Check computer science papers: citations, claims and retractions

Quantum Walk in Position Space with Single Optically Trapped Atoms

Karski, M., Förster, L., Choi, J.-M., Steffen, A., Alt, W., Meschede, D., & Widera, A. (2009) · Science

Quantum Computing Algorithms and ArchitectureCold Atom Physics and Bose-Einstein Condensates

Cited by 659

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

Ni, J., Zhang, K., Lin, X., & Shen, X. (2018) · IEEE Communications Surveys & Tutorials

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 659

Aleatoric uncertainty estimation with test-time augmentation for medical image segmentation with convolutional neural networks

Wang, G., Li, W., Aertsen, M., Deprest, J., Ourselin, S., & Vercauteren, T. (2019) · Neurocomputing

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 659

The seven tools of causal inference, with reflections on machine learning

Pearl, J. (2019) · Communications of the ACM

Bayesian Modeling and Causal InferenceLogic, Reasoning, and Knowledge

Cited by 659

Best practices in eye tracking research

Carter, B. T., & Luke, S. G. (2020) · International Journal of Psychophysiology

Gaze Tracking and Assistive TechnologyVisual Attention and Saliency Detection

Cited by 659

An Empirical Study of Spatial Attention Mechanisms in Deep Networks

Zhu, X., Cheng, D., Zhang, Z., Lin, S., & Dai, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 659

MOTS: Multi-Object Tracking and Segmentation

Voigtlaender, P., Krause, M., Osep, A., Luiten, J., Sekar, B. B. G., Geiger, A., & Leibe, B. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Video Surveillance and Tracking MethodsAdvanced Neural Network Applications

Cited by 658

Fifty Years of Moore's Law

Mack, C. A. (2011) · IEEE Transactions on Semiconductor Manufacturing

History of Computing TechnologiesAdvancements in Semiconductor Devices and Circuit Design

Cited by 658

The Insight ToolKit image registration framework

Avants, B. B., Tustison, N. J., Stauffer, M., Song, G., Wu, B., & Gee, J. C. (2014) · Frontiers in Neuroinformatics

Medical Image Segmentation TechniquesAdvanced Radiotherapy Techniques

Cited by 658

A Discourse-Aware Attention Model for Abstractive Summarization of Long Documents

Cohan, A., Dernoncourt, F., Kim, D. S., Bui, T., Kim, S., Chang, W., & Goharian, N. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)

Topic ModelingAdvanced Text Analysis Techniques

Cited by 658

Captum: A unified and generic model interpretability library for PyTorch

Kokhlikyan, N., Miglani, V., Martin, M., Wang, E., Alsallakh, B., Reynolds, J., Melnikov, A., Kliushkina, N., Araya, C., Yan, S., et al. (2020) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 658

Holographic reduced representations

Plate, T. A. (1995) · IEEE Transactions on Neural Networks

Neural Networks and ApplicationsCognitive Computing and Networks

Cited by 657

Deep Learning for Emotion Recognition on Small Datasets using Transfer Learning

Ng, H.-W., Nguyen, V. D., Vonikakis, V., & Winkler, S. (2015) · Proceedings of the 2015 ACM on International Conference on Multimodal Interaction

Emotion and Mood RecognitionFace recognition and analysis

Cited by 657

DiffusionDet: Diffusion Model for Object Detection

Chen, S., Sun, P., Song, Y., & Luo, P. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 657

Emergence of Locomotion Behaviours in Rich Environments

Heess, N., TB, D., Sriram, S., Lemmon, J., Merel, J., Wayne, G., Tassa, Y., Erez, T., Wang, Z., Eslami, S. M. A., et al. (2017) · arXiv

Reinforcement Learning in RoboticsRobotic Locomotion and Control

Cited by 657

Modeling Relation Paths for Representation Learning of Knowledge Bases

Lin, Y., Liu, Z., Luan, H., Sun, M., Rao, S., & Liu, S. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Advanced Graph Neural NetworksTopic Modeling

Cited by 657

UberNet: Training a Universal Convolutional Neural Network for Low-, Mid-, and High-Level Vision Using Diverse Datasets and Limited Memory

Kokkinos, I. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 657

What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic properties

Conneau, A., Kruszewski, G., Lample, G., Barrault, L., & Baroni, M. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 657

On the Automatic Generation of Medical Imaging Reports

Jing, B., Xie, P., & Xing, E. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Multimodal Machine Learning ApplicationsArtificial Intelligence in Healthcare and Education

Cited by 657

Syntax-guided synthesis

Alur, R., Bodik, R., Juniwal, G., Martin, M. M. K., Raghothaman, M., Seshia, S. A., Singh, R., Solar-Lezama, A., Torlak, E., & Udupa, A. (2013) · 2013 Formal Methods in Computer-Aided Design

Logic, programming, and type systemsFormal Methods in Verification

Cited by 656

PointFlow: 3D Point Cloud Generation With Continuous Normalizing Flows

Yang, G., Huang, X., Hao, Z., Liu, M.-Y., Belongie, S., & Hariharan, B. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

3D Shape Modeling and AnalysisGenerative Adversarial Networks and Image Synthesis

Cited by 656

Adversarial NLI: A New Benchmark for Natural Language Understanding

Nie, Y., Williams, A., Dinan, E., Bansal, M., Weston, J., & Kiela, D. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 656

A Bayesian foundation for individual learning under uncertainty

Mathys, C. (2011) · Frontiers in Human Neuroscience

Reinforcement Learning in RoboticsNeural and Behavioral Psychology Studies

Cited by 655

The Power of Ensembles for Active Learning in Image Classification

Beluch, W. H., Genewein, T., Nurnberger, A., & Kohler, J. M. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

Machine Learning and AlgorithmsDomain Adaptation and Few-Shot Learning

Cited by 655