---
title: "Who cites Jia Hao Deng — 3,783 papers of our library"
description: "3,783 papers of the Elamur Research library cite the works of Jia Hao Deng, found in their bibliographies · page 1 of 3 · 1–100; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Jia Hao Deng](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/) / cited by

# Who cites Jia Hao Deng

4,007 citations of their works from 3,783 papers of our library (of the 149,439 whose bibliographies we read) · 281 with a page here, the most cited first · page 1 of 3 · 1–100 · [← Jia Hao Deng](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/)

**1** [2](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/page/2/)

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. **[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*
4. **[Rich feature hierarchies for accurate object detection and semantic segmentation](https://md.research.elamur.ai/free/en/papers/girshick-2013-rich-feature-hierarchies-accurate-object/) [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/)**Ross Girshick, Jeff Donahue, Trevor Darrell, et al.*2013 · 31,930 citations*
5. [**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/)
6. **[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*
7. [**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/)
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. **[SQuAD: 100,000+ Questions for Machine Comprehension of Text](https://md.research.elamur.ai/free/en/papers/rajpurkar-2016-squad-100-000-questions-machine/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Pranav Rajpurkar, Jian Zhang, Konstantin Lopyrev, et al.*2016 · 9,621 citations*
10. [**Deep Residual Learning for Image Recognition arXiv:1512.03385**Kaiming He, Xiangyu Zhang, Shaoqing Ren, et al.*2015 · 9,425 citations*](https://md.research.elamur.ai/free/en/papers/he-2015-deep-residual-learning-image-recognition/)
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. [**Explaining and Harnessing Adversarial Examples arXiv:1412.6572**Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy*2014 · 8,577 citations*](https://md.research.elamur.ai/free/en/papers/goodfellow-2014-explaining-harnessing-adversarial-examples/)
13. [**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/)
14. [**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/)
15. [**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/)
16. [**Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks arXiv:1511.06434**Alec Radford, Luke Metz, Soumith Chintala*2015 · 6,654 citations*](https://md.research.elamur.ai/free/en/papers/radford-2015-unsupervised-representation-learning-deep-convolutional/)
17. **[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*
18. **[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*
19. **[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*
20. [**Intriguing properties of neural networks arXiv:1312.6199**Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, et al.*2013 · 5,999 citations*](https://md.research.elamur.ai/free/en/papers/szegedy-2013-intriguing-properties-neural-networks/)
21. **[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*
22. **[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*
23. **[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*
24. [**Microsoft COCO: Common Objects in Context arXiv:1405.0312**Tsung-Yi Lin, Michael Maire, Serge Belongie, et al.*2014 · 5,131 citations*](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
25. [**mixup: Beyond Empirical Risk Minimization arXiv:1710.09412**Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, et al.*2017 · 4,928 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2017-mixup-empirical-risk-minimization/)
26. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
27. [**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/)
28. **[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*
29. [**ImageNet Classification with Deep Convolutional Neural Networks NeurIPS 2012**Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton*2012 · 2,801 citations*](https://md.research.elamur.ai/free/en/papers/krizhevsky-2012-imagenet-classification-deep-convolutional/)
30. **[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*
31. [**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/)
32. [**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/)
33. [**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/)
34. [**DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models arXiv:2402.03300**Zhihong Shao, Peiyi Wang, Qihao Zhu, et al.*2024 · 2,115 citations*](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/)
35. **[GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering](https://md.research.elamur.ai/free/en/papers/hudson-2019-gqa-new-dataset-real-world/) [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/)**Drew A. Hudson, Christopher D. Manning*2019 · 2,078 citations*
36. **[Reading Tea Leaves: How Humans Interpret Topic Models](https://md.research.elamur.ai/free/en/papers/chang-2009-reading-tea-leaves-humans-interpret-topic/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, et al.*2009 · 2,053 citations*
37. [**MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems arXiv:1512.01274**Tianqi Chen, Mu Li, Yutian Li, et al.*2015 · 2,000 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-mxnet-flexible-efficient-machine-learning/)
38. [**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/)
39. [**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/)
40. [**Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj**Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, et al.*2022 · 1,756 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2022-imitation-game-quantifying-extrapolating-capabilities/)
41. **[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*
42. **[Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task](https://md.research.elamur.ai/free/en/papers/yu-2018-spider-large-scale-human-labeled/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Tao Yu, Rui Zhang, Kai Yang, et al.*2018 · 1,602 citations*
43. [**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/)
44. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
45. [**Zero-Shot Text-to-Image Generation arXiv:2102.12092**Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/)
46. [**Understanding Black-box Predictions via Influence Functions arXiv:1703.04730**Pang Wei Koh, Percy Liang*2017 · 1,514 citations*](https://md.research.elamur.ai/free/en/papers/koh-2017-understanding-black-box-predictions-influence/)
47. [**DINOv2: Learning Robust Visual Features without Supervision arXiv:2304.07193**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
48. [**LAION-5B: An open large-scale dataset for training next generation image-text models arXiv:2210.08402**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
49. **[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*
50. **[Generation and Comprehension of Unambiguous Object Descriptions](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/) [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/)**Junhua Mao, Jonathan Huang, Alexander Toshev, et al.*2015 · 1,388 citations*
51. [**Sequence Level Training with Recurrent Neural Networks arXiv:1511.06732**Marc'Aurelio Ranzato, Sumit Chopra, Michael Auli, et al.*2015 · 1,370 citations*](https://md.research.elamur.ai/free/en/papers/ranzato-2015-sequence-level-training-recurrent-neural/)
52. **[ReferItGame: Referring to Objects in Photographs of Natural Scenes](https://md.research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, et al.*2014 · 1,317 citations*
53. **[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*
54. **[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*
55. **[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*
56. [**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/)
57. [**Classifier-Free Diffusion Guidance arXiv:2207.12598**Jonathan Ho, Tim Salimans*2022 · 1,116 citations*](https://md.research.elamur.ai/free/en/papers/ho-2022-classifier-free-diffusion-guidance/)
58. [**VL-BERT: Pre-training of Generic Visual-Linguistic Representations arXiv:1908.08530**Weijie Su, Xizhou Zhu, Yue Cao, et al.*2019 · 1,066 citations*](https://md.research.elamur.ai/free/en/papers/su-2019-vl-bert-pre-training-generic/)
59. **[Effective Use of Word Order for Text Categorization with Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/johnson-2015-effective-use-word-order-text/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Rie Johnson, Tong Zhang*2015 · 971 citations*
60. **[ImageBind: One Embedding Space To Bind Them All](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/) [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/)**Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, et al.*2023 · 954 citations*
61. **[Visual7W: Grounded Question Answering in Images](https://md.research.elamur.ai/free/en/papers/zhu-2015-visual7w-grounded-question-answering-images/) [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/)**Yuke Zhu, Oliver Groth, Michael Bernstein, et al.*2015 · 952 citations*
62. [**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/)
63. [**CIDEr: Consensus-based Image Description Evaluation arXiv:1411.5726**Ramakrishna Vedantam, C. Lawrence Zitnick, Devi Parikh*2014 · 827 citations*](https://md.research.elamur.ai/free/en/papers/vedantam-2014-cider-consensus-based-image-description/)
64. [**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/)
65. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) [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/)**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 769 citations*
66. **[Learning Distributed Representations of Sentences from Unlabelled Data](https://md.research.elamur.ai/free/en/papers/hill-2016-learning-distributed-representations-sentences-unlabelled/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Felix Hill, Kyunghyun Cho, Anna Korhonen*2016 · 765 citations*
67. **[MMLU-Pro: A More Robust and Challenging Multi-Task Language Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/wang-2024-mmlu-pro-more-robust-challenging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yubo Wang, Xueguang Ma, Ge Zhang, et al.*2024 · 761 citations*
68. [**Show and Tell: A Neural Image Caption Generator arXiv:1411.4555**Oriol Vinyals, Alexander Toshev, Samy Bengio, et al.*2014 · 729 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2014-show-tell-neural-image-caption/)
69. **[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*
70. [**Learning multiple visual domains with residual adapters arXiv:1705.08045**Sylvestre-Alvise Rebuffi, Hakan Bilen, Andrea Vedaldi*2017 · 694 citations*](https://md.research.elamur.ai/free/en/papers/rebuffi-2017-learning-multiple-visual-domains-residual/)
71. **[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*
72. [**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/)
73. [**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/)
74. [**e-SNLI: Natural Language Inference with Natural Language Explanations arXiv:1812.01193**Oana-Maria Camburu, Tim Rocktäschel, Thomas Lukasiewicz, et al.*2018 · 622 citations*](https://md.research.elamur.ai/free/en/papers/camburu-2018-e-snli-natural-language-inference/)
75. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations*
76. [**SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis arXiv:2307.01952**Dustin Podell, Zion English, Kyle Lacey, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/podell-2023-sdxl-improving-latent-diffusion-models/)
77. [**Deep Visual-Semantic Alignments for Generating Image Descriptions arXiv:1412.2306**Andrej Karpathy, Li Fei-Fei*2014 · 608 citations*](https://md.research.elamur.ai/free/en/papers/karpathy-2014-deep-visual-semantic-alignments-generating/)
78. [**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/)
79. **[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*
80. **[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*
81. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models arXiv:2309.12284**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
82. **[MIND: A Large-scale Dataset for News Recommendation](https://md.research.elamur.ai/free/en/papers/wu-2020-mind-large-scale-dataset-news/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Fangzhao Wu, Ying Qiao, Jiun-Hung Chen, et al.*2020 · 564 citations*
83. [**Unleashing the potential of prompt engineering for large language models arXiv:2310.14735 · Patterns 6(6) 101,260 (2025)**Banghao Chen, Zhaofeng Zhang, Nicolas Langrene, et al.*2023 · 536 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-unleashing-potential-prompt-engineering-large/)
84. [**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/)
85. [**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/)
86. [**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/)
87. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time arXiv:2203.05482**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 507 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
88. **[Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://md.research.elamur.ai/free/en/papers/wang-2024-math-shepherd-verify-reinforce-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiyi Wang, Lei Li, Zhihong Shao, et al.*2024 · 497 citations*
89. **[The Ubuntu Dialogue Corpus: A Large Dataset for Research in Unstructured Multi-Turn Dialogue Systems](https://md.research.elamur.ai/free/en/papers/lowe-2015-ubuntu-dialogue-corpus-large-dataset/) [Annual Meeting of the Special Interest Group on Discourse and Dialogue (SIGDIAL)](https://md.research.elamur.ai/free/en/venues/sigdial/)**Ryan Lowe, Nissan Pow, Iulian Serban, et al.*2015 · 494 citations*
90. [**Flow Matching for Generative Modeling arXiv:2210.02747**Yaron Lipman, Ricky T. Q. Chen, Heli Ben-Hamu, et al.*2022 · 481 citations*](https://md.research.elamur.ai/free/en/papers/lipman-2022-flow-matching-generative-modeling/)
91. **[HERO: Hierarchical Encoder for Video+Language Omni-representation Pre-training](https://md.research.elamur.ai/free/en/papers/li-2020-hero-hierarchical-encoder-video-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Linjie Li, Yen-Chun Chen, Yu Cheng, et al.*2020 · 481 citations*
92. [**CogView: Mastering Text-to-Image Generation via Transformers arXiv:2105.13290**Ming Ding, Zhuoyi Yang, Wenyi Hong, et al.*2021 · 469 citations*](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)
93. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework arXiv:2202.03052**Peng Wang, An Yang, Rui Men, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
94. **[MVBench: A Comprehensive Multi-modal Video Understanding Benchmark](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/) [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/)**Kunchang Li, Yali Wang, Yinan He, et al.*2023 · 463 citations*
95. **[Multi-Modal Sarcasm Detection in Twitter with Hierarchical Fusion Model](https://md.research.elamur.ai/free/en/papers/cai-2019-multi-modal-sarcasm-detection-twitter/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yitao Cai, Huiyu Cai, Xiaojun Wan*2019 · 451 citations*
96. **[oLMpics — On what Language Model Pre-training Captures](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 440 citations*
97. **[Experience Grounds Language](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 439 citations*
98. [**InternVL: Scaling up Vision Foundation Models and Aligning for Generic Visual-Linguistic Tasks arXiv:2312.14238**Zhe Chen, Jiannan Wu, Wenhai Wang, et al.*2023 · 435 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-internvl-scaling-up-vision/)
99. [**Qwen2.5-Math Technical Report: Toward Mathematical Expert Model via Self-Improvement arXiv:2409.12122**An Yang, Beichen Zhang, Binyuan Hui, et al.*2024 · 417 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-qwen2-5-math-technical-report/)
100. [**Deep Learning: A Critical Appraisal arXiv:1801.00631**Gary Marcus*2018 · 417 citations*](https://md.research.elamur.ai/free/en/papers/marcus-2018-deep-learning-critical-appraisal/)

**1** [2](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/page/2/) [3](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/page/3/) [next →](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/cited-by/page/2/)

- [← back to](https://md.research.elamur.ai/free/en/authors/jia-hao-deng/) — Jia Hao Deng