

Cited by 2,315

p-σ-π Analysis. A Method for the Correlation of Biological Activity and Chemical Structure

Hansch, C., & Fujita, T. (1964) · Journal of the American Chemical Society

Computational Drug Discovery MethodsChemical Reaction Mechanisms

Cited by 2,313

ConceptNet 5.5: An Open Multilingual Graph of General Knowledge

Speer, R., Chin, J., & Havasi, C. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence

Topic ModelingNatural Language Processing Techniques

Cited by 2,312

Catastrophic forgetting in connectionist networks

French, R. (1999) · Trends in Cognitive Sciences

Domain Adaptation and Few-Shot LearningNeural Networks and Applications

Cited by 2,311

Opportunities and obstacles for deep learning in biology and medicine

Ching, T., Himmelstein, D. S., Beaulieu-Jones, B. K., Kalinin, A. A., Do, B. T., Way, G. P., Ferrero, E., Agapow, P.-M., Zietz, M., Hoffman, M. M., et al. (2018) · Journal of The Royal Society Interface

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 2,310

Pre-Trained Image Processing Transformer

Chen, H., Wang, Y., Guo, T., Xu, C., Deng, Y., Liu, Z., Ma, S., Xu, C., Xu, C., & Gao, W. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Image Processing TechniquesImage and Signal Denoising Methods

Cited by 2,308

GCNet: Non-Local Networks Meet Squeeze-Excitation Networks and Beyond

Cao, Y., Xu, J., Lin, S., Wei, F., & Hu, H. (2019) · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 2,307

Induction: Processes of Inference, Learning, and Discovery

Holland, J. H., Holyoak, K. J., Nisbett, R. E., Thagard, P. R., & Smoliar, S. W. (1987) · IEEE Expert

AI-based Problem Solving and PlanningLogic, Reasoning, and Knowledge

Cited by 2,305

The Experience of Presence: Factor Analytic Insights

Schubert, T., Friedmann, F., & Regenbrecht, H. (2001) · Presence: Teleoperators and Virtual Environments

Virtual Reality Applications and ImpactsFlow Experience in Various Fields

Cited by 2,303

Topology and data

Carlsson, G. (2009) · Bulletin of the American Mathematical Society

Topological and Geometric Data AnalysisGeometric and Algebraic Topology

Cited by 2,302

Time series classification from scratch with deep neural networks: A strong baseline

Wang, Z., Yan, W., & Oates, T. (2017) · 2017 International Joint Conference on Neural Networks (IJCNN)

Time Series Analysis and ForecastingMusic and Audio Processing

Cited by 2,299

DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation

Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., & Aberman, K. (2023) · 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications

Cited by 2,295

Video Swin Transformer

Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2021) · arXiv

Human Pose and Action RecognitionAdvanced Neural Network Applications

Cited by 2,294

Accurate, Large Minibatch SGD: Training ImageNet in 1 Hour

Goyal, P., Dollár, P., Girshick, R., Noordhuis, P., Wesolowski, L., Kyrola, A., Tulloch, A., Jia, Y., & He, K. (2017) · arXiv

Stochastic Gradient Optimization TechniquesParallel Computing and Optimization Techniques

Cited by 2,294

Video Swin Transformer

Liu, Z., Ning, J., Cao, Y., Wei, Y., Zhang, Z., Lin, S., & Hu, H. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Human Pose and Action RecognitionAdvanced Neural Network Applications

Cited by 2,294

SemanticKITTI: A Dataset for Semantic Scene Understanding of LiDAR Sequences

Behley, J., Garbade, M., Milioto, A., Quenzel, J., Behnke, S., Stachniss, C., & Gall, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAutonomous Vehicle Technology and Safety

Cited by 2,294

A survey of mobile phone sensing

Lane, N., Miluzzo, E., Lu, H., Peebles, D., Choudhury, T., & Campbell, A. (2010) · IEEE Communications Magazine

Mobile Crowdsensing and CrowdsourcingContext-Aware Activity Recognition Systems

Cited by 2,292

Unbiased average age-appropriate atlases for pediatric studies

Fonov, V., Evans, A. C., Botteron, K., Almli, C. R., McKinstry, R. C., & Collins, D. L. (2011) · NeuroImage

Medical Image Segmentation TechniquesAdvanced MRI Techniques and Applications

Cited by 2,290

Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering

Goyal, Y., Khot, T., Summers-Stay, D., Batra, D., & Parikh, D. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Multimodal Machine Learning ApplicationsVisual Attention and Saliency Detection

Cited by 2,287

A Bayesian model of shape and appearance for subcortical brain segmentation

Patenaude, B., Smith, S. M., Kennedy, D. N., & Jenkinson, M. (2011) · NeuroImage

Medical Image Segmentation TechniquesBrain Tumor Detection and Classification

Cited by 2,287