Check computer science papers: citations, claims and retractions

Robust fine-tuning of zero-shot models

Wortsman, M., Ilharco, G., Kim, J. W., Li, M., Kornblith, S., Roelofs, R., Lopes, R. G., Hajishirzi, H., Farhadi, A., Namkoong, H., et al. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 428

ProjectQ: An open source software framework for quantum computing

Steiger, D. S., Häner, T., & Troyer, M. (2018) · Quantum

Quantum Computing Algorithms and ArchitectureParallel Computing and Optimization Techniques

Cited by 428

Towards Reasoning in Large Language Models: A Survey

Huang, J., & Chang, K. C.-C. (2023) · Findings of the Association for Computational Linguistics: ACL 2023

Topic ModelingNatural Language Processing Techniques

Cited by 428

Confidence calibration and predictive uncertainty estimation for deep medical image segmentation

Mehrtash, A., Wells, W. M., Tempany, C. M., Abolmaesumi, P., & Kapur, T. (2020) · IEEE Transactions on Medical Imaging

Medical Image Segmentation TechniquesAdvanced Neural Network Applications

Cited by 428

Towards Reasoning in Large Language Models: A Survey

Huang, J., & Chang, K. C.-C. (2022) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 428

End-to-End Reconstruction-Classification Learning for Face Forgery Detection

Cao, J., Ma, C., Yao, T., Chen, S., Ding, S., & Yang, X. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Digital Media Forensic DetectionGenerative Adversarial Networks and Image Synthesis

Cited by 428

Why do deep convolutional networks generalize so poorly to small image transformations?

Azulay, A., & Weiss, Y. (2018) · arXiv

Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning

Cited by 428

Prototype Augmentation and Self-Supervision for Incremental Learning

Zhu, F., Zhang, X.-Y., Wang, C., Yin, F., & Liu, C.-L. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 427

Analysing real-time communications: controller area network (CAN)

Tindell, Hansson, & Wellings (1994) · Proceedings Real-Time Systems Symposium REAL-94

Real-Time Systems SchedulingNetwork Time Synchronization Technologies

Cited by 427

Deep Learning Scaling is Predictable, Empirically

Hestness, J., Narang, S., Ardalani, N., Diamos, G., Jun, H., Kianinejad, H., Patwary, M. M. A., Yang, Y., & Zhou, Y. (2017) · arXiv

Machine Learning and Data ClassificationStochastic Gradient Optimization Techniques

Cited by 427

The deepfake detection challenge (dfdc) dataset

Dolhansky, B., Bitton, J., Pflaum, B., Lu, J., Howes, R., Wang, M., & Ferrer, C. C. (2020) · arXiv

Digital Media Forensic DetectionFace recognition and analysis

Cited by 427

SpAtten: Efficient Sparse Attention Architecture with Cascade Token and Head Pruning

Wang, H., Zhang, Z., & Han, S. (2021) · 2021 IEEE International Symposium on High-Performance Computer Architecture (HPCA)

Advanced Neural Network ApplicationsParallel Computing and Optimization Techniques

Cited by 427

Direct Estimations of Linear and Nonlinear Functionals of a Quantum State

Ekert, A. K., Alves, C. M., Oi, D. K. L., Horodecki, M., Horodecki, P., & Kwek, L. C. (2002) · Physical Review Letters

Quantum Computing Algorithms and ArchitectureQuantum Information and Cryptography

Cited by 427

An efficient framework for learning sentence representations

Logeswaran, L., & Lee, H. (2018) · arXiv

Topic ModelingSentiment Analysis and Opinion Mining

Cited by 427

Fast iterative alignment of pose graphs with poor initial estimates

Olson, E., Leonard, J., & Teller, S. (2006) · Proceedings 2006 IEEE International Conference on Robotics and Automation, 2006. ICRA 2006.

Robotics and Sensor-Based LocalizationTarget Tracking and Data Fusion in Sensor Networks

Cited by 427

Visualizing high-dimensional data: advances in the past decade

Liu, S., Maljovec, D., Wang, B., Bremer, P.-T., & Pascucci, V. (2017) · IEEE Transactions on Visualization and Computer Graphics

Data Visualization and AnalyticsComputer Graphics and Visualization Techniques

Cited by 427

Simple Black-box Adversarial Attacks

Guo, C., Gardner, J. R., You, Y., Wilson, A. G., & Weinberger, K. Q. (2019) · arXiv

Adversarial Robustness in Machine LearningDigital Media Forensic Detection

Cited by 427

A Systematic Review of Re-Identification Attacks on Health Data

El Emam, K., Jonker, E., Arbuckle, L., & Malin, B. (2011) · PLoS ONE

Privacy-Preserving Technologies in DataThoreau and American Literature

Cited by 427

JCrasher: an automatic robustness tester for Java

Csallner, C., & Smaragdakis, Y. (2004) · Software: Practice and Experience

Software Testing and Debugging TechniquesSoftware Engineering Research

Cited by 426

Quantitative Image Analysis of Cellular Heterogeneity in Breast Tumors Complements Genomic Profiling

Yuan, Y., Failmezger, H., Rueda, O. M., Ali, H. R., Gräf, S., Chin, S.-F., Schwarz, R. F., Curtis, C., Dunning, M. J., Bardwell, H., et al. (2012) · Science Translational Medicine

AI in cancer detectionBreast Cancer Treatment Studies

Cited by 426

Learning Explanatory Rules from Noisy Data

Evans, R., & Grefenstette, E. (2018) · Journal of Artificial Intelligence Research

Logic, Reasoning, and KnowledgeExplainable Artificial Intelligence (XAI)

Cited by 426

Automated Systolic Array Architecture Synthesis for High Throughput CNN Inference on FPGAs

Wei, X., Yu, C. H., Zhang, P., Chen, Y., Wang, Y., Hu, H., Liang, Y., & Cong, J. (2017) · Proceedings of the 54th Annual Design Automation Conference 2017

Embedded Systems Design TechniquesAdvanced Neural Network Applications

Cited by 426

DeepFix: Fixing Common C Language Errors by Deep Learning

Gupta, R., Pal, S., Kanade, A., & Shevade, S. (2017) · Proceedings of the AAAI Conference on Artificial Intelligence

Software Engineering ResearchSoftware Testing and Debugging Techniques

Cited by 426

Spotting opinion spammers using behavioral footprints

Mukherjee, A., Kumar, A., Liu, B., Wang, J., Hsu, M., Castellanos, M., & Ghosh, R. (2013) · Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining

Spam and Phishing DetectionMisinformation and Its Impacts

Cited by 426