Check computer science papers: citations, claims and retractions

CUBIC: a new TCP-friendly high-speed TCP variant

Ha, S., Rhee, I., & Xu, L. (2008) · ACM SIGOPS Operating Systems Review

Network Traffic and Congestion ControlSoftware-Defined Networks and 5G

Cited by 2,287

Deep domain confusion Maximizing for domain invariance

Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., & Darrell, T. (2014) · arXiv

Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications

Cited by 2,285

Scalable Diffusion Models with Transformers

Peebles, W., & Xie, S. (2022) · arXiv

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 2,284

Scalable Diffusion Models with Transformers

Peebles, W., & Xie, S. (2023) · 2023 IEEE/CVF International Conference on Computer Vision (ICCV)

Generative Adversarial Networks and Image SynthesisAdvanced Neural Network Applications

Cited by 2,284

Data-efficient and weakly supervised computational pathology on whole-slide images

Lu, M. Y., Williamson, D. F. K., Chen, T. Y., Chen, R. J., Barbieri, M., & Mahmood, F. (2021) · Nature Biomedical Engineering

AI in cancer detectionRadiomics and Machine Learning in Medical Imaging

Cited by 2,282

Longformer: The Long-Document Transformer

Beltagy, I., Peters, M. E., & Cohan, A. (2020) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,281

Soft-NMS — Improving Object Detection with One Line of Code

Bodla, N., Singh, B., Chellappa, R., & Davis, L. S. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)

Advanced Neural Network ApplicationsVisual Attention and Saliency Detection

Cited by 2,274

Object scene flow for autonomous vehicles

Menze, M., & Geiger, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)

Advanced Vision and ImagingAutonomous Vehicle Technology and Safety

Cited by 2,270

Parameter-efficient transfer learning for NLP

Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., de Laroussilhe, Q., Gesmundo, A., Attariyan, M., & Gelly, S. (2019) · arXiv

Domain Adaptation and Few-Shot LearningTopic Modeling

Cited by 2,270

Named data networking

Zhang, L., Afanasyev, A., Burke, J., Jacobson, V., claffy, K., Crowley, P., Papadopoulos, C., Wang, L., & Zhang, B. (2014) · ACM SIGCOMM Computer Communication Review

Caching and Content DeliveryOpportunistic and Delay-Tolerant Networks

Cited by 2,270

Generative communication in Linda

Gelernter, D. (1985) · ACM Transactions on Programming Languages and Systems

Distributed and Parallel Computing SystemsDistributed systems and fault tolerance

Cited by 2,266

Aligning Books and Movies: Towards Story-Like Visual Explanations by Watching Movies and Reading Books

Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV)

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 2,264

Empirical Evaluation of Rectified Activations in Convolutional Network

Xu, B., Wang, N., Chen, T., & Li, M. (2015) · arXiv

Advanced Neural Network ApplicationsNeural Networks and Applications

Cited by 2,264

Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books

Zhu, Y., Kiros, R., Zemel, R., Salakhutdinov, R., Urtasun, R., Torralba, A., & Fidler, S. (2015) · arXiv

Multimodal Machine Learning ApplicationsTopic Modeling

Cited by 2,264

Multimodal Transformer for Unaligned Multimodal Language Sequences

Tsai, Y.-H. H., Bai, S., Liang, P. P., Kolter, J. Z., Morency, L.-P., & Salakhutdinov, R. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Multimodal Machine Learning ApplicationsTime Series Analysis and Forecasting

Cited by 2,259

Practical Recommendations for Gradient-Based Training of Deep Architectures

Bengio, Y. (2012) · Lecture Notes in Computer Science

Neural Networks and ApplicationsStochastic Gradient Optimization Techniques

Cited by 2,259

VoxCeleb2: Deep Speaker Recognition

Chung, J. S., Nagrani, A., & Zisserman, A. (2018) · Interspeech 2018

Speech Recognition and SynthesisSpeech and Audio Processing

Cited by 2,256

Embedding Entities and Relations for Learning and Inference in Knowledge Bases

Yang, B., Yih, W.-T., He, X., Gao, J., & Deng, L. (2014) · arXiv

Advanced Graph Neural NetworksLogic, Reasoning, and Knowledge

Cited by 2,252

Educational Data Mining: A review of the state of the art

Romero, C., & Ventura, S. (2010) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews)

Online Learning and AnalyticsIntelligent Tutoring Systems and Adaptive Learning

Cited by 2,251

A semiempirical free energy force field with charge‐based desolvation

Huey, R., Morris, G. M., Olson, A. J., & Goodsell, D. S. (2007) · Journal of Computational Chemistry

Computational Drug Discovery MethodsAdvanced Chemical Physics Studies

Cited by 2,246

Peres-Horodecki Separability Criterion for Continuous Variable Systems

Simon, R. (2000) · Physical Review Letters

Quantum Information and CryptographyQuantum Mechanics and Applications

Cited by 2,245

Know What You Don't Know: Unanswerable Questions for SQuAD

Rajpurkar, P., Jia, R., & Liang, P. (2018) · arXiv

Topic ModelingNatural Language Processing Techniques

Cited by 2,242

Data collection in a flat world: the strengths and weaknesses of Mechanical Turk samples

Goodman, J. K., Cryder, C. E., & Cheema, A. (2013) · Journal of Behavioral Decision Making

Mobile Crowdsensing and CrowdsourcingDecision-Making and Behavioral Economics

Cited by 2,242

Know What You Don’t Know: Unanswerable Questions for SQuAD

Rajpurkar, P., Jia, R., & Liang, P. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

Topic ModelingNatural Language Processing Techniques

Cited by 2,240