Check computer science papers: citations, claims and retractions

Deal or No Deal? End-to-End Learning of Negotiation Dialogues

Lewis, M., Yarats, D., Dauphin, Y., Parikh, D., & Batra, D. (2017) · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Speech and dialogue systemsMulti-Agent Systems and Negotiation

Cited by 347

Resilient quantum computation: error models and thresholds

Knill, E., Laflamme, R., & Zurek, W. H. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 346

Shapley additive explanations for NO2 forecasting

Vega García, M., & Aznarte, J. L. (2020) · Ecological Informatics

Explainable Artificial Intelligence (XAI)Air Quality Monitoring and Forecasting

Cited by 346

Feature Transfer Learning for Face Recognition With Under-Represented Data

Yin, X., Yu, X., Sohn, K., Liu, X., & Chandraker, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Face recognition and analysisFace and Expression Recognition

Cited by 346

Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems

Jung, J., Murray-Rust, D., Gadiraju, U., Bozzon, A., Shamma, D. A., Drukcer, S., Barbosa, S., Lampe, C. A. C., Appert, C., Williamson, J., et al. (2020)

Emotion and Mood RecognitionVirtual Reality Applications and Impacts

Cited by 346

Learning Affective Features With a Hybrid Deep Model for Audio–Visual Emotion Recognition

Zhang, S., Zhang, S., Huang, T., Gao, W., & Tian, Q. (2018) · IEEE Transactions on Circuits and Systems for Video Technology

Emotion and Mood RecognitionSpeech and Audio Processing

Cited by 346

SC-FEGAN: Face Editing Generative Adversarial Network With User’s Sketch and Color

Jo, Y., & Park, J. (2019) · 2019 IEEE/CVF International Conference on Computer Vision (ICCV)

Generative Adversarial Networks and Image SynthesisDigital Media Forensic Detection

Cited by 346

PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models

Scholak, T., Schucher, N., & Bahdanau, D. (2021) · arXiv

Natural Language Processing TechniquesTopic Modeling

Cited by 346

Sikuli: using GUI screenshots for search and automation

Yeh, T., Chang, T.-H., & Miller, R. C. (2009) · Proceedings of the 22nd annual ACM symposium on User interface software and technology

Usability and User Interface DesignSoftware Testing and Debugging Techniques

Cited by 346

Empirical evaluation and new design for fighting evolving Twitter spammers

Yang, C., Harkreader, R., & Gu, G. (2013) · IEEE Transactions on Information Forensics and Security

Spam and Phishing DetectionHate Speech and Cyberbullying Detection

Cited by 346

Prospects for an engineering discipline of software

Shaw, M. (1990) · IEEE Software

Software Engineering Techniques and PracticesAdvanced Software Engineering Methodologies

Cited by 346

Using Machine Teaching to Identify Optimal Training-Set Attacks on Machine Learners

Mei, S., & Zhu, X. (2015) · Proceedings of the AAAI Conference on Artificial Intelligence

Adversarial Robustness in Machine LearningPrivacy-Preserving Technologies in Data

Cited by 346

Examining menstrual tracking to inform the design of personal informatics tools

Epstein, D. A., Lee, N. B., Kang, J. H., Agapie, E., Schroeder, J., Pina, L. R., Fogarty, J., Kientz, J. A., & Munson, S. (2017) · Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

Innovative Human-Technology InteractionMobile Health and mHealth Applications

Cited by 346

Blockchain for Internet of Energy management: Review, solutions, and challenges

Miglani, A., Kumar, N., Chamola, V., & Zeadally, S. (2020) · Computer Communications

Blockchain Technology Applications and SecuritySmart Grid Energy Management

Cited by 346

Sensor Positioning for Activity Recognition Using Wearable Accelerometers

Atallah, L., Lo, B., King, R., & Yang, G.-Z. (2011) · IEEE Transactions on Biomedical Circuits and Systems

Context-Aware Activity Recognition SystemsPhysical Activity and Health

Cited by 346

Learning Interactive Real-World Simulators

Yang, S., Du, Y., Ghasemipour, K., Tompson, J., Kaelbling, L., Schuurmans, D., & Abbeel, P. (2023) · arXiv

Reinforcement Learning in RoboticsMultimodal Machine Learning Applications

Cited by 346

Three-dimensional statistical analysis of sulcal variability in the human brain

Thompson, P. M., Schwartz, C., Lin, R. T., Khan, A. A., & Toga, A. W. (1996) · The Journal of Neuroscience

Medical Image Segmentation TechniquesAnatomy and Medical Technology

Cited by 346

Universality in quantum computation

Deutsch, D. E., Barenco, A., & Ekert, A. (1995) · Proceedings of the Royal Society of London. Series A: Mathematical and Physical Sciences

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 346

Multi-task Self-Supervised Learning for Human Activity Detection

Saeed, A., Ozcelebi, T., & Lukkien, J. (2019) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies

Context-Aware Activity Recognition SystemsDomain Adaptation and Few-Shot Learning

Cited by 346

Learning Depth with Convolutional Spatial Propagation Network

Cheng, X., Wang, P., & Yang, R. (2018) · arXiv

Advanced Vision and ImagingAdvanced Neural Network Applications

Cited by 345

Selection history: How reward modulates selectivity of visual attention.

Failing, M., & Theeuwes, J. (2018) · Psychonomic Bulletin & Review

Visual Attention and Saliency DetectionVisual perception and processing mechanisms

Cited by 345

Visual inspection of independent components: defining a procedure for artifact removal from fMRI data

Kelly, R. E., Alexopoulos, G. S., Wang, Z., Gunning, F. M., Murphy, C. F., Morimoto, S. S., Kanellopoulos, D., Jia, Z., Lim, K. O., & Hoptman, M. J. (2010) · Journal of Neuroscience Methods

Blind Source Separation TechniquesAdvanced MRI Techniques and Applications

Cited by 345

Training wheels in a user interface

Carroll, J. M., & Carrithers, C. (1984) · Communications of the ACM

Usability and User Interface DesignVisual and Cognitive Learning Processes

Cited by 345

GAMENet: Graph Augmented MEmory Networks for Recommending Medication Combination

Shang, J., Xiao, C., Ma, T., Li, H., & Sun, J. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence

Machine Learning in HealthcareRecommender Systems and Techniques

Cited by 345