Check computer science papers: citations, claims and retractions

Language (Technology) is Power: A Critical Survey of “Bias” in NLP

Blodgett, S. L., Barocas, S., Daumé III, H., & Wallach, H. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics

Ethics and Social Impacts of AINatural Language Processing Techniques

Cited by 1,050

Predicting clicks: estimating the click-through rate for new ads

Richardson, M., Dominowska, E., & Ragno, R. (2007) · Proceedings of the 16th international conference on World Wide Web

Information Retrieval and Search BehaviorRecommender Systems and Techniques

Cited by 1,049

Voxel R-CNN: Towards High Performance Voxel-based 3D Object Detection

Deng, J., Shi, S., Li, P., Zhou, W., Zhang, Y., & Li, H. (2021) · Proceedings of the AAAI Conference on Artificial Intelligence

Advanced Neural Network Applications3D Shape Modeling and Analysis

Cited by 1,048

Deep Visual-Semantic Alignments for Generating Image Descriptions

Karpathy, A., & Fei-Fei, L. (2017) · IEEE Transactions on Pattern Analysis and Machine Intelligence

Multimodal Machine Learning ApplicationsImage Retrieval and Classification Techniques

Cited by 1,048

Multiple dipole modeling and localization from spatio-temporal MEG data

Mosher, J. C., Lewis, P. S., & Leahy, R. M. (1992) · IEEE Transactions on Biomedical Engineering

Direction-of-Arrival Estimation TechniquesEEG and Brain-Computer Interfaces

Cited by 1,048

Meteor: an automatic metric for MT evaluation with high levels of correlation with human judgments

Lavie, A., & Agarwal, A. (2007) · Proceedings of the Second Workshop on Statistical Machine Translation - StatMT '07

Natural Language Processing TechniquesTopic Modeling

Cited by 1,047

SSD-6D: Making RGB-Based 3D Detection and 6D Pose Estimation Great Again

Kehl, W., Manhardt, F., Tombari, F., Ilic, S., & Navab, N. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network Applications3D Shape Modeling and Analysis

Cited by 1,047

Cost function dependent barren plateaus in shallow parametrized quantum circuits

Cerezo, M., Sone, A., Volkoff, T., Cincio, L., & Coles, P. J. (2021) · Nature Communications

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 1,047

Unmasking Clever Hans predictors and assessing what machines really learn

Lapuschkin, S., Wäldchen, S., Binder, A., Montavon, G., Samek, W., & Müller, K.-R. (2019) · Nature Communications

Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques

Cited by 1,047

Recent advances in deep learning for object detection

Wu, X., Sahoo, D., & Hoi, S. C. H. (2020) · Neurocomputing

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 1,047

A large-scale study of web password habits

Florencio, D., & Herley, C. (2007) · Proceedings of the 16th international conference on World Wide Web

User Authentication and Security SystemsPrivacy, Security, and Data Protection

Cited by 1,047

New Technology and Digital Worlds: Analyzing Evidence of Equity in Access, Use, and Outcomes

Warschauer, M., & Matuchniak, T. (2010) · Review of Research in Education

Mobile Learning in EducationEducational Technology in Learning

Cited by 1,047

Using linguistic cues for the automatic recognition of personality in conversation and text

Mairesse, F., Walker, M. A., Mehl, M. R., & Moore, R. K. (2007) · Journal of Artificial Intelligence Research

Authorship Attribution and ProfilingMental Health via Writing

Cited by 1,046

Generalizing to unseen domains: A survey on domain generalization

Wang, J., Lan, C., Liu, C., Ouyang, Y., Qin, T., Lu, W., Chen, Y., Zeng, W., & Yu, P. (2022) · IEEE Transactions on Knowledge and Data Engineering

Domain Adaptation and Few-Shot LearningReinforcement Learning in Robotics

Cited by 1,046

Symmetric Cross Entropy for Robust Learning With Noisy Labels

Wang, Y., Ma, X., Chen, Z., Luo, Y., Yi, J., & Bailey, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Machine Learning and Data ClassificationAdvanced Neural Network Applications

Cited by 1,046

Very Deep Convolutional Networks for Text Classification

Conneau, A., Schwenk, H., Barrault, L., & Lecun, Y. (2017) · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

Topic ModelingText and Document Classification Technologies

Cited by 1,046

Learning Multi-attention Convolutional Neural Network for Fine-Grained Image Recognition

Zheng, H., Fu, J., Mei, T., & Luo, J. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsAdvanced Image and Video Retrieval Techniques

Cited by 1,045

ON THE DERIVATION OF A “CHAOTIC” ENCRYPTION ALGORITHM

Matthews, R. (1989) · Cryptologia

Chaos-based Image/Signal EncryptionMathematical Dynamics and Fractals

Cited by 1,044

RACE: Large-scale ReAding Comprehension Dataset From Examinations

Lai, G., Xie, Q., Liu, H., Yang, Y., & Hovy, E. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Topic ModelingReading and Literacy Development

Cited by 1,044

Bias in data‐driven artificial intelligence systems—An introductory survey

Ntoutsi, E., Fafalios, P., Gadiraju, U., Iosifidis, V., Nejdl, W., Vidal, M., Ruggieri, S., Turini, F., Papadopoulos, S., Krasanakis, E., et al. (2020) · WIREs Data Mining and Knowledge Discovery

Ethics and Social Impacts of AIExplainable Artificial Intelligence (XAI)

Cited by 1,043

Competition-Level Code Generation with AlphaCode

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Lago, A. D., et al. (2022) · arXiv

Software Engineering ResearchTeaching and Learning Programming

Cited by 1,043

Competition-level code generation with AlphaCode

Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., et al. (2022) · Science

Software Engineering ResearchTeaching and Learning Programming

Cited by 1,043

MediaPipe: A framework for building perception pipelines

Lugaresi, C., Tang, J., Nash, H., McClanahan, C., Uboweja, E., Hays, M., Zhang, F., Chang, C.-L., Yong, M. G., Lee, J., et al. (2019) · arXiv

Advanced Neural Network ApplicationsComputer Science and Engineering

Cited by 1,043

State-of-the-Art Speech Recognition with Sequence-to-Sequence Models

Chiu, C.-C., Sainath, T. N., Wu, Y., Prabhavalkar, R., Nguyen, P., Chen, Z., Kannan, A., Weiss, R. J., Rao, K., Gonina, E., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 1,043