Most cited computer science papers
- Verified
Denoising Diffusion Implicit Models
Song, J., Meng, C., & Ermon, S. (2020) · arXiv
Generative Adversarial Networks and Image SynthesisImage and Signal Denoising Methods
Cited by 2,507
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Explaining Explanations: An Overview of Interpretability of Machine Learning
Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018) · 2018 IEEE 5th International Conference on Data Science and Advanced Analytics (DSAA)
Explainable Artificial Intelligence (XAI)Software Testing and Debugging Techniques
Cited by 2,507
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A survey of named entity recognition and classification
Nadeau, D., & Sekine, S. (2007) · Lingvisticae Investigationes
Topic ModelingNatural Language Processing Techniques
Cited by 2,507
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Beta Regression in R
Cribari-Neto, F., & Zeileis, A. (2010) · Journal of Statistical Software
Data Analysis with RStatistical Distribution Estimation and Applications
Cited by 2,505
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Self-Attention with Relative Position Representations
Shaw, P., Uszkoreit, J., & Vaswani, A. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Topic ModelingNatural Language Processing Techniques
Cited by 2,505
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Students' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv
Artificial Intelligence in EducationArtificial Intelligence in Healthcare and Education
Cited by 2,504
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The Effectiveness of Data Augmentation in Image Classification using Deep Learning
Perez, L., & Wang, J. (2017) · arXiv
Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning
Cited by 2,499
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Unsupervised Learning of Visual Features by Contrasting Cluster Assignments
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., & Joulin, A. (2020) · arXiv
Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications
Cited by 2,499
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Generative Adversarial Text to Image Synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., & Lee, H. (2016) · arXiv
Generative Adversarial Networks and Image SynthesisMultimodal Machine Learning Applications
Cited by 2,494
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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., & Courville, A. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence
Multimodal Machine Learning ApplicationsAdvanced Neural Network Applications
Cited by 2,490
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Three-dimensional alpha shapes
Edelsbrunner, H., & Mücke, E. P. (1994) · ACM Transactions on Graphics
Computational Geometry and Mesh GenerationTopological and Geometric Data Analysis
Cited by 2,488
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A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., & Brox, T. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
Advanced Vision and ImagingAdvanced Neural Network Applications
Cited by 2,488
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Convolutional 2D Knowledge Graph Embeddings
Dettmers, T., Minervini, P., Stenetorp, P., & Riedel, S. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence
Advanced Graph Neural NetworksTopic Modeling
Cited by 2,486
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Revisiting Unreasonable Effectiveness of Data in Deep Learning Era
Sun, C., Shrivastava, A., Singh, S., & Gupta, A. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV)
Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot Learning
Cited by 2,484
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Unsupervised Representation Learning by Predicting Image Rotations
Gidaris, S., Singh, P., & Komodakis, N. (2018) · arXiv
Domain Adaptation and Few-Shot LearningAdvanced Neural Network Applications
Cited by 2,477
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Unsupervised deep embedding for clustering analysis
Xie, J., Girshick, R., & Farhadi, A. (2015) · arXiv
Advanced Clustering Algorithms ResearchFace and Expression Recognition
Cited by 2,476
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Practical byzantine fault tolerance and proactive recovery
Castro, M., & Liskov, B. (2002) · ACM Transactions on Computer Systems
Distributed systems and fault toleranceSoftware System Performance and Reliability
Cited by 2,475
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Datasheets for Datasets
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., Daumé, H., & Crawford, K. (2018) · arXiv
Research Data Management PracticesScientific Computing and Data Management
Cited by 2,474
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Mobile Edge Computing: A Survey
Abbas, N., Zhang, Y., Taherkordi, A., & Skeie, T. (2018) · IEEE Internet of Things Journal
IoT and Edge/Fog ComputingCaching and Content Delivery
Cited by 2,474
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Prefix-Tuning: Optimizing Continuous Prompts for Generation
Li, X. L., & Liang, P. (2021) · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Topic ModelingNatural Language Processing Techniques
Cited by 2,473
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Datasheets for datasets
Gebru, T., Morgenstern, J., Vecchione, B., Vaughan, J. W., Wallach, H., III, H. D., & Crawford, K. (2021) · Communications of the ACM
Research Data Management PracticesScientific Computing and Data Management
Cited by 2,469
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Quantum internet: a vision for the road ahead
Wehner, S., Elkouss, D., & Hanson, R. (2018) · Science
Quantum Information and CryptographyQuantum Computing Algorithms and Architecture
Cited by 2,467
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A quantitative analysis of kinase inhibitor selectivity
Karaman, M. W., Herrgard, S., Treiber, D. K., Gallant, P., Atteridge, C. E., Campbell, B. T., Chan, K. W., Ciceri, P., Davis, M. I., Edeen, P. T., et al. (2008) · Nature Biotechnology
Computational Drug Discovery MethodsProtein Kinase Regulation and GTPase Signaling
Cited by 2,466
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A Deep Learning Approach to Antibiotic Discovery
Stokes, J. M., Yang, K., Swanson, K., Jin, W., Cubillos-Ruiz, A., Donghia, N. M., MacNair, C. R., French, S., Carfrae, L. A., Bloom-Ackermann, Z., et al. (2020) · Cell
Computational Drug Discovery MethodsMicrobial Natural Products and Biosynthesis
Cited by 2,463
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