Check computer science papers: citations, claims and retractions

Transfer learning for time series classification

Ismail Fawaz, H., Forestier, G., Weber, J., Idoumghar, L., & Muller, P.-A. (2018) · 2018 IEEE International Conference on Big Data (Big Data)

Time Series Analysis and ForecastingDomain Adaptation and Few-Shot Learning

Cited by 319

SgxPectre: Stealing Intel Secrets from SGX Enclaves Via Speculative Execution

Chen, G., Chen, S., Xiao, Y., Zhang, Y., Lin, Z., & Lai, T. H. (2019) · 2019 IEEE European Symposium on Security and Privacy (EuroS&P)

Security and Verification in ComputingCryptographic Implementations and Security

Cited by 319

Breaking NLI Systems with Sentences that Require Simple Lexical Inferences

Glockner, M., Shwartz, V., & Goldberg, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 319

Machine Learning in Disaster Management: Recent Developments in Methods and Applications

Linardos, V., Drakaki, M., Tzionas, P., & Karnavas, Y. (2022) · Machine Learning and Knowledge Extraction

Energy and Environmental SystemsDisaster Management and Resilience

Cited by 319

Towards Conversational Search and Recommendation: System Ask, User Respond

Zhang, Y., Chen, X., Ai, Q., Yang, L., & Croft, W. B. (2018) · Proceedings of the 27th ACM International Conference on Information and Knowledge Management

Recommender Systems and TechniquesInformation Retrieval and Search Behavior

Cited by 319

Reflecting on reflection: framing a design landscape

Fleck, R., & Fitzpatrick, G. (2010) · Proceedings of the 22nd Conference of the Computer-Human Interaction Special Interest Group of Australia on Computer-Human Interaction

Innovative Human-Technology InteractionReflective Practices in Education

Cited by 319

A Machine Learning Approach for Non-blind Image Deconvolution

Schuler, C. J., Burger, H. C., Harmeling, S., & Scholkopf, B. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition

Advanced Image Processing TechniquesImage and Signal Denoising Methods

Cited by 319

ELI5: long form question answering

Fan, A., Jernite, Y., Perez, E., Grangier, D., Weston, J., & Auli, M. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Topic ModelingNatural Language Processing Techniques

Cited by 319

Log2vec: A Heterogeneous Graph Embedding Based Approach for Detecting Cyber Threats within Enterprise

Liu, F., Wen, Y., Zhang, D., Jiang, X., Xing, X., & Meng, D. (2019) · Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security

Network Security and Intrusion DetectionInformation and Cyber Security

Cited by 319

Systems perspective of Amazon mechanical Turk for organizational research: review and recommendations

Keith, M. G., Tay, L., & Harms, P. D. (2017) · Frontiers in Psychology

Mobile Crowdsensing and CrowdsourcingSurvey Methodology and Nonresponse

Cited by 319

E-health in low- and middle-income countries: findings from the Center for Health Market Innovations

Lewis, T., Synowiec, C., Lagomarsino, G., & Schweitzer, J. (2012) · Bulletin of the World Health Organization

ICT in Developing CommunitiesMobile Health and mHealth Applications

Cited by 319

Traversing Knowledge Graphs in Vector Space

Guu, K., Miller, J., & Liang, P. (2015) · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

Advanced Graph Neural NetworksTopic Modeling

Cited by 319

Linked independent component analysis for multimodal data fusion

Groves, A. R., Beckmann, C. F., Smith, S. M., & Woolrich, M. W. (2011) · NeuroImage

Blind Source Separation TechniquesTensor decomposition and applications

Cited by 319

Spatiotemporal Anomaly Detection Using Deep Learning for Real-Time Video Surveillance

Nawaratne, R., Alahakoon, D., De Silva, D., & Yu, X. (2020) · IEEE Transactions on Industrial Informatics

Anomaly Detection Techniques and ApplicationsVideo Surveillance and Tracking Methods

Cited by 318

Inclusive AI literacy for kids around the world

Druga, S., Vu, S. T., Likhith, E., & Qiu, T. (2019) · Proceedings of FabLearn 2019

Teaching and Learning ProgrammingChild Development and Digital Technology

Cited by 318

U-Net-Based Medical Image Segmentation

Yin, X.-X., Sun, L., Fu, Y., Lu, R., & Zhang, Y. (2022) · Journal of Healthcare Engineering

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 318

Trends in IoT based solutions for health care: Moving AI to the edge

Greco, L., Percannella, G., Ritrovato, P., Tortorella, F., & Vento, M. (2020) · Pattern Recognition Letters

IoT and Edge/Fog ComputingInternet of Things and AI

Cited by 318

Triton: an intermediate language and compiler for tiled neural network computations

Tillet, P., Kung, H. T., & Cox, D. (2019) · Proceedings of the 3rd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages

Parallel Computing and Optimization TechniquesEmbedded Systems Design Techniques

Cited by 318

A Crowdsourcing Framework for On-Device Federated Learning

Pandey, S. R., Tran, N. H., Bennis, M., Tun, Y. K., Manzoor, A., & Hong, C. S. (2020) · IEEE Transactions on Wireless Communications

Privacy-Preserving Technologies in DataMobile Crowdsensing and Crowdsourcing

Cited by 318

Expressive power of parametrized quantum circuits

Du, Y., Hsieh, M.-H., Liu, T., & Tao, D. (2020) · Physical Review Research

Quantum Computing Algorithms and ArchitectureQuantum many-body systems

Cited by 318

Learning to explore using active neural SLAM

Chaplot, D. S., Gandhi, D., Gupta, S., Gupta, A., & Salakhutdinov, R. (2020) · arXiv

Reinforcement Learning in RoboticsRobotics and Sensor-Based Localization

Cited by 318

What Clinicians Want: Contextualizing Explainable Machine Learning for Clinical End Use

Tonekaboni, S., Joshi, S., McCradden, M. D., & Goldenberg, A. (2019) · arXiv

Explainable Artificial Intelligence (XAI)Artificial Intelligence in Healthcare and Education

Cited by 318

Turbo-Aggregate: Breaking the Quadratic Aggregation Barrier in Secure Federated Learning

So, J., Guler, B., & Avestimehr, A. S. (2021) · IEEE Journal on Selected Areas in Information Theory

Privacy-Preserving Technologies in DataCryptography and Data Security

Cited by 318

On Safeguarding Privacy and Security in the Framework of Federated Learning

Ma, C., Li, J., Ding, M., Yang, H. H., Shu, F., Quek, T. Q. S., & Poor, H. V. (2020) · IEEE Network

Privacy-Preserving Technologies in DataCloud Data Security Solutions

Cited by 318