---
title: "Who cites Alex Krizhevsky — 4,289 papers of our library"
description: "4,289 papers of the Elamur Research library cite the works of Alex Krizhevsky, found in their bibliographies · page 1 of 5 · 1–100; the most cited first."
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---
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# Who cites Alex Krizhevsky

4,603 citations of their works from 4,289 papers of our library (of the 149,439 whose bibliographies we read) · 472 with a page here, the most cited first · page 1 of 5 · 1–100 · [← Alex Krizhevsky](https://md.research.elamur.ai/free/en/authors/alex-krizhevsky/)

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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. [**Dropout: A Simple Way to Prevent Neural Networks from Overfitting JMLR 15(56):1929–1958**Nitish Srivastava, Geoffrey Hinton, Alex Krizhevsky, et al.*2014 · 34,422 citations*](https://md.research.elamur.ai/free/en/papers/srivastava-2014-dropout-simple-way-prevent/)
4. **[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*
5. **[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*
6. **[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*
7. [**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/)
8. [**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/)
9. [**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/)
10. **[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*
11. [**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/)
12. **[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*
13. [**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/)
14. [**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/)
15. [**YOLOv4: Optimal Speed and Accuracy of Object Detection arXiv:2004.10934**Alexey Bochkovskiy, Chien-Yao Wang, Hong-Yuan Mark Liao*2020 · 10,403 citations*](https://md.research.elamur.ai/free/en/papers/bochkovskiy-2020-yolov4-optimal-speed-accuracy-object/)
16. [**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/)
17. [**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/)
18. [**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/)
19. [**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/)
20. [**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/)
21. [**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/)
22. [**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/)
23. [**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/)
24. [**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/)
25. [**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/)
26. [**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/)
27. **[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*
28. **[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*
29. [**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/)
30. **[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*
31. [**Generative Adversarial Nets**Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, et al.*2014 · 5,790 citations*](https://md.research.elamur.ai/free/en/papers/goodfellow-2014-generative-adversarial-nets/)
32. **[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*
33. **[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*
34. **[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*
35. **[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*
36. [**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/)
37. [**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/)
38. [**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/)
39. **[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*
40. [**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/)
41. **[XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks](https://md.research.elamur.ai/free/en/papers/rastegari-2016-xnor-net-imagenet-classification-using/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Mohammad Rastegari, Vicente Ordonez, Joseph Redmon, et al.*2016 · 4,402 citations*
42. [**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/)
43. **[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*
44. [**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/)
45. **[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*
46. [**DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition arXiv:1310.1531**Jeff Donahue, Yangqing Jia, Oriol Vinyals, et al.*2013 · 3,463 citations*](https://md.research.elamur.ai/free/en/papers/donahue-2013-decaf-deep-convolutional-activation-feature/)
47. [**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/)
48. **[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*
49. [**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/)
50. [**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/)
51. [**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/)
52. **[Revisiting Unreasonable Effectiveness of Data in Deep Learning Era](https://md.research.elamur.ai/free/en/papers/sun-2017-revisiting-unreasonable-effectiveness-data-deep/) [IEEE International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-international-conference-on-computer-vision-iccv/)**Chen Sun, Abhinav Shrivastava, Saurabh Singh, et al.*2017 · 2,512 citations*
53. **[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*
54. **[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*
55. [**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/)
56. [**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/)
57. [**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/)
58. [**A Fast and Accurate Dependency Parser using Neural Networks**Danqi Chen, Christopher Manning*2014 · 2,314 citations*](https://md.research.elamur.ai/free/en/papers/chen-2014-fast-accurate-dependency-parser-neural/)
59. **[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*
60. **[Deep Networks with Stochastic Depth](https://md.research.elamur.ai/free/en/papers/huang-2016-deep-networks-stochastic-depth/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Gao Huang, Yu Sun, Zhuang Liu, et al.*2016 · 2,160 citations*
61. **[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*
62. [**Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1 arXiv:1602.02830**Matthieu Courbariaux, Itay Hubara, Daniel Soudry, et al.*2016 · 2,143 citations*](https://md.research.elamur.ai/free/en/papers/courbariaux-2016-binarized-neural-networks-training-deep/)
63. **[Designing Network Design Spaces](https://md.research.elamur.ai/free/en/papers/radosavovic-2020-designing-network-design-spaces/) [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/)**Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, et al.*2020 · 2,040 citations*
64. [**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/)
65. [**Bidirectional Attention Flow for Machine Comprehension arXiv:1611.01603**Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al.*2016 · 1,988 citations*](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
66. [**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/)
67. [**BinaryConnect: Training Deep Neural Networks with binary weights during propagations arXiv:1511.00363**Matthieu Courbariaux, Yoshua Bengio, Jean-Pierre David*2015 · 1,885 citations*](https://md.research.elamur.ai/free/en/papers/courbariaux-2015-binaryconnect-training-deep-neural-networks/)
68. [**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/)
69. **[Character-Aware Neural Language Models](https://md.research.elamur.ai/free/en/papers/kim-2015-character-aware-neural-language-models/) [Proceedings of the AAAI Conference on Artificial Intelligence](https://md.research.elamur.ai/free/en/venues/proceedings-of-the-aaai-conference-on-artificial-intelligence/)**Yoon Kim, Yacine Jernite, David Sontag, et al.*2015 · 1,874 citations*
70. [**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/)
71. [**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/)
72. [**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/)
73. [**DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients arXiv:1606.06160**Shuchang Zhou, Yuxin Wu, Zekun Ni, et al.*2016 · 1,762 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2016-dorefa-net-training-low-bitwidth/)
74. **[YFCC100M: The New Data in Multimedia Research](https://md.research.elamur.ai/free/en/papers/thomee-2015-yfcc100m-new-data-multimedia-research/) [Communications of the ACM](https://md.research.elamur.ai/free/en/venues/communications-of-the-acm/)**Bart Thomee, David A. Shamma, Gerald Friedland, et al.*2015 · 1,671 citations*
75. [**Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations arXiv:1609.07061**Itay Hubara, Matthieu Courbariaux, Daniel Soudry, et al.*2016 · 1,610 citations*](https://md.research.elamur.ai/free/en/papers/hubara-2016-quantized-neural-networks-training-neural/)
76. **[Memory Aware Synapses: Learning what (not) to forget](https://md.research.elamur.ai/free/en/papers/aljundi-2017-memory-aware-synapses-learning-forget/) [Lecture notes in computer science](https://md.research.elamur.ai/free/en/venues/lecture-notes-in-computer-science/)**Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, et al.*2017 · 1,608 citations*
77. **[End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures](https://md.research.elamur.ai/free/en/papers/miwa-2016-end-end-relation-extraction-lstms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Makoto Miwa, Mohit Bansal*2016 · 1,606 citations*
78. [**Deep Speech: Scaling up end-to-end speech recognition arXiv:1412.5567**Awni Hannun, Carl Case, Jared Casper, et al.*2014 · 1,600 citations*](https://md.research.elamur.ai/free/en/papers/hannun-2014-deep-speech-scaling-up-end/)
79. [**On the Convergence of Adam and Beyond arXiv:1904.09237**Sashank J. Reddi, Satyen Kale, Sanjiv Kumar*2019 · 1,598 citations*](https://md.research.elamur.ai/free/en/papers/reddi-2019-convergence-adam/)
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83. [**OverFeat: Integrated Recognition, Localization and Detection using Convolutional Networks arXiv:1312.6229**Pierre Sermanet, David Eigen, Xiang Zhang, et al.*2013 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/sermanet-2013-overfeat-integrated-recognition-localization-detection/)
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85. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models arXiv:1411.2539**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
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95. **[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*
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97. [**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/)
98. [**A Theoretically Grounded Application of Dropout in Recurrent Neural Networks arXiv:1512.05287**Yarin Gal, Zoubin Ghahramani*2015 · 1,032 citations*](https://md.research.elamur.ai/free/en/papers/gal-2015-theoretically-grounded-application-dropout-recurrent/)
99. [**Exact solutions to the nonlinear dynamics of learning in deep linear neural networks arXiv:1312.6120**Andrew M. Saxe, James L. McClelland, Surya Ganguli*2013 · 1,027 citations*](https://md.research.elamur.ai/free/en/papers/saxe-2013-exact-solutions-nonlinear-dynamics-learning/)
100. [**BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain arXiv:1708.06733**Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg*2017 · 1,024 citations*](https://md.research.elamur.ai/free/en/papers/gu-2017-badnets-identifying-vulnerabilities-machine-learning/)

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