Check computer science papers: citations, claims and retractions

Domain randomization for transferring deep neural networks from simulation to the real world

Tobin, J., Fong, R., Ray, A., Schneider, J., Zaremba, W., & Abbeel, P. (2017) · 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Reinforcement Learning in RoboticsRobot Manipulation and Learning

Cited by 3,124

Towards A Rigorous Science of Interpretable Machine Learning

Doshi-Velez, F., & Kim, B. (2017) · arXiv

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 3,121

Prioritized Experience Replay

Schaul, T., Quan, J., Antonoglou, I., & Silver, D. (2015) · arXiv

Reinforcement Learning in RoboticsArtificial Intelligence in Games

Cited by 3,120

The future of digital health with federated learning

Rieke, N., Hancox, J., Li, W., Milletarì, F., Roth, H. R., Albarqouni, S., Bakas, S., Galtier, M. N., Landman, B. A., Maier-Hein, K., et al. (2020) · npj Digital Medicine

Machine Learning in HealthcarePrivacy-Preserving Technologies in Data

Cited by 3,119

Continual lifelong learning with neural networks: A review

Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks

Domain Adaptation and Few-Shot LearningReinforcement Learning in Robotics

Cited by 3,117

Training Language Models to Follow Instructions with Human Feedback

Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., et al. (2022) · Advances in Neural Information Processing Systems 35

Natural Language Processing TechniquesTopic Modeling

Cited by 3,117

Understanding Machine Learning: From Theory to Algorithms

Shalev-Shwartz, S., & Ben-David, S. (2014) · Cambridge University Press eBooks

Machine Learning and AlgorithmsStochastic Gradient Optimization Techniques

Cited by 3,115

MoleculeNet: a benchmark for molecular machine learning

Wu, Z., Ramsundar, B., Feinberg, E. N., Gomes, J., Geniesse, C., Pappu, A. S., Leswing, K., & Pande, V. (2018) · Chemical Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 3,107

Automatic Chemical Design Using a Data-Driven Continuous Representation of Molecules

Gómez-Bombarelli, R., Wei, J. N., Duvenaud, D., Hernández-Lobato, J. M., Sánchez-Lengeling, B., Sheberla, D., Aguilera-Iparraguirre, J., Hirzel, T. D., Adams, R. P., & Aspuru-Guzik, A. (2018) · ACS Central Science

Computational Drug Discovery MethodsMachine Learning in Materials Science

Cited by 3,106

Ensemble learning: A survey

Sagi, O., & Rokach, L. (2018) · WIREs Data Mining and Knowledge Discovery

Machine Learning and Data ClassificationImbalanced Data Classification Techniques

Cited by 3,103

New High-Intensity Source of Polarization-Entangled Photon Pairs

Kwiat, P. G., Mattle, K., Weinfurter, H., Zeilinger, A., Sergienko, A. V., & Shih, Y. (1995) · Physical Review Letters

Quantum Information and CryptographyQuantum Mechanics and Applications

Cited by 3,102

Locality-constrained Linear Coding for image classification

Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., & Gong, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

Advanced Image and Video Retrieval TechniquesImage Retrieval and Classification Techniques

Cited by 3,100

CircStat : A MATLAB Toolbox for Circular Statistics

Berens, P. (2009) · Journal of Statistical Software

Data Analysis with RStatistical Distribution Estimation and Applications

Cited by 3,099

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3,096

Class-Balanced Loss Based on Effective Number of Samples

Cui, Y., Jia, M., Lin, T.-Y., Song, Y., & Belongie, S. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network ApplicationsImbalanced Data Classification Techniques

Cited by 3,094

PointRCNN: 3D Object Proposal Generation and Detection From Point Cloud

Shi, S., Wang, X., & Li, H. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Neural Network Applications3D Shape Modeling and Analysis

Cited by 3,093

The Power of Scale for Parameter-Efficient Prompt Tuning

Lester, B., Al-Rfou, R., & Constant, N. (2021) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 3,092

A systematic study of the class imbalance problem in convolutional neural networks

Buda, M., Maki, A., & Mazurowski, M. A. (2018) · Neural Networks

Imbalanced Data Classification TechniquesAdvanced Neural Network Applications

Cited by 3,088

What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?

Kendall, A., & Gal, Y. (2017) · arXiv

Advanced Neural Network ApplicationsAdversarial Robustness in Machine Learning

Cited by 3,088

Universal Quantum Simulators

Lloyd, S. (1996) · Science

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 3,088

Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks

Tai, K. S., Socher, R., & Manning, C. D. (2015) · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 3,081

Deep learning for healthcare: review, opportunities and challenges

Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018) · Briefings in Bioinformatics

Machine Learning in HealthcareArtificial Intelligence in Healthcare and Education

Cited by 3,076

An algorithm for tracking multiple targets

Reid, D. (1979) · IEEE Transactions on Automatic Control

Target Tracking and Data Fusion in Sensor NetworksInertial Sensor and Navigation

Cited by 3,059

Surface codes: Towards practical large-scale quantum computation

Fowler, A. G., Mariantoni, M., Martinis, J. M., & Cleland, A. N. (2012) · Physical Review A

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 3,059