---
title: "Who cites Deep Residual Learning for Image Recognition"
description: "5,237 papers of the Elamur Research library cite «Deep Residual Learning for Image Recognition», found in their bibliographies · page 1 of 3…"
canonical: "https://research.elamur.ai/free/en/papers/he-2015-deep-residual-learning-image-recognition/cited-by/"
---
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# Who cites «Deep Residual Learning for Image Recognition»

5,237 papers of our library cite it (of the 149,439 whose bibliographies we read) · 251 with a page here, the most cited first · page 1 of 3 · 1–100 · [← back to «Deep Residual Learning for Image Recognition»](https://md.research.elamur.ai/free/en/papers/he-2015-deep-residual-learning-image-recognition/)

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1. **[Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Ze Liu, Yutong Lin, Yue Cao, et al.*2021 · 33,968 citations*
2. [**An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale arXiv:2010.11929**Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, et al.*2020 · 23,726 citations*](https://md.research.elamur.ai/free/en/papers/dosovitskiy-2020-image-worth-16x16-words-transformers/)
3. [**Attention Is All You Need arXiv:1706.03762**Ashish Vaswani, Noam Shazeer, Niki Parmar, et al.*2017 · 17,458 citations*](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/)
4. **[Momentum Contrast for Unsupervised Visual Representation Learning](https://md.research.elamur.ai/free/en/papers/he-2019-momentum-contrast-unsupervised-visual-representation/) [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/)**Kaiming He, Haoqi Fan, Yuxin Wu, et al.*2019 · 13,302 citations*
5. [**TensorFlow: A system for large-scale machine learning arXiv:1605.08695**Martin Abadi, Paul Barham, Jianmin Chen, et al.*2016 · 12,711 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-system-large-scale-machine/)
6. [**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/)
7. [**A Comprehensive Survey on Graph Neural Networks arXiv:1901.00596**Zonghan Wu, Shirui Pan, Fengwen Chen, et al.*2019 · 10,312 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-comprehensive-survey-graph-neural-networks/)
8. [**MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications arXiv:1704.04861**Andrew G. Howard, Menglong Zhu, Bo Chen, et al.*2017 · 9,729 citations*](https://md.research.elamur.ai/free/en/papers/howard-2017-mobilenets-efficient-convolutional-neural-networks/)
9. [**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/)
10. [**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/)
11. [**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/)
12. [**A Simple Framework for Contrastive Learning of Visual Representations arXiv:2002.05709**Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.*2020 · 8,000 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-simple-framework-contrastive-learning-visual/)
13. **[Learning Transferable Architectures for Scalable Image Recognition](https://md.research.elamur.ai/free/en/papers/zoph-2017-learning-transferable-architectures-scalable-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/)**Barret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.*2017 · 6,583 citations*
14. **[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*
15. **[Emerging Properties in Self-Supervised Vision Transformers](https://md.research.elamur.ai/free/en/papers/caron-2021-emerging-properties-self-supervised-vision/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Mathilde Caron, Hugo Touvron, Ishan Misra, et al.*2021 · 6,046 citations*
16. **[EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/) [International Conference on Machine Learning (ICML)](https://md.research.elamur.ai/free/en/venues/international-conference-on-machine-learning/)**Mingxing Tan, Quoc V. Le*2019 · 5,416 citations*
17. [**WaveNet: A Generative Model for Raw Audio arXiv:1609.03499**Aaron van den Oord, Sander Dieleman, Heiga Zen, et al.*2016 · 5,269 citations*](https://md.research.elamur.ai/free/en/papers/oord-2016-wavenet-generative-model-raw-audio/)
18. **[Billion-scale similarity search with GPUs](https://md.research.elamur.ai/free/en/papers/johnson-2017-billion-scale-similarity-search-gpus/) [IEEE Transactions on Big Data](https://md.research.elamur.ai/free/en/venues/ieee-transactions-on-big-data/)**Jeff Johnson, Matthijs Douze, Hervé Jégou*2017 · 4,728 citations*
19. [**Neural Architecture Search with Reinforcement Learning arXiv:1611.01578**Barret Zoph, Quoc V. Le*2016 · 4,413 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
20. [**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/)
21. [**Convolutional Sequence to Sequence Learning arXiv:1705.03122**Jonas Gehring, Michael Auli, David Grangier, et al.*2017 · 3,042 citations*](https://md.research.elamur.ai/free/en/papers/gehring-2017-convolutional-sequence-sequence-learning/)
22. [**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/)
23. **[LXMERT: Learning Cross-Modality Encoder Representations from Transformers](https://md.research.elamur.ai/free/en/papers/tan-2019-lxmert-learning-cross-modality-encoder/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hao Tan, Mohit Bansal*2019 · 2,789 citations*
24. **[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*
25. [**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/)
26. **[Don't Give Me the Details, Just the Summary! Topic-Aware Convolutional Neural Networks for Extreme Summarization](https://md.research.elamur.ai/free/en/papers/narayan-2018-don-t-give-me-details/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Shashi Narayan, Shay B. Cohen, Mirella Lapata*2018 · 2,149 citations*
27. [**Focal Loss for Dense Object Detection arXiv:1708.02002**Tsung-Yi Lin, Priya Goyal, Ross Girshick, et al.*2017 · 1,995 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-focal-loss-dense-object-detection/)
28. **[Incorporating Copying Mechanism in Sequence-to-Sequence Learning](https://md.research.elamur.ai/free/en/papers/gu-2016-incorporating-copying-mechanism-sequence-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jiatao Gu, Zhengdong Lu, Hang Li, et al.*2016 · 1,915 citations*
29. [**Towards Deep Learning Models Resistant to Adversarial Attacks arXiv:1706.06083**Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, et al.*2017 · 1,874 citations*](https://md.research.elamur.ai/free/en/papers/madry-2017-towards-deep-learning-models-resistant/)
30. [**Unsupervised Data Augmentation for Consistency Training arXiv:1904.12848**Qizhe Xie, Zihang Dai, Eduard Hovy, et al.*2019 · 1,871 citations*](https://md.research.elamur.ai/free/en/papers/xie-2019-unsupervised-data-augmentation-consistency-training/)
31. [**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/)
32. [**VisualBERT: A Simple and Performant Baseline for Vision and Language arXiv:1908.03557**Liunian Harold Li, Mark Yatskar, Da Yin, et al.*2019 · 1,568 citations*](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/)
33. **[Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/) [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/)**Peter Anderson, Qi Wu, Damien Teney, et al.*2017 · 1,414 citations*
34. [**Language Modeling with Gated Convolutional Networks arXiv:1612.08083**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,346 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
35. **[Adversarial NLI: A New Benchmark for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/nie-2020-adversarial-nli-new-benchmark-natural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Nie, Adina Williams, Emily Dinan, et al.*2020 · 1,296 citations*
36. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
37. **[Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://md.research.elamur.ai/free/en/papers/goyal-2016-making-v-vqa-matter-elevating/) [International Journal of Computer Vision](https://md.research.elamur.ai/free/en/venues/international-journal-of-computer-vision/)**Yash Goyal, Tejas Khot, Douglas Summers-Stay, et al.*2016 · 1,281 citations*
38. **[Modeling Context in Referring Expressions](https://md.research.elamur.ai/free/en/papers/yu-2016-modeling-context-referring-expressions/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Licheng Yu, Patrick Poirson, Shan Yang, et al.*2016 · 1,203 citations*
39. [**MASS: Masked Sequence to Sequence Pre-training for Language Generation arXiv:1905.02450**Kaitao Song, Xu Tan, Tao Qin, et al.*2019 · 1,143 citations*](https://md.research.elamur.ai/free/en/papers/song-2019-mass-masked-sequence-sequence-pre/)
40. [**MS MARCO: A Human Generated MAchine Reading COmprehension Dataset arXiv:1611.09268**Payal Bajaj, Daniel Campos, Nick Craswell, et al.*2016 · 1,138 citations*](https://md.research.elamur.ai/free/en/papers/bajaj-2016-ms-marco-human-generated/)
41. **[Very Deep Convolutional Networks for Text Classification](https://md.research.elamur.ai/free/en/papers/conneau-2017-very-deep-convolutional-networks-text/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Alexis Conneau, Holger Schwenk, Loïc Barrault, et al.*2017 · 1,094 citations*
42. **[MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,016 citations*
43. [**Segment Anything arXiv:2304.02643**Alexander Kirillov, Eric Mintun, Nikhila Ravi, et al.*2023 · 960 citations*](https://md.research.elamur.ai/free/en/papers/kirillov-2023-segment-anything/)
44. [**DocVQA: A Dataset for VQA on Document Images arXiv:2007.00398**Minesh Mathew, Dimosthenis Karatzas, C.V. Jawahar*2020 · 844 citations*](https://md.research.elamur.ai/free/en/papers/mathew-2020-docvqa-dataset-vqa-document-images/)
45. **[Scaling Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/ott-2018-scaling-neural-machine-translation/) [Conference on Machine Translation: Research Papers](https://md.research.elamur.ai/free/en/venues/conference-on-machine-translation-research-papers/)**Myle Ott, Sergey Edunov, David Grangier, et al.*2018 · 826 citations*
46. **[Learning Deep Transformer Models for Machine Translation](https://md.research.elamur.ai/free/en/papers/wang-2019-learning-deep-transformer-models-machine/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Qiang Wang, Bei Li, Tong Xiao, et al.*2019 · 810 citations*
47. [**ViLT: Vision-and-Language Transformer Without Convolution or Region Supervision arXiv:2102.03334**Wonjae Kim, Bokyung Son, Ildoo Kim*2021 · 804 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-vilt-vision-language-transformer-convolution/)
48. [**Achieving Human Parity on Automatic Chinese to English News Translation arXiv:1803.05567**Hany Hassan, Anthony Aue, Chang Chen, et al.*2018 · 799 citations*](https://md.research.elamur.ai/free/en/papers/hassan-2018-achieving-human-parity-automatic-chinese/)
49. **[ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://md.research.elamur.ai/free/en/papers/shridhar-2019-alfred-benchmark-interpreting-grounded-instructions/) [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/)**Mohit Shridhar; Jesse Thomason; Daniel Gordon; Yonatan Bisk; Winson Han; Roozbeh Mottaghi; Luke Zettlemoyer;, Dieter Fox*2019 · 765 citations*
50. **[SentEval: An Evaluation Toolkit for Universal Sentence Representations](https://md.research.elamur.ai/free/en/papers/conneau-2018-senteval-evaluation-toolkit-universal-sentence/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Alexis Conneau, Douwe Kiela*2018 · 747 citations*
51. **[Dice Loss for Data-imbalanced NLP Tasks](https://md.research.elamur.ai/free/en/papers/li-2020-dice-loss-data-imbalanced-nlp/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xiaoya Li, Xiaofei Sun, Yuxian Meng, et al.*2020 · 735 citations*
52. [**Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations MAPL 2019**Philippe Tillet, H. T. Kung, David Cox*2019 · 729 citations*](https://md.research.elamur.ai/free/en/papers/tillet-2019-triton-intermediate-language-compiler-tiled/)
53. **[Generating Radiology Reports via Memory-driven Transformer](https://md.research.elamur.ai/free/en/papers/chen-2020-generating-radiology-reports-memory-driven/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhihong Chen, Yan Song, Tsung-Hui Chang, et al.*2020 · 715 citations*
54. **[TVQA: Localized, Compositional Video Question Answering](https://md.research.elamur.ai/free/en/papers/lei-2018-tvqa-localized-compositional-video-question/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jie Lei, Licheng Yu, Mohit Bansal, et al.*2018 · 687 citations*
55. [**Similarity of Neural Network Representations Revisited arXiv:1905.00414**Simon Kornblith, Mohammad Norouzi, Honglak Lee, et al.*2019 · 679 citations*](https://md.research.elamur.ai/free/en/papers/kornblith-2019-similarity-neural-network-representations-revisited/)
56. [**A Corpus for Reasoning About Natural Language Grounded in Photographs (NLVR2)**Alane Suhr, Stephanie Zhou, Ally Zhang, et al.*2019 · 670 citations*](https://md.research.elamur.ai/free/en/papers/suhr-2019-corpus-reasoning-natural-language-grounded/)
57. [**CoCa: Contrastive Captioners are Image-Text Foundation Models arXiv:2205.01917**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 667 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
58. **[Pointing the Unknown Words](https://md.research.elamur.ai/free/en/papers/gulcehre-2016-pointing-unknown-words/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, et al.*2016 · 651 citations*
59. **[Object Hallucination in Image Captioning](https://md.research.elamur.ai/free/en/papers/rohrbach-2018-object-hallucination-image-captioning/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Anna Rohrbach, Lisa Anne Hendricks, Kaylee Burns, et al.*2018 · 626 citations*
60. **[Transformation Networks for Target-Oriented Sentiment Classification](https://md.research.elamur.ai/free/en/papers/li-2018-transformation-networks-target-oriented-sentiment/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xin Li, Lidong Bing, Wai Lam, et al.*2018 · 600 citations*
61. [**Masked Autoencoders Are Scalable Vision Learners arXiv:2111.06377**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 596 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
62. **[Dissecting Contextual Word Embeddings: Architecture and Representation](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 592 citations*
63. **[OCR-free Document Understanding Transformer](https://md.research.elamur.ai/free/en/papers/kim-2021-ocr-free-document-understanding-transformer/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Geewook Kim, Teakgyu Hong, Moonbin Yim, et al.*2021 · 584 citations*
64. **[Massive Exploration of Neural Machine Translation Architectures](https://md.research.elamur.ai/free/en/papers/britz-2017-massive-exploration-neural-machine-translation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Denny Britz, Anna Goldie, Minh-Thang Luong, et al.*2017 · 574 citations*
65. **[Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement](https://md.research.elamur.ai/free/en/papers/lee-2018-deterministic-non-autoregressive-neural-sequence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jason Lee, Elman Mansimov, Kyunghyun Cho*2018 · 568 citations*
66. [**Reducing Transformer Depth on Demand with Structured Dropout arXiv:1909.11556**Angela Fan, Edouard Grave, Armand Joulin*2019 · 560 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
67. **[The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/chen-2018-best-both-worlds-combining-recent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mia Xu Chen, Orhan Firat, Ankur Bapna, et al.*2018 · 559 citations*
68. **[Simple, Scalable Adaptation for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/bapna-2019-simple-scalable-adaptation-neural-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ankur Bapna, Orhan Firat*2019 · 530 citations*
69. [**Training data-efficient image transformers & distillation through attention arXiv:2012.12877**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 524 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/)
70. [**Deep Learning Scaling is Predictable, Empirically arXiv:1712.00409**Joel Hestness, Sharan Narang, Newsha Ardalani, et al.*2017 · 518 citations*](https://md.research.elamur.ai/free/en/papers/hestness-2017-deep-learning-scaling-predictable-empirically/)
71. **[DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 516 citations*
72. [**Learned in Translation: Contextualized Word Vectors arXiv:1708.00107**Bryan McCann, James Bradbury, Caiming Xiong, et al.*2017 · 512 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2017-learned-translation-contextualized-word-vectors/)
73. **[A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 509 citations*
74. [**Regularizing Neural Networks by Penalizing Confident Output Distributions arXiv:1701.06548**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 496 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
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