---
title: "Who cites Geoffrey E. Hinton — 10,217 papers of our library"
description: "10,217 papers of the Elamur Research library cite the works of Geoffrey E. Among them: Adam: A Method for Stochastic Optimization…"
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---
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# Who cites Geoffrey E. Hinton

11,868 citations of their works from 10,217 papers of our library (of the 149,439 whose bibliographies we read) · 1,026 with a page here, the most cited first · page 1 of 11 · 1–100 · [← Geoffrey E. Hinton](https://md.research.elamur.ai/free/en/authors/geoffrey-e-hinton/)

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1. [**Adam: A Method for Stochastic Optimization arXiv:1412.6980**Diederik P. Kingma, Jimmy Ba*2014 · 90,277 citations*](https://md.research.elamur.ai/free/en/papers/kingma-2014-adam-method-stochastic-optimization/)
2. [**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/)
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. **[Learning Phrase Representations using RNN Encoder–Decoder for Statistical Machine Translation](https://md.research.elamur.ai/free/en/papers/cho-2014-learning-phrase-representations-rnn-encoder/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, et al.*2014 · 26,725 citations*
6. [**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/)
7. [**Sequence to Sequence Learning with Neural Networks arXiv:1409.3215**Ilya Sutskever, Oriol Vinyals, Quoc V. Le*2014 · 18,409 citations*](https://md.research.elamur.ai/free/en/papers/sutskever-2014-sequence-sequence-learning-neural-networks/)
8. [**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/)
9. **[Convolutional Neural Networks for Sentence Classification](https://md.research.elamur.ai/free/en/papers/kim-2014-convolutional-neural-networks-sentence-classification/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Yoon Kim*2014 · 16,299 citations*
10. [**Distilling the Knowledge in a Neural Network arXiv:1503.02531**Geoffrey Hinton, Oriol Vinyals, Jeff Dean*2015 · 15,229 citations*](https://md.research.elamur.ai/free/en/papers/hinton-2015-distilling-knowledge-neural-network/)
11. **[Deep Contextualized Word Representations](https://md.research.elamur.ai/free/en/papers/peters-2018-deep-contextualized-word-representations/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Matthew E. Peters, Mark Neumann, Mohit Iyyer, et al.*2018 · 14,944 citations*
12. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
13. **[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*
14. **[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,856 citations*
15. [**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/)
16. [**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/)
17. [**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/)
18. [**TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems arXiv:1603.04467**Martín Abadi, Ashish Agarwal, Paul Barham, et al.*2016 · 9,512 citations*](https://md.research.elamur.ai/free/en/papers/abadi-2016-tensorflow-large-scale-machine-learning/)
19. [**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/)
20. [**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/)
21. [**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/)
22. [**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/)
23. [**LoRA: Low-Rank Adaptation of Large Language Models arXiv:2106.09685**Edward J. Hu, Yelong Shen, Phillip Wallis, et al.*2021 · 8,661 citations*](https://md.research.elamur.ai/free/en/papers/hu-2021-lora-low-rank-adaptation-large/)
24. [**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/)
25. [**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/)
26. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
27. [**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/)
28. [**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/)
29. **[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*
30. **[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*
31. **[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*
32. [**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/)
33. **[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*
34. [**Distributed Representations of Sentences and Documents arXiv:1405.4053**Quoc V. Le, Tomas Mikolov*2014 · 5,803 citations*](https://md.research.elamur.ai/free/en/papers/le-2014-distributed-representations-sentences-documents/)
35. **[A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://md.research.elamur.ai/free/en/papers/williams-2018-broad-coverage-challenge-corpus-sentence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Adina Williams, Nikita Nangia, Samuel R. Bowman*2018 · 5,551 citations*
36. **[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*
37. **[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*
38. **[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*
39. **[Neural Architectures for Named Entity Recognition](https://md.research.elamur.ai/free/en/papers/lample-2016-neural-architectures-named-entity-recognition/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Guillaume Lample, Miguel Ballesteros, Sandeep Subramanian, et al.*2016 · 5,319 citations*
40. **[A large annotated corpus for learning natural language inference](https://md.research.elamur.ai/free/en/papers/bowman-2015-large-annotated-corpus-learning-natural/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Samuel R. Bowman, Gabor Angeli, Christopher Potts, et al.*2015 · 5,299 citations*
41. [**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/)
42. [**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/)
43. [**Character-level Convolutional Networks for Text Classification arXiv:1509.01626**Xiang Zhang, Junbo Zhao, Yann LeCun*2015 · 4,827 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/)
44. [**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/)
45. **[DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-r1-incentivizing-reasoning-capability/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**DeepSeek-AI, Daya Guo, Dejian Yang, et al.*2025 · 4,728 citations*
46. **[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*
47. [**Efficient Memory Management for Large Language Model Serving with PagedAttention arXiv:2309.06180**Woosuk Kwon, Zhuohan Li, Siyuan Zhuang, et al.*2023 · 4,531 citations*](https://md.research.elamur.ai/free/en/papers/kwon-2023-efficient-memory-management-large-language/)
48. [**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/)
49. [**Caffe: Convolutional Architecture for Fast Feature Embedding arXiv:1408.5093**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,209 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
50. **[fairseq: A Fast, Extensible Toolkit for Sequence Modeling](https://md.research.elamur.ai/free/en/papers/ott-2019-fairseq-fast-extensible-toolkit-sequence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Myle Ott, Sergey Edunov, Alexei Baevski, et al.*2019 · 3,984 citations*
51. **[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*
52. **[SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition](https://md.research.elamur.ai/free/en/papers/park-2019-specaugment-simple-data-augmentation-method/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Daniel S. Park, William Chan, Yu Zhang, et al.*2019 · 3,837 citations*
53. [**Bidirectional LSTM-CRF Models for Sequence Tagging arXiv:1508.01991**Zhiheng Huang, Wei Xu, Kai Yu*2015 · 3,822 citations*](https://md.research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/)
54. [**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/)
55. [**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/)
56. **[Linguistic Regularities in Continuous Space Word Representations](https://md.research.elamur.ai/free/en/papers/mikolov-2013-linguistic-regularities-continuous-space-word/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tomas Mikolov, Wen-tau Yih, Geoffrey Zweig*2013 · 3,513 citations*
57. **[Conformer: Convolution-augmented Transformer for Speech Recognition](https://md.research.elamur.ai/free/en/papers/gulati-2020-conformer-convolution-augmented-transformer-speech/) [Interspeech](https://md.research.elamur.ai/free/en/venues/interspeech-usb/)**Anmol Gulati, James Qin, Chung-Cheng Chiu, et al.*2020 · 3,475 citations*
58. [**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/)
59. **[A Neural Attention Model for Abstractive Sentence Summarization](https://md.research.elamur.ai/free/en/papers/rush-2015-neural-attention-model-abstractive-sentence/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexander M. Rush, Sumit Chopra, Jason Weston*2015 · 3,390 citations*
60. [**Continual Lifelong Learning with Neural Networks: A Review arXiv:1802.07569**German I. Parisi, Ronald Kemker, Jose L. Part, et al.*2018 · 3,224 citations*](https://md.research.elamur.ai/free/en/papers/parisi-2018-continual-lifelong-learning-neural-networks/)
61. [**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/)
62. [**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/)
63. **[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*
64. **[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*
65. **[Self-Attention with Relative Position Representations](https://md.research.elamur.ai/free/en/papers/shaw-2018-self-attention-relative-position-representations/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Peter Shaw, Jakob Uszkoreit, Ashish Vaswani*2018 · 2,848 citations*
66. [**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/)
67. [**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/)
68. **[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*
69. **[Improved Baselines with Visual Instruction Tuning](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/) [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/)**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,614 citations*
70. [**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/)
71. **[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*
72. **[UNITER: UNiversal Image-TExt Representation Learning](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,418 citations*
73. [**Recurrent Neural Network Regularization arXiv:1409.2329**Wojciech Zaremba, Ilya Sutskever, Oriol Vinyals*2014 · 2,412 citations*](https://md.research.elamur.ai/free/en/papers/zaremba-2014-recurrent-neural-network-regularization/)
74. **[TinyBERT: Distilling BERT for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoqi Jiao, Yichun Yin, Lifeng Shang, et al.*2020 · 2,378 citations*
75. **[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*
76. [**Deep Speech 2: End-to-End Speech Recognition in English and Mandarin arXiv:1512.02595**Dario Amodei, Rishita Anubhai, Eric Battenberg, et al.*2015 · 2,341 citations*](https://md.research.elamur.ai/free/en/papers/amodei-2015-deep-speech-2-end-end/)
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78. [**Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs) arXiv:1511.07289**Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter*2015 · 2,314 citations*](https://md.research.elamur.ai/free/en/papers/clevert-2015-fast-accurate-deep-network-learning/)
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92. [**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/)
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94. [**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/)
95. [**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/)
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97. [**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/)
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99. [**A Neural Conversational Model arXiv:1506.05869**Oriol Vinyals, Quoc Le*2015 · 1,791 citations*](https://md.research.elamur.ai/free/en/papers/vinyals-2015-neural-conversational-model/)
100. [**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/)

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