---
title: "Computer Vision and Pattern Recognition — papers, page 2"
description: "Computer Vision and Pattern Recognition: 1,029 papers of the Elamur Research library · page 2 of 12 · 26–125, the most cited first — our summary of each…"
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---
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# 👁️ Computer Vision and Pattern Recognition

1,029 papers · page 2 of 12 · 26–125 · the most cited first · [all shelves](https://md.research.elamur.ai/free/en/shelves/)

Computer Vision and Pattern Recognition is a subfield of Computer Science in the OpenAlex classification of science. The topics most of its papers here are on: «Multimodal Machine Learning Applications», «Advanced Neural Network Applications», «Handwritten Text Recognition Techniques».

## Papers

[← prev](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) [1](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) **2** [3](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/3/) … [12](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/12/) [next →](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/page/3/)

26. [**Dense Passage Retrieval for Open-Domain Question Answering**Vladimir Karpukhin, Barlas Oguz, Sewon Min, et al.*2020 · 5,370 citations*](https://md.research.elamur.ai/free/en/papers/karpukhin-2020-dense-passage-retrieval-open-domain/)
27. [**EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks**Mingxing Tan, Quoc V. Le*2019 · 5,308 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-efficientnet-rethinking-model-scaling-convolutional/)
28. [**Character-level Convolutional Networks for Text Classification**Xiang Zhang, Junbo Zhao, Yann LeCun*2015 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2015-character-level-convolutional-networks-text/)
29. [**Billion-scale similarity search with GPUs**Jeff Johnson, Matthijs Douze, Hervé Jégou*2017 · 4,687 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2017-billion-scale-similarity-search-gpus/)
30. [**Microsoft COCO: Common Objects in Context**Tsung-Yi Lin, Michael Maire, Serge Belongie, et al.*2014 · 4,529 citations*](https://md.research.elamur.ai/free/en/papers/lin-2014-microsoft-coco-common-objects-context/)
31. [**Neural Architecture Search with Reinforcement Learning**Barret Zoph, Quoc V. Le*2016 · 4,331 citations*](https://md.research.elamur.ai/free/en/papers/zoph-2016-neural-architecture-search-reinforcement-learning/)
32. [**Caffe: Convolutional Architecture for Fast Feature Embedding**Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.*2014 · 4,064 citations*](https://md.research.elamur.ai/free/en/papers/jia-2014-caffe-convolutional-architecture-fast-feature/)
33. [**Bidirectional LSTM-CRF Models for Sequence Tagging**Zhiheng Huang, Wei Xu, Kai Yu*2015 · 3,802 citations*](https://md.research.elamur.ai/free/en/papers/huang-2015-bidirectional-lstm-crf-models-sequence/)
34. [**Large language models are zero-shot reasoners**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,780 citations*](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)
35. [**XLNet: Generalized Autoregressive Pretraining for Language Understanding**Zhilin Yang, Zihang Dai, Yiming Yang, et al.*2019 · 3,740 citations*](https://md.research.elamur.ai/free/en/papers/yang-2019-xlnet-generalized-autoregressive-pretraining-language/)
36. [**A Survey on Evaluation of Large Language Models**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,640 citations*](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/)
37. [**SciBERT: A Pretrained Language Model for Scientific Text**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,548 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
38. [**Measuring Massive Multitask Language Understanding**Dan Hendrycks, Collin Burns, Steven Basart, et al.*2020 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-measuring-massive-multitask-language-understanding/)
39. [**HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering**Zhilin Yang, Peng Qi, Saizheng Zhang, et al.*2018 · 3,489 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-hotpotqa-dataset-diverse-explainable-multi/)
40. [**Know What You Don't Know: Unanswerable Questions for SQuAD**Pranav Rajpurkar, Robin Jia, Percy Liang*2018 · 3,360 citations*](https://md.research.elamur.ai/free/en/papers/rajpurkar-2018-know-what-you-don-t/)
41. [**Qwen3 Technical Report**An Yang, Anfeng Li, Baosong Yang, et al.*2025 · 3,276 citations*](https://md.research.elamur.ai/free/en/papers/yang-2025-qwen3-technical-report/)
42. [**Self-Consistency Improves Chain of Thought Reasoning in Language Models**Xuezhi Wang, Jason Wei, Dale Schuurmans, et al.*2022 · 3,035 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-self-consistency-improves-chain-thought/)
43. [**Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books**Yukun Zhu, Ryan Kiros, Richard Zemel, et al.*2015 · 3,030 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2015-aligning-books-movies-towards-story/)
44. [**Language Models as Knowledge Bases?**Fabio Petroni, Tim Rocktäschel, Sebastian Riedel, et al.*2019 · 2,977 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2019-language-models-as-knowledge-bases/)
45. [**Gemini: A Family of Highly Capable Multimodal Models**Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.*2023 · 2,951 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2023-gemini-family-highly-capable-multimodal/)
46. [**A Survey of Large Language Models**Wayne Xin Zhao, Kun Zhou, Junyi Li, et al.*2023 · 2,847 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-survey-large-language-models/)
47. [**What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?**Alex Kendall, Yarin Gal*2017 · 2,844 citations*](https://md.research.elamur.ai/free/en/papers/kendall-2017-uncertainties-need-bayesian-deep-learning/)
48. [**Self-Attention with Relative Position Representations**Peter Shaw, Jakob Uszkoreit, Ashish Vaswani*2018 · 2,811 citations*](https://md.research.elamur.ai/free/en/papers/shaw-2018-self-attention-relative-position-representations/)
49. [**LXMERT: Learning Cross-Modality Encoder Representations from Transformers**Hao Tan, Mohit Bansal*2019 · 2,714 citations*](https://md.research.elamur.ai/free/en/papers/tan-2019-lxmert-learning-cross-modality-encoder/)
50. [**Hierarchical Text-Conditional Image Generation with CLIP Latents**Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, et al.*2022 · 2,599 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2022-hierarchical-text-conditional-image-generation/)
51. [**Scalable Diffusion Models with Transformers**William Peebles, Saining Xie*2022 · 2,595 citations*](https://md.research.elamur.ai/free/en/papers/peebles-2022-scalable-diffusion-models-transformers/)
52. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
53. [**Improved Baselines with Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Yuheng Li, et al.*2023 · 2,557 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-improved-baselines-visual-instruction-tuning/)
54. [**Lost in the Middle: How Language Models Use Long Contexts**Nelson F. Liu, Kevin Lin, John Hewitt, et al.*2023 · 2,540 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-lost-middle-language-models-use/)
55. [**An Overview of Multi-Task Learning in Deep Neural Networks**Sebastian Ruder*2017 · 2,506 citations*](https://md.research.elamur.ai/free/en/papers/ruder-2017-overview-multi-task-learning-deep/)
56. [**Visual Instruction Tuning**Haotian Liu, Chunyuan Li, Qingyang Wu, et al.*2023 · 2,487 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-visual-instruction-tuning/)
57. [**Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding**Chitwan Saharia, William Chan, Saurabh Saxena, et al.*2022 · 2,416 citations*](https://md.research.elamur.ai/free/en/papers/saharia-2022-photorealistic-text-image-diffusion-models/)
58. [**Self-critical Sequence Training for Image Captioning**Steven J. Rennie, Etienne Marcheret, Youssef Mroueh, et al.*2016 · 2,367 citations*](https://md.research.elamur.ai/free/en/papers/rennie-2016-self-critical-sequence-training-image/)
59. [**UNITER: UNiversal Image-TExt Representation Learning**Yen-Chun Chen, Linjie Li, Licheng Yu, et al.*2019 · 2,344 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-uniter-universal-image-text-representation/)
60. [**Conceptual Captions: A Cleaned, Hypernymed, Image Alt-text Dataset For Automatic Image Captioning**Piyush Sharma, Nan Ding, Sebastian Goodman, et al.*2018 · 2,278 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2018-conceptual-captions-cleaned-hypernymed-image/)
61. [**Named Entity Recognition with Bidirectional LSTM-CNNs**Jason P. C. Chiu, Eric Nichols*2015 · 2,276 citations*](https://md.research.elamur.ai/free/en/papers/chiu-2015-named-entity-recognition-bidirectional-lstm/)
62. [**Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)**Djork-Arné Clevert, Thomas Unterthiner, Sepp Hochreiter*2015 · 2,274 citations*](https://md.research.elamur.ai/free/en/papers/clevert-2015-fast-accurate-deep-network-learning/)
63. [**GPT-4o System Card**OpenAI, Aaron Hurst, Adam Lerer, et al.*2024 · 2,221 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-gpt-4o-system-card/)
64. [**ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks**Jiasen Lu, Dhruv Batra, Devi Parikh, et al.*2019 · 2,217 citations*](https://md.research.elamur.ai/free/en/papers/lu-2019-vilbert-pretraining-task-agnostic-visiolinguistic/)
65. [**Emergent Abilities of Large Language Models**Jason Wei, Yi Tay, Rishi Bommasani, et al.*2022 · 2,215 citations*](https://md.research.elamur.ai/free/en/papers/wei-2022-emergent-abilities-large-language-models/)
66. [**Multimodal Transformer for Unaligned Multimodal Language Sequences**Yao-Hung Hubert Tsai, Shaojie Bai, Paul Pu Liang, et al.*2019 · 2,170 citations*](https://md.research.elamur.ai/free/en/papers/tsai-2019-multimodal-transformer-unaligned-multimodal-language/)
67. [**A Primer in BERTology: What We Know About How BERT Works**Anna Rogers, Olga Kovaleva, Anna Rumshisky*2020 · 2,127 citations*](https://md.research.elamur.ai/free/en/papers/rogers-2020-primer-bertology-what-we-know/)
68. [**BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models**Junnan Li, Dongxu Li, Silvio Savarese, et al.*2023 · 2,114 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-blip-2-bootstrapping-language-image/)
69. [**Flamingo: a Visual Language Model for Few-Shot Learning**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc, et al.*2022 · 2,108 citations*](https://md.research.elamur.ai/free/en/papers/alayrac-2022-flamingo-visual-language-model-few/)
70. [**Tensor Fusion Network for Multimodal Sentiment Analysis**Amir Zadeh, Minghai Chen, Soujanya Poria, et al.*2017 · 2,107 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2017-tensor-fusion-network-multimodal-sentiment/)
71. [**Reading Tea Leaves: How Humans Interpret Topic Models**Jonathan Chang, Jordan Boyd-Graber, Sean Gerrish, et al.*2009 · 2,037 citations*](https://md.research.elamur.ai/free/en/papers/chang-2009-reading-tea-leaves-humans-interpret-topic/)
72. [**Toolformer: Language Models Can Teach Themselves to Use Tools**Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, et al.*2023 · 2,020 citations*](https://md.research.elamur.ai/free/en/papers/schick-2023-toolformer-language-models-can-teach/)
73. [**Knowledge Graphs**Aidan Hogan, Eva Blomqvist, Michael Cochez, et al.*2020 · 2,011 citations*](https://md.research.elamur.ai/free/en/papers/hogan-2020-knowledge-graphs/)
74. [**GQA: A New Dataset for Real-World Visual Reasoning and Compositional Question Answering**Drew A. Hudson, Christopher D. Manning*2019 · 1,997 citations*](https://md.research.elamur.ai/free/en/papers/hudson-2019-gqa-new-dataset-real-world/)
75. [**MXNet: A Flexible and Efficient Machine Learning Library for Heterogeneous Distributed Systems**Tianqi Chen, Mu Li, Yutian Li, et al.*2015 · 1,981 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-mxnet-flexible-efficient-machine-learning/)
76. [**Bidirectional Attention Flow for Machine Comprehension**Minjoon Seo, Aniruddha Kembhavi, Ali Farhadi, et al.*2016 · 1,956 citations*](https://md.research.elamur.ai/free/en/papers/seo-2016-bidirectional-attention-flow-machine-comprehension/)
77. [**Focal Loss for Dense Object Detection**Tsung-Yi Lin, Priya Goyal, Ross Girshick, et al.*2017 · 1,867 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-focal-loss-dense-object-detection/)
78. [**Microsoft COCO Captions: Data Collection and Evaluation Server**Xinlei Chen, Hao Fang, Tsung-Yi Lin, et al.*2015 · 1,859 citations*](https://md.research.elamur.ai/free/en/papers/chen-2015-microsoft-coco-captions-data-collection/)
79. [**Design Challenges and Misconceptions in Named Entity Recognition**Lev Ratinov, Dan Roth*2009 · 1,836 citations*](https://md.research.elamur.ai/free/en/papers/ratinov-2009-design-challenges-misconceptions-named-entity/)
80. [**Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks**Xiujun Li, Xi Yin, Chunyuan Li, et al.*2020 · 1,827 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-oscar-object-semantics-aligned-pre/)
81. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,800 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
82. [**Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,797 citations*](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/)
83. [**ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERT**Omar Khattab, Matei Zaharia*2020 · 1,776 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2020-colbert-efficient-effective-passage/)
84. [**A Structured Self-attentive Sentence Embedding**Zhouhan Lin, Minwei Feng, Cicero Nogueira dos Santos, et al.*2017 · 1,773 citations*](https://md.research.elamur.ai/free/en/papers/lin-2017-structured-self-attentive-sentence-embedding/)
85. [**Leveraging Passage Retrieval with Generative Models for Open Domain Question Answering**Gautier Izacard, Edouard Grave*2021 · 1,674 citations*](https://md.research.elamur.ai/free/en/papers/izacard-2021-leveraging-passage-retrieval-generative-models/)
86. [**Retrieval-Augmented Generation for Large Language Models: A Survey**Yunfan Gao, Yun Xiong, Xinyu Gao, et al.*2023 · 1,647 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-retrieval-augmented-generation-large-language/)
87. [**Qwen Technical Report**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,643 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
88. [**Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding**Akira Fukui, Dong Huk Park, Daylen Yang, et al.*2016 · 1,642 citations*](https://md.research.elamur.ai/free/en/papers/fukui-2016-multimodal-compact-bilinear-pooling-visual/)
89. [**A Decomposable Attention Model for Natural Language Inference**Ankur Parikh, Oscar Täckström, Dipanjan Das, et al.*2016 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2016-decomposable-attention-model-natural-language/)
90. [**Deep Unsupervised Learning using Nonequilibrium Thermodynamics**Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, et al.*2015 · 1,588 citations*](https://md.research.elamur.ai/free/en/papers/sohl-dickstein-2015-deep-unsupervised-learning-using-nonequilibrium/)
91. [**Layer Normalization**Jimmy Lei Ba, Jamie Ryan Kiros, Geoffrey E. Hinton*2016 · 1,587 citations*](https://md.research.elamur.ai/free/en/papers/ba-2016-layer-normalization/)
92. [**Scaling Up Visual and Vision-Language Representation Learning With Noisy Text Supervision**Chao Jia, Yinfei Yang, Ye Xia, et al.*2021 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/jia-2021-scaling-up-visual-vision-language/)
93. [**Score-Based Generative Modeling through Stochastic Differential Equations**Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, et al.*2020 · 1,528 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-score-based-generative-modeling-through/)
94. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,522 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
95. [**Efficient Transformers: A Survey**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,513 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/)
96. [**Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned**Elena Voita, David Talbot, Fedor Moiseev, et al.*2019 · 1,511 citations*](https://md.research.elamur.ai/free/en/papers/voita-2019-analyzing-multi-head-self-attention/)
97. [**VisualBERT: A Simple and Performant Baseline for Vision and Language**Liunian Harold Li, Mark Yatskar, Da Yin, et al.*2019 · 1,510 citations*](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/)
98. [**Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph**AmirAli Bagher Zadeh, Paul Pu Liang, Soujanya Poria, et al.*2018 · 1,506 citations*](https://md.research.elamur.ai/free/en/papers/zadeh-2018-multimodal-language-analysis-wild-cmu/)
99. [**CLIPScore: A Reference-free Evaluation Metric for Image Captioning**Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.*2021 · 1,498 citations*](https://md.research.elamur.ai/free/en/papers/hessel-2021-clipscore-reference-free-evaluation-metric/)
100. [**How Can We Know What Language Models Know?**Zhengbao Jiang, Frank F. Xu, Jun Araki, et al.*2020 · 1,493 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2020-how-can-we-know-what/)
101. [**Pre-Training with Whole Word Masking for Chinese BERT**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,485 citations*](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/)
102. [**BLIP: Bootstrapping Language-Image Pre-training for Unified Vision-Language Understanding and Generation**Junnan Li, Dongxu Li, Caiming Xiong, et al.*2022 · 1,463 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-blip-bootstrapping-language-image-pre/)
103. [**GLM: General Language Model Pretraining with Autoregressive Blank Infilling**Zhengxiao Du, Yujie Qian, Xiao Liu, et al.*2022 · 1,449 citations*](https://md.research.elamur.ai/free/en/papers/du-2022-glm-general-language-model-pretraining/)
104. [**Sequence-Level Knowledge Distillation**Yoon Kim, Alexander M. Rush*2016 · 1,440 citations*](https://md.research.elamur.ai/free/en/papers/kim-2016-sequence-level-knowledge-distillation/)
105. [**Sigmoid Loss for Language Image Pre-Training**Xiaohua Zhai, Basil Mustafa, Alexander Kolesnikov, et al.*2023 · 1,427 citations*](https://md.research.elamur.ai/free/en/papers/zhai-2023-sigmoid-loss-language-image-pre/)
106. [**DINOv2: Learning Robust Visual Features without Supervision**Maxime Oquab, Timothee Darcet, Theo Moutakanni, et al.*2023 · 1,425 citations*](https://md.research.elamur.ai/free/en/papers/oquab-2023-dinov2-learning-robust-visual-features/)
107. [**Zero-Shot Text-to-Image Generation**Aditya Ramesh, Mikhail Pavlov, Gabriel Goh, et al.*2021 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/ramesh-2021-zero-shot-text-image-generation/)
108. [**Improved Techniques for Training GANs**Tim Salimans, Ian Goodfellow, Wojciech Zaremba, et al.*2016 · 1,420 citations*](https://md.research.elamur.ai/free/en/papers/salimans-2016-improved-techniques-training-gans/)
109. [**Fast Graph Representation Learning with PyTorch Geometric**Matthias Fey, Jan Eric Lenssen*2019 · 1,413 citations*](https://md.research.elamur.ai/free/en/papers/fey-2019-fast-graph-representation-learning-pytorch/)
110. [**LAION-5B: An open large-scale dataset for training next generation image-text models**Christoph Schuhmann, Romain Beaumont, Richard Vencu, et al.*2022 · 1,400 citations*](https://md.research.elamur.ai/free/en/papers/schuhmann-2022-laion-5b-open-large-scale/)
111. [**Unified Language Model Pre-training for Natural Language Understanding and Generation**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,389 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
112. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,371 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/)
113. [**Generation and Comprehension of Unambiguous Object Descriptions**Junhua Mao, Jonathan Huang, Alexander Toshev, et al.*2015 · 1,358 citations*](https://md.research.elamur.ai/free/en/papers/mao-2015-generation-comprehension-unambiguous-object-descriptions/)
114. [**Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments**Peter Anderson, Qi Wu, Damien Teney, et al.*2017 · 1,346 citations*](https://md.research.elamur.ai/free/en/papers/anderson-2017-vision-language-navigation-interpreting-visually/)
115. [**Unifying Visual-Semantic Embeddings with Multimodal Neural Language Models**Ryan Kiros, Ruslan Salakhutdinov, Richard S. Zemel*2014 · 1,315 citations*](https://md.research.elamur.ai/free/en/papers/kiros-2014-unifying-visual-semantic-embeddings-multimodal/)
116. [**VideoBERT: A Joint Model for Video and Language Representation Learning**Chen Sun, Austin Myers, Carl Vondrick, et al.*2019 · 1,305 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-videobert-joint-model-video-language/)
117. [**Long Short-Term Memory-Networks for Machine Reading**Jianpeng Cheng, Li Dong, Mirella Lapata*2016 · 1,300 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2016-long-short-term-memory-networks/)
118. [**A Comprehensive Overview of Large Language Models**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,286 citations*](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/)
119. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
120. [**ReferItGame: Referring to Objects in Photographs of Natural Scenes**Sahar Kazemzadeh, Vicente Ordonez, Mark Matten, et al.*2014 · 1,276 citations*](https://md.research.elamur.ai/free/en/papers/kazemzadeh-2014-referitgame-referring-objects-photographs-natural/)
121. [**CTRL: A Conditional Transformer Language Model for Controllable Generation**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,273 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
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124. [**M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation**Jianlv Chen, Shitao Xiao, Peitian Zhang, et al.*2024 · 1,196 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-m3-embedding-multi-linguality/)
125. [**Efficient Low-rank Multimodal Fusion With Modality-Specific Factors**Zhun Liu, Ying Shen, Varun Bharadhwaj Lakshminarasimhan, et al.*2018 · 1,174 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-efficient-low-rank-multimodal-fusion/)

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