---
title: "United States — works"
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# Works of United States

2809 · page 1 of 29 · the most cited first · [← United States](https://md.research.elamur.ai/free/en/countries/united-states/)

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1. [**Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks**Shaoqing Ren, Kaiming He, Ross Girshick, et al.*2015 · 64,743 citations*](https://md.research.elamur.ai/free/en/papers/ren-2015-faster-r-cnn-towards-real/)
2. [**SciPy 1.0 — Fundamental Algorithms for Scientific Computing in Python**Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, et al.*2019 · 38,317 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-scipy-1-0-fundamental-algorithms/)
3. [**SMOTE: Synthetic Minority Over-sampling Technique**Nitesh V. Chawla, Kevin W. Bowyer, Lawrence Hall, et al.*2002 · 32,661 citations*](https://md.research.elamur.ai/free/en/papers/chawla-2002-smote-synthetic-minority-over-sampling/)
4. [**Bleu: a Method for Automatic Evaluation of Machine Translation**Kishore Papineni, Salim Roukos, Todd Ward, et al.*2002 · 32,065 citations*](https://md.research.elamur.ai/free/en/papers/papineni-2002-bleu-method-automatic-evaluation-machine/)
5. [**Rich feature hierarchies for accurate object detection and semantic segmentation**Ross Girshick, Jeff Donahue, Trevor Darrell, et al.*2013 · 31,715 citations*](https://md.research.elamur.ai/free/en/papers/girshick-2013-rich-feature-hierarchies-accurate-object/)
6. [**Latent Dirichlet Allocation**David M. Blei, Andrew Y. Ng, Michael I. Jordan*2003 · 28,297 citations*](https://md.research.elamur.ai/free/en/papers/blei-2003-latent-dirichlet-allocation/)
7. [**Array Programming with NumPy**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,591 citations*](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/)
8. [**End-to-End Object Detection with Transformers**Nicolas Carion, Francisco Massa, Gabriel Synnaeve, et al.*2020 · 16,761 citations*](https://md.research.elamur.ai/free/en/papers/carion-2020-end-end-object-detection-transformers/)
9. [**Bagging Predictors**Leo Breiman*1996 · 16,493 citations*](https://md.research.elamur.ai/free/en/papers/breiman-1996-bagging-predictors/)
10. [**The Anatomy of a Large-Scale Hypertextual Web Search Engine**Sergey Brin, Lawrence Page*1998 · 16,227 citations*](https://md.research.elamur.ai/free/en/papers/brin-1998-anatomy-large-scale-hypertextual-web/)
11. [**Convolutional Neural Networks for Sentence Classification**Yoon Kim*2014 · 16,218 citations*](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/)
12. [**Transformers: State-of-the-Art Natural Language Processing**Thomas Wolf, Lysandre Debut, Victor Sanh, et al.*2020 · 15,591 citations*](https://md.research.elamur.ai/free/en/papers/wolf-2020-transformers-state-art-natural-language/)
13. [**Momentum Contrast for Unsupervised Visual Representation Learning**Kaiming He, Haoqi Fan, Yuxin Wu, et al.*2019 · 13,143 citations*](https://md.research.elamur.ai/free/en/papers/he-2019-momentum-contrast-unsupervised-visual-representation/)
14. [**BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,752 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/)
15. [**FaceNet: A Unified Embedding for Face Recognition and Clustering**Florian Schroff, Dmitry Kalenichenko, James Philbin*2015 · 11,939 citations*](https://md.research.elamur.ai/free/en/papers/schroff-2015-facenet-unified-embedding-face-recognition/)
16. [**Chain-of-Thought Prompting Elicits Reasoning in Large Language Models**Jason Wei, Xuezhi Wang, Dale Schuurmans, et al.*2022 · 11,278 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-chain-thought-prompting-elicits-reasoning/)
17. [**Politeness: Some Universals in Language Usage**Penelope Brown, Stephen C. Levinson, John J. Gumperz*1987 · 10,545 citations*](https://md.research.elamur.ai/free/en/papers/brown-1987-politeness-some-universals-language-usage/)
18. [**Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead**Cynthia Rudin*2018 · 10,427 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2018-stop-explaining-black-box/)
19. [**A Comprehensive Survey on Graph Neural Networks**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,295 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
20. [**MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications**Andrew G. Howard, Menglong Zhu, Bo Chen, et al.*2017 · 9,633 citations*](https://md.research.elamur.ai/free/en/papers/howard-2017-mobilenets-efficient-convolutional-neural-networks/)
21. [**SQuAD: 100,000+ Questions for Machine Comprehension of Text**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,488 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/)
22. [**Authoritative Sources in a Hyperlinked Environment**Jon M. Kleinberg*1999 · 9,089 citations*](https://md.research.elamur.ai/free/en/papers/kleinberg-1999-authoritative-sources-hyperlinked-environment/)
23. [**The Stanford CoreNLP Natural Language Processing Toolkit**Christopher Manning, Mihai Surdeanu, John Bauer, et al.*2014 · 8,620 citations*](https://md.research.elamur.ai/free/en/papers/manning-2014-stanford-corenlp-natural-language-processing/)
24. [**MIMIC-III, a freely accessible critical care database**Alistair E. W. Johnson, Tom Pollard, Lu Shen, et al.*2016 · 8,530 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-mimic-iii-freely-accessible-critical/)
25. [**Explaining and Harnessing Adversarial Examples**Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy*2014 · 8,430 citations*](https://md.research.elamur.ai/free/en/papers/goodfellow-2014-explaining-harnessing-adversarial-examples/)
26. [**Learning Transferable Visual Models From Natural Language Supervision**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,295 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
27. [**A Unified Approach to Interpreting Model Predictions**Scott Lundberg, Su-In Lee*2017 · 8,086 citations*](https://md.research.elamur.ai/free/en/papers/lundberg-2017-unified-approach-interpreting-model-predictions/)
28. [**A Simple Framework for Contrastive Learning of Visual Representations**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 7,774 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
29. [**Thumbs up? Sentiment Classification using Machine Learning Techniques**Bo Pang, Lillian Lee, Shivakumar Vaithyanathan*2002 · 7,756 citations*](https://md.research.elamur.ai/free/en/papers/pang-2002-thumbs-up-sentiment-classification-machine/)
30. [**GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,141 citations*](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/)
31. [**Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks**Alec Radford, Luke Metz, Soumith Chintala*2015 · 6,519 citations*](https://md.research.elamur.ai/free/en/papers/radford-2015-unsupervised-representation-learning-deep-convolutional/)
32. [**Learning Transferable Architectures for Scalable Image Recognition**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,518 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-image/)
33. [**Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization**Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, et al.*2016 · 6,327 citations*](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/)
34. [**Natural Language Processing (almost) from Scratch**Ronan Collobert, Jason Weston, Leon Bottou, et al.*2011 · 6,099 citations*](https://md.research.elamur.ai/free/en/papers/collobert-2011-natural-language-processing-almost-scratch/)
35. [**Emerging Properties in Self-Supervised Vision Transformers**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 5,910 citations*](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/)
36. [**Intriguing properties of neural networks**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,870 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
37. [**Combining Labeled and Unlabeled Data with Co-Training**Avrim Blum, Tom Mitchell*1998 · 5,829 citations*](https://md.research.elamur.ai/free/en/papers/blum-1998-combining-labeled-unlabeled-data-co-training/)
38. [**Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations**Ranjay Krishna, Yuke Zhu, Oliver Groth, et al.*2016 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/)
39. [**Distributed Representations of Sentences and Documents**Quoc V. Le, Tomas Mikolov*2014 · 5,747 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
40. [**METEOR: An Automatic Metric for MT Evaluation with Improved Correlation with Human Judgments**Satanjeev Banerjee, Alon Lavie*2005 · 5,483 citations*](https://md.research.elamur.ai/free/en/papers/banerjee-2005-meteor-automatic-metric-mt-evaluation/)
41. [**Hierarchical Attention Networks for Document Classification**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,409 citations*](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/)
42. [**Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,375 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/)
43. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,370 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
44. [**EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**Mingxing Tan, Quoc V. Le*2019 · 5,308 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/)
45. [**Categorization and Representation of Physics Problems by Experts and Novices**Michelene T. H. Chi, Paul J. Feltovich, Robert Glaser*1981 · 5,250 citations*](https://md.research.elamur.ai/free/en/papers/chi-1981-categorization-representation-physics-problems-experts/)
46. [**Instance-Based Learning Algorithms**David W. Aha, Dennis Kibler, Marc K. Albert*1991 · 5,248 citations*](https://md.research.elamur.ai/free/en/papers/aha-1991-instance-based-learning-algorithms/)
47. [**Character-level Convolutional Networks for Text Classification**Xiang Zhang, Junbo Zhao, Yann LeCun*2015 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/)
48. [**Inductive Representation Learning on Large Graphs**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
49. [**Billion-scale similarity search with GPUs**Jeff Johnson, Matthijs Douze, Hervé Jégou*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2017-billion-scale-similarity-search-gpus/)
50. [**Microsoft COCO: Common Objects in Context**Tsung-Yi Lin, Michael Maire, Serge Belongie, et al.*2014 · 4,529 citations*](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
51. [**Large Language Models Encode Clinical Knowledge**Karan Singhal, Shekoofeh Azizi, Tao Tu, et al.*2022 · 4,464 citations*](https://md.research.elamur.ai/free/en/papers/singhal-2022-large-language-models-encode-clinical/)
52. [**SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing**Taku Kudo, John Richardson*2018 · 4,461 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2018-sentencepiece-simple-language-independent-subword/)
53. [**Learning Word Vectors for Sentiment Analysis**Andrew L. Maas, Raymond E. Daly, Peter T. Pham, et al.*2011 · 4,272 citations*](https://md.research.elamur.ai/free/en/papers/maas-2011-learning-word-vectors-sentiment-analysis/)
54. [**Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models**Tiffany H. Kung, Morgan Cheatham, Arielle Medenilla, et al.*2023 · 4,154 citations*](https://md.research.elamur.ai/free/en/papers/kung-2023-performance-chatgpt-usmle-potential-ai/)
55. [**Caffe: Convolutional Architecture for Fast Feature Embedding**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,064 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
56. [**Energy and Policy Considerations for Deep Learning in NLP**Emma Strubell, Ananya Ganesh, Andrew McCallum*2019 · 4,049 citations*](https://md.research.elamur.ai/free/en/papers/strubell-2019-energy-policy-considerations-deep-learning/)
57. [**Natural Questions: A Benchmark for Question Answering Research**Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al.*2019 · 4,031 citations*](https://md.research.elamur.ai/free/en/papers/kwiatkowski-2019-natural-questions-benchmark-question-answering/)
58. [**Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis**Theresa Wilson, Janyce Wiebe, Paul Hoffmann*2005 · 3,979 citations*](https://md.research.elamur.ai/free/en/papers/wilson-2005-recognizing-contextual-polarity-phrase-level/)
59. [**Accurate Unlexicalized Parsing**Dan Klein, Christopher D. Manning*2003 · 3,942 citations*](https://md.research.elamur.ai/free/en/papers/klein-2003-accurate-unlexicalized-parsing/)
60. [**SimCSE: Simple Contrastive Learning of Sentence Embeddings**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,921 citations*](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/)
61. [**A Sentimental Education: Sentiment Analysis Using Subjectivity Summarization Based on Minimum Cuts**Bo Pang, Lillian Lee*2004 · 3,842 citations*](https://md.research.elamur.ai/free/en/papers/pang-2004-sentimental-education-sentiment-analysis-subjectivity/)
62. [**A Study of Translation Edit Rate with Targeted Human Annotation**Matthew Snover, Bonnie Dorr, Rich Schwartz, et al.*2006 · 3,824 citations*](https://md.research.elamur.ai/free/en/papers/snover-2006-study-translation-edit-rate-targeted/)
63. [**SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition**Daniel S. Park, William Chan, Yu Zhang, et al.*2019 · 3,802 citations*](https://md.research.elamur.ai/free/en/papers/park-2019-specaugment-simple-data-augmentation-method/)
64. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,740 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
65. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
66. [**A Maximum Entropy Approach to Natural Language Processing**Adam L. Berger, Stephen A. Della Pietra, Vincent J. Della Pietra*1996 · 3,633 citations*](https://md.research.elamur.ai/free/en/papers/berger-1996-maximum-entropy-approach-natural-language/)
67. [**Incorporating Non-local Information into Information Extraction Systems by Gibbs Sampling**Jenny Rose Finkel, Trond Grenager, Christopher Manning*2005 · 3,618 citations*](https://md.research.elamur.ai/free/en/papers/finkel-2005-incorporating-non-local-information-into/)
68. [**Feature-Rich Part-of-Speech Tagging with a Cyclic Dependency Network**Kristina Toutanova, Dan Klein, Christopher D. Manning, et al.*2003 · 3,559 citations*](https://md.research.elamur.ai/free/en/papers/toutanova-2003-feature-rich-part-speech-tagging/)
69. [**SciBERT: A Pretrained Language Model for Scientific Text**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,548 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
70. [**The Berkeley FrameNet Project**Collin F. Baker, Charles J. Fillmore, John B. Lowe*1998 · 3,544 citations*](https://md.research.elamur.ai/free/en/papers/baker-1998-berkeley-framenet/)
71. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
72. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
73. [**Linguistic Regularities in Continuous Space Word Representations**Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig*2013 · 3,482 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-linguistic-regularities-continuous-space-word/)
74. [**HuBERT: Self-Supervised Speech Representation Learning by Masked Prediction of Hidden Units**Wei-Ning Hsu, Benjamin Bolte, Yao-Hung Hubert Tsai, et al.*2021 · 3,441 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2021-hubert-self-supervised-speech-representation/)
75. [**Conformer: Convolution-augmented Transformer for Speech Recognition**Anmol Gulati, James Qin, Chung-Cheng Chiu, et al.*2020 · 3,433 citations*](https://md.research.elamur.ai/free/en/papers/gulati-2020-conformer-convolution-augmented-transformer-speech/)
76. [**MIMIC-IV, a freely accessible electronic health record dataset**Alistair E. W. Johnson, Lucas Bulgarelli, Lu Shen, et al.*2023 · 3,385 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2023-mimic-iv-freely-accessible-electronic/)
77. [**Know What You Don't Know: Unanswerable Questions for SQuAD**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,360 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/)
78. [**Verb Semantics and Lexical Selection**Zhibiao Wu, Martha Palmer*1994 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/wu-1994-verb-semantics-lexical-selection/)
79. [**Continual Lifelong Learning with Neural Networks: A Review**German I. Parisi, Ronald Kemker, Jose L. Part, et al.*2018 · 3,210 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/)
80. [**The Proposition Bank: An Annotated Corpus of Semantic Roles**Martha Palmer, Daniel Gildea, Paul Kingsbury*2005 · 3,208 citations*](https://md.research.elamur.ai/free/en/papers/palmer-2005-proposition-bank-annotated-corpus-semantic/)
81. [**TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,204 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
82. [**Accurate Methods for the Statistics of Surprise and Coincidence**Ted Dunning*1993 · 3,060 citations*](https://md.research.elamur.ai/free/en/papers/dunning-1993-accurate-methods-statistics-surprise-coincidence/)
83. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,030 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
84. [**Language Models as Knowledge Bases?**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,977 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/)
85. [**CodeBERT: A Pre-Trained Model for Programming and Natural Languages**Zhangyin Feng, Daya Guo, Duyu Tang, et al.*2020 · 2,954 citations*](https://md.research.elamur.ai/free/en/papers/feng-2020-codebert-pre-trained-model-programming/)
86. [**A Diversity-Promoting Objective Function for Neural Conversation Models**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 2,925 citations*](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/)
87. [**Attention, Intentions, and the Structure of Discourse**Barbara J. Grosz, Candace L. Sidner*1986 · 2,923 citations*](https://md.research.elamur.ai/free/en/papers/grosz-1986-attention-intentions-structure-discourse/)
88. [**Learning to Rank using Gradient Descent**Chris Burges, Tal Shaked, Erin Renshaw, et al.*2005 · 2,896 citations*](https://md.research.elamur.ai/free/en/papers/burges-2005-learning-rank-using-gradient/)
89. [**Generating Typed Dependency Parses from Phrase Structure Parses**Marie-Catherine de Marneffe, Bill MacCartney, Christopher D. Manning*2006 · 2,886 citations*](https://md.research.elamur.ai/free/en/papers/marneffe-2006-generating-typed-dependency-parses-phrase/)
90. [**Unsupervised Word Sense Disambiguation Rivaling Supervised Methods**David Yarowsky*1995 · 2,840 citations*](https://md.research.elamur.ai/free/en/papers/yarowsky-1995-unsupervised-word-sense-disambiguation-rivaling/)
91. [**Self-Attention with Relative Position Representations**Peter Shaw, Jakob Uszkoreit, Ashish Vaswani*2018 · 2,811 citations*](https://md.research.elamur.ai/free/en/papers/shaw-2018-self-attention-relative-position-representations/)
92. [**Seeing Stars: Exploiting Class Relationships for Sentiment Categorization with Respect to Rating Scales**Bo Pang, Lillian Lee*2005 · 2,722 citations*](https://md.research.elamur.ai/free/en/papers/pang-2005-seeing-stars-exploiting-class-relationships/)
93. [**LXMERT: Learning Cross-Modality Encoder Representations from Transformers**Hao Tan, Mohit Bansal*2019 · 2,714 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-lxmert-learning-cross-modality-encoder/)
94. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,636 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
95. [**Scalable Diffusion Models with Transformers**William Peebles, Saining Xie*2022 · 2,595 citations*](https://md.research.elamur.ai/free/en/papers/peebles-2022-scalable-diffusion-models-transformers/)
96. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
97. [**Improved Baselines with Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,557 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/)
98. [**Lost in the Middle: How Language Models Use Long Contexts**Nelson F. Liu, Kevin Lin, John Hewitt, et al.*2023 · 2,540 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/)
99. [**Discriminative Training Methods for Hidden Markov Models: Theory and Experiments with Perceptron Algorithms**Michael Collins*2002 · 2,510 citations*](https://md.research.elamur.ai/free/en/papers/collins-2002-discriminative-training-methods-hidden-markov/)
100. [**Dynamic Topic Models**David M. Blei, John D. Lafferty*2006 · 2,508 citations*](https://md.research.elamur.ai/free/en/papers/blei-2006-dynamic-topic-models/)

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