---
title: "Who cites Richard S. Zemel — 4,143 papers of our library"
description: "4,143 papers of the Elamur Research library cite the works of Richard S. Zemel, found in their bibliographies · page 1 of 7 · 1–100; the most…"
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---
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# Who cites Richard S. Zemel

4,711 citations of their works from 4,143 papers of our library (of the 149,439 whose bibliographies we read) · 622 with a page here, the most cited first · page 1 of 7 · 1–100 · [← Richard S. Zemel](https://md.research.elamur.ai/free/en/authors/richard-s-zemel/)

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1. [**Very Deep Convolutional Networks for Large-Scale Image Recognition arXiv:1409.1556**Karen Simonyan, Andrew Zisserman*2014 · 73,393 citations*](https://md.research.elamur.ai/free/en/papers/simonyan-2014-very-deep-convolutional-networks-large/)
2. **[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 56,209 citations*
3. **[Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://md.research.elamur.ai/free/en/papers/reimers-2019-sentence-bert-sentence-embeddings-siamese/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Nils Reimers, Iryna Gurevych*2019 · 17,096 citations*
4. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
5. **[A Comprehensive Survey on Graph Neural Networks](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/) [IEEE Transactions on Neural Networks and Learning Systems](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-neural-networks-and-learning-systems/)**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,312 citations*
6. [**RoBERTa: A Robustly Optimized BERT Pretraining Approach arXiv:1907.11692**Yinhan Liu, Myle Ott, Naman Goyal, et al.*2019 · 9,837 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/)
7. **[Effective Approaches to Attention-based Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/luong-2015-effective-approaches-attention-based-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thang Luong, Hieu Pham, Christopher D. Manning*2015 · 9,718 citations*
8. [**Graph Attention Networks arXiv:1710.10903**Petar Veličkovic, Guillem Cucurull, Arantxa Casanova, et al.*2017 · 9,035 citations*](https://md.research.elamur.ai/free/en/papers/veli-vckovic-2017-graph-attention-networks/)
9. [**Semi-Supervised Classification with Graph Convolutional Networks arXiv:1609.02907**Thomas N. Kipf, Max Welling*2016 · 9,023 citations*](https://md.research.elamur.ai/free/en/papers/kipf-2016-semi-supervised-classification-graph-convolutional/)
10. [**Learning Transferable Visual Models From Natural Language Supervision arXiv:2103.00020**Alec Radford, Jong Wook Kim, Chris Hallacy, et al.*2021 · 8,767 citations*](https://md.research.elamur.ai/free/en/papers/radford-2021-learning-transferable-visual-models-natural/)
11. [**Show, Attend and Tell: Neural Image Caption Generation with Visual Attention arXiv:1502.03044**Kelvin Xu, Jimmy Ba, Ryan Kiros, et al.*2015 · 7,670 citations*](https://md.research.elamur.ai/free/en/papers/xu-2015-show-attend-tell-neural-image/)
12. **[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/) [EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-blackboxnlp-analyzing-and-interpreting-neural-networks-for-nlp/)**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,276 citations*
13. [**Representation Learning with Contrastive Predictive Coding arXiv:1807.03748**Aaron van den Oord, Yazhe Li, Oriol Vinyals*2018 · 7,022 citations*](https://md.research.elamur.ai/free/en/papers/oord-2018-representation-learning-contrastive-predictive-coding/)
14. [**DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter arXiv:1910.01108**Victor Sanh, Lysandre Debut, Julien Chaumond, et al.*2019 · 6,751 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2019-distilbert-distilled-version-bert-smaller/)
15. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
16. **[Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://md.research.elamur.ai/free/en/papers/selvaraju-2016-grad-cam-visual-explanations-deep/) [International Journal of Computer Vision](https://md.research.elamur.ai/free/en/venues/international-journal-of-computer-vision/)**Ramprasaath R. Selvaraju, Michael Cogswell, Abhishek Das, et al.*2016 · 6,394 citations*
17. **[Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://md.research.elamur.ai/free/en/papers/krishna-2016-visual-genome-connecting-language-vision/) [International Journal of Computer Vision](https://md.research.elamur.ai/free/en/venues/international-journal-of-computer-vision/)**Ranjay Krishna, Yuke Zhu, Oliver Groth, et al.*2016 · 5,876 citations*
18. **[A Survey on Bias and Fairness in Machine Learning](https://md.research.elamur.ai/free/en/papers/mehrabi-2019-survey-bias-fairness-machine-learning/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Ninareh Mehrabi, Fred Morstatter, Nripsuta Saxena, et al.*2019 · 5,638 citations*
19. **[Hierarchical Attention Networks for Document Classification](https://md.research.elamur.ai/free/en/papers/yang-2016-hierarchical-attention-networks-document-classification/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Zichao Yang, Diyi Yang, Chris Dyer, et al.*2016 · 5,436 citations*
20. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
21. [**Inductive Representation Learning on Large Graphs arXiv:1706.02216**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,795 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
22. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
23. **[SimCSE: Simple Contrastive Learning of Sentence Embeddings](https://md.research.elamur.ai/free/en/papers/gao-2021-simcse-simple-contrastive-learning-sentence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tianyu Gao, Xingcheng Yao, Danqi Chen*2021 · 3,942 citations*
24. [**Longformer: The Long-Document Transformer arXiv:2004.05150**Iz Beltagy, Matthew E. Peters, Arman Cohan*2020 · 3,880 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2020-longformer-long-document-transformer/)
25. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding arXiv:1906.08237**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,857 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
26. [**Neural Message Passing for Quantum Chemistry arXiv:1704.01212**Justin Gilmer, Samuel S. Schoenholz, Patrick F. Riley, et al.*2017 · 3,793 citations*](https://md.research.elamur.ai/free/en/papers/gilmer-2017-neural-message-passing-quantum-chemistry/)
27. [**Categorical Reparameterization with Gumbel-Softmax arXiv:1611.01144**Eric Jang, Shixiang Gu, Ben Poole*2016 · 3,656 citations*](https://md.research.elamur.ai/free/en/papers/jang-2016-categorical-reparameterization-gumbel-softmax/)
28. [**Measuring Massive Multitask Language Understanding arXiv:2009.03300**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,620 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
29. [**Scaling Laws for Neural Language Models arXiv:2001.08361**Jared Kaplan, Sam McCandlish, Tom Henighan, et al.*2020 · 3,434 citations*](https://md.research.elamur.ai/free/en/papers/kaplan-2020-scaling-laws-neural-language-models/)
30. **[TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,263 citations*
31. [**Towards A Rigorous Science of Interpretable Machine Learning arXiv:1702.08608**Finale Doshi-Velez, Been Kim*2017 · 3,226 citations*](https://md.research.elamur.ai/free/en/papers/doshi-velez-2017-towards-rigorous-science-interpretable-machine/)
32. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
33. **[Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/) [IEEE International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-international-conference-on-computer-vision-iccv/)**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,083 citations*
34. [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 3,028 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/)
35. **[Language Models as Knowledge Bases?](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 3,028 citations*
36. **[A Diversity-Promoting Objective Function for Neural Conversation Models](https://md.research.elamur.ai/free/en/papers/li-2016-diversity-promoting-objective-function-neural/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiwei Li, Michel Galley, Chris Brockett, et al.*2016 · 2,960 citations*
37. **[A Survey of Large Language Models](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/) [Frontiers of Computer Science](https://md.research.elamur.ai/free/en/venues/frontiers-of-computer-science/)**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,875 citations*
38. [**What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? arXiv:1703.04977**Alex Kendall, Yarin Gal*2017 · 2,870 citations*](https://md.research.elamur.ai/free/en/papers/kendall-2017-uncertainties-need-bayesian-deep-learning/)
39. [**ALBERT: A Lite BERT for Self-supervised Learning of Language Representations arXiv:1909.11942**Zhenzhong Lan, Mingda Chen, Sebastian Goodman, et al.*2019 · 2,806 citations*](https://md.research.elamur.ai/free/en/papers/lan-2019-albert-lite-bert-self-supervised/)
40. [**Stochastic Backpropagation and Approximate Inference in Deep Generative Models arXiv:1401.4082**Danilo Jimenez Rezende, Shakir Mohamed, Daan Wierstra*2014 · 2,765 citations*](https://md.research.elamur.ai/free/en/papers/rezende-2014-stochastic-backpropagation-approximate-inference-deep/)
41. [**Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing arXiv:2007.15779**Yu Gu; Robert Tinn; Hao Cheng; Michael Lucas; Naoto Usuyama; Xiaodong Liu; Tristan Naumann; Jianfeng Gao;, Hoifung Poon*2020 · 2,674 citations*](https://md.research.elamur.ai/free/en/papers/gu-2020-domain-specific-language-model-pretraining/)
42. **[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,616 citations*
43. [**Robust Speech Recognition via Large-Scale Weak Supervision arXiv:2212.04356**Alec Radford, Jong Wook Kim, Tao Xu, et al.*2022 · 2,546 citations*](https://md.research.elamur.ai/free/en/papers/radford-2022-robust-speech-recognition-large-scale/)
44. [**Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding arXiv:2205.11487**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2022 · 2,533 citations*](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/)
45. **[OpenNMT: Open-Source Toolkit for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/klein-2017-opennmt-open-source-toolkit-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guillaume Klein, Yoon Kim, Yuntian Deng, et al.*2017 · 2,475 citations*
46. **[Generating Sentences from a Continuous Space](https://md.research.elamur.ai/free/en/papers/bowman-2016-generating-sentences-continuous-space/) [Conference on Computational Natural Language Learning (CoNLL)](https://md.research.elamur.ai/free/en/venues/conll/)**Samuel R. Bowman, Luke Vilnis, Oriol Vinyals, et al.*2016 · 2,435 citations*
47. **[Self-critical Sequence Training for Image Captioning](https://md.research.elamur.ai/free/en/papers/rennie-2016-self-critical-sequence-training-image/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, et al.*2016 · 2,404 citations*
48. **[Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Piyush Sharma, Nan Ding, Sebastian Goodman, et al.*2018 · 2,348 citations*
49. [**Relational inductive biases, deep learning, and graph networks arXiv:1806.01261**Peter W. Battaglia, Jessica B. Hamrick, Victor Bapst, et al.*2018 · 2,335 citations*](https://md.research.elamur.ai/free/en/papers/battaglia-2018-relational-inductive-biases-deep-learning/)
50. [**ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks arXiv:1908.02265**Jiasen Lu, Dhruv Batra, Devi Parikh, et al.*2019 · 2,302 citations*](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/)
51. [**Equality of Opportunity in Supervised Learning arXiv:1610.02413**Moritz Hardt, Eric Price, Nathan Srebro*2016 · 2,262 citations*](https://md.research.elamur.ai/free/en/papers/hardt-2016-equality-opportunity-supervised-learning/)
52. [**Flamingo: a Visual Language Model for Few-Shot Learning arXiv:2204.14198**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,246 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
53. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,227 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
54. **[Attention-Based Bidirectional Long Short-Term Memory Networks for Relation Classification](https://md.research.elamur.ai/free/en/papers/zhou-2016-attention-based-bidirectional-long-short/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peng Zhou, Wei Shi, Jun Tian, et al.*2016 · 2,219 citations*
55. **[Stacked Attention Networks for Image Question Answering](https://md.research.elamur.ai/free/en/papers/yang-2015-stacked-attention-networks-image-question/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Zichao Yang, Xiaodong He, Jianfeng Gao, et al.*2015 · 2,157 citations*
56. [**Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings arXiv:1607.06520**Tolga Bolukbasi, Kai-Wei Chang, James Zou, et al.*2016 · 2,079 citations*](https://md.research.elamur.ai/free/en/papers/bolukbasi-2016-man-computer-programmer-woman-homemaker/)
57. [**Microsoft COCO Captions: Data Collection and Evaluation Server arXiv:1504.00325**Xinlei Chen, Hao Fang, Tsung-Yi Lin, et al.*2015 · 1,948 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-microsoft-coco-captions-data-collection/)
58. **[Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Xiujun Li, Xi Yin, Chunyuan Li, et al.*2020 · 1,877 citations*
59. **[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,861 citations*
60. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
61. **[SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation](https://md.research.elamur.ai/free/en/papers/cer-2017-semeval-2017-task-1-semantic/) [International Workshop on Semantic Evaluation (SemEval)](https://md.research.elamur.ai/free/en/venues/semeval/)**Daniel Cer, Mona Diab, Eneko Agirre, et al.*2017 · 1,828 citations*
62. [**Layer Normalization arXiv:1607.06450**Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton*2016 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/)
63. [**Attention-Based Models for Speech Recognition arXiv:1506.07503**Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk, et al.*2015 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/chorowski-2015-attention-based-models-speech-recognition/)
64. [**A Structured Self-attentive Sentence Embedding arXiv:1703.03130**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,797 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
65. [**Deep Unsupervised Learning using Nonequilibrium Thermodynamics arXiv:1503.03585**Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, et al.*2015 · 1,761 citations*](https://md.research.elamur.ai/free/en/papers/sohl-dickstein-2015-deep-unsupervised-learning-using-nonequilibrium/)
66. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
67. [**OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,755 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
68. **[How NOT To Evaluate Your Dialogue System: An Empirical Study of Unsupervised Evaluation Metrics for Dialogue Response Generation](https://md.research.elamur.ai/free/en/papers/liu-2016-how-not-evaluate-your-dialogue/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Chia-Wei Liu, Ryan Lowe, Iulian Serban, et al.*2016 · 1,752 citations*
69. **[Language (Technology) is Power: A Critical Survey of “Bias” in NLP](https://md.research.elamur.ai/free/en/papers/blodgett-2020-language-technology-is-power-critical/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Su Lin Blodgett, Solon Barocas, Hal Daumé III, et al.*2020 · 1,716 citations*
70. **[Document Modeling with Gated Recurrent Neural Network for Sentiment Classification](https://md.research.elamur.ai/free/en/papers/tang-2015-document-modeling-gated-recurrent-neural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Duyu Tang, Bing Qin, Ting Liu*2015 · 1,686 citations*
71. [**Universal Sentence Encoder arXiv:1803.11175**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
72. [**The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables arXiv:1611.00712**Chris J. Maddison, Andriy Mnih, Yee Whye Teh*2016 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/maddison-2016-concrete-distribution-continuous-relaxation-discrete/)
73. **[Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,615 citations*
74. [**REALM: Retrieval-Augmented Language Model Pre-Training arXiv:2002.08909**Kelvin Guu, Kenton Lee, Zora Tung, et al.*2020 · 1,580 citations*](https://md.research.elamur.ai/free/en/papers/guu-2020-realm-retrieval-augmented-language-model/)
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97. [**Matching Networks for One Shot Learning arXiv:1606.04080**Oriol Vinyals, Charles Blundell, Timothy Lillicrap, et al.*2016 · 1,197 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2016-matching-networks-one-shot-learning/)
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