---
title: "Who cites Andy Davis — 2,080 papers of our library"
description: "2,080 papers of the Elamur Research library cite the works of Andy Davis, found in their bibliographies · page 1 of 3 · 1–100; the most cited first."
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---
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# Who cites Andy Davis

2,112 citations of their works from 2,080 papers of our library (of the 149,439 whose bibliographies we read) · 215 with a page here, the most cited first · page 1 of 3 · 1–100 · [← Andy Davis](https://md.research.elamur.ai/free/en/authors/andy-davis/)

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

1. **[Array Programming with NumPy](https://md.research.elamur.ai/free/en/papers/harris-2020-array-programming-numpy/) [Nature](https://md.research.elamur.ai/free/en/venues/nature/)**Charles R. Harris, K. Jarrod Millman, Stéfan J. van der Walt, et al.*2020 · 23,607 citations*
2. [**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/)
3. [**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/)
4. [**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/)
5. [**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/)
6. [**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/)
7. [**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/)
8. [**Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks arXiv:1703.03400**Chelsea Finn, Pieter Abbeel, Sergey Levine*2017 · 6,549 citations*](https://md.research.elamur.ai/free/en/papers/finn-2017-model-agnostic-meta-learning-fast/)
9. [**Inductive Representation Learning on Large Graphs arXiv:1706.02216**William L. Hamilton, Rex Ying, Jure Leskovec*2017 · 4,795 citations*](https://md.research.elamur.ai/free/en/papers/hamilton-2017-inductive-representation-learning-large-graphs/)
10. [**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/)
11. **[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*
12. [**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/)
13. [**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/)
14. **[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*
15. [**What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision? arXiv:1703.04977**Alex Kendall, Yarin Gal*2017 · 2,870 citations*](https://md.research.elamur.ai/free/en/papers/kendall-2017-uncertainties-need-bayesian-deep-learning/)
16. **[Convolutional Neural Networks on Graphs with Fast Localized Spectral Filtering](https://md.research.elamur.ai/free/en/papers/defferrard-2016-convolutional-neural-networks-graphs-fast/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Michael Defferrard, Xavier Bresson, Pierre Vandergheynst*2016 · 1,861 citations*
17. [**AllenNLP: A Deep Semantic Natural Language Processing Platform arXiv:1803.07640**Matt Gardner, Joel Grus, Mark Neumann, et al.*2018 · 1,819 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2018-allennlp-deep-semantic-natural-language/)
18. [**Universal Sentence Encoder arXiv:1803.11175**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,629 citations*](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder/)
19. [**Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context arXiv:2403.05530**Gemini Team, Petko Georgiev, Ving Ian Lei, et al.*2024 · 1,589 citations*](https://md.research.elamur.ai/free/en/papers/gemini-team-2024-gemini-1-5-unlocking-multimodal/)
20. **[Universal Sentence Encoder for English](https://md.research.elamur.ai/free/en/papers/cer-2018-universal-sentence-encoder-english/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Daniel Cer, Yinfei Yang, Sheng-yi Kong, et al.*2018 · 1,546 citations*
21. [**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/)
22. [**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/)
23. **[Pre-Training with Whole Word Masking for Chinese BERT](https://md.research.elamur.ai/free/en/papers/cui-2019-pre-training-whole-word-masking/) [IEEE/ACM Transactions on Audio Speech and Language Processing](https://md.research.elamur.ai/free/en/venues/ieee-acm-transactions-on-audio-speech-and-language-processing/)**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2019 · 1,490 citations*
24. **[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*
25. [**TVM: An Automated End-to-End Optimizing Compiler for Deep Learning arXiv:1802.04799**Tianqi Chen, Thierry Moreau, Ziheng Jiang, et al.*2018 · 1,384 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-tvm-automated-end-end-optimizing/)
26. [**Language Modeling with Gated Convolutional Networks arXiv:1612.08083**Yann N. Dauphin, Angela Fan, Michael Auli, et al.*2016 · 1,346 citations*](https://md.research.elamur.ai/free/en/papers/dauphin-2016-language-modeling-gated-convolutional-networks/)
27. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
28. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-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/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
29. [**DailyDialog: A Manually Labelled Multi-turn Dialogue Dataset arXiv:1710.03957**Yanran Li, Hui Su, Xiaoyu Shen, et al.*2017 · 1,206 citations*](https://md.research.elamur.ai/free/en/papers/li-2017-dailydialog-manually-labelled-multi-turn/)
30. [**Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities arXiv:2507.06261**Gheorghe Comanici, Eric Bieber, Mike Schaekermann, et al.*2025 · 974 citations*](https://md.research.elamur.ai/free/en/papers/comanici-2025-gemini-2-5-pushing-frontier/)
31. [**QSGD: Communication-Efficient SGD via Gradient Quantization and Encoding arXiv:1610.02132**Dan Alistarh, Demjan Grubic, Jerry Li, et al.*2016 · 962 citations*](https://md.research.elamur.ai/free/en/papers/alistarh-2016-qsgd-communication-efficient-sgd-gradient/)
32. **[PyTorch 2: Faster Machine Learning Through Dynamic Python Bytecode Transformation and Graph Compilation](https://md.research.elamur.ai/free/en/papers/ansel-2024-pytorch-2-faster-machine-learning/) [ACM International Conference on Architectural Support for Programming Languages and Operating Systems (ASPLOS)](https://md.research.elamur.ai/free/en/venues/acm-international-conference-on-architectural-support-for-programming-languages/)**Jason Ansel, Edward Yang, Horace He, et al.*2024 · 946 citations*
33. [**Revisiting Pre-Trained Models for Chinese Natural Language Processing arXiv:2004.13922**Yiming Cui, Wanxiang Che, Ting Liu, et al.*2020 · 790 citations*](https://md.research.elamur.ai/free/en/papers/cui-2020-revisiting-pre-trained-models-chinese/)
34. **[AdapterHub: A Framework for Adapting Transformers](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 754 citations*
35. [**Triton: An Intermediate Language and Compiler for Tiled Neural Network Computations MAPL 2019**Philippe Tillet, H. T. Kung, David Cox*2019 · 729 citations*](https://md.research.elamur.ai/free/en/papers/tillet-2019-triton-intermediate-language-compiler-tiled/)
36. [**Horovod: fast and easy distributed deep learning in TensorFlow arXiv:1802.05799**Alexander Sergeev, Mike Del Balso*2018 · 695 citations*](https://md.research.elamur.ai/free/en/papers/sergeev-2018-horovod-fast-easy-distributed-deep/)
37. **[Efficient Content-Based Sparse Attention with Routing Transformers](https://md.research.elamur.ai/free/en/papers/roy-2020-efficient-content-based-sparse-attention/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Aurko Roy, Mohammad Saffar, Ashish Vaswani, et al.*2020 · 677 citations*
38. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model arXiv:2201.11990**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 615 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/)
39. [**QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension arXiv:1804.09541**Adams Wei Yu, David Dohan, Minh-Thang Luong, et al.*2018 · 597 citations*](https://md.research.elamur.ai/free/en/papers/yu-2018-qanet-combining-local-convolution-global/)
40. [**gpt-oss-120b & gpt-oss-20b Model Card arXiv:2508.10925**OpenAI: Sandhini Agarwal, Lama Ahmad, Jason Ai, et al.*2025 · 576 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2025-gpt-oss-120b-gpt-oss/)
41. **[Massive Exploration of Neural Machine Translation Architectures](https://md.research.elamur.ai/free/en/papers/britz-2017-massive-exploration-neural-machine-translation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Denny Britz, Anna Goldie, Minh-Thang Luong, et al.*2017 · 574 citations*
42. **[Multilingual Universal Sentence Encoder for Semantic Retrieval](https://md.research.elamur.ai/free/en/papers/yang-2020-multilingual-universal-sentence-encoder-semantic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yinfei Yang, Daniel Cer, Amin Ahmad, et al.*2020 · 570 citations*
43. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges arXiv:1907.05019**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 564 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
44. [**HDLTex: Hierarchical Deep Learning for Text Classification ICMLA 2017**Kamran Kowsari, Donald E. Brown, Mojtaba Heidarysafa, et al.*2017 · 532 citations*](https://md.research.elamur.ai/free/en/papers/kowsari-2017-hdltex-hierarchical-deep-learning-text/)
45. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
46. [**Regularizing Neural Networks by Penalizing Confident Output Distributions arXiv:1701.06548**Gabriel Pereyra, George Tucker, Jan Chorowski, et al.*2017 · 496 citations*](https://md.research.elamur.ai/free/en/papers/pereyra-2017-regularizing-neural-networks-penalizing-confident/)
47. [**Texygen: A Benchmarking Platform for Text Generation Models arXiv:1802.01886**Yaoming Zhu, Sidi Lu, Lei Zheng, et al.*2018 · 495 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2018-texygen-benchmarking-platform-text-generation/)
48. [**DyNet: The Dynamic Neural Network Toolkit arXiv:1701.03980**Graham Neubig, Chris Dyer, Yoav Goldberg, et al.*2017 · 489 citations*](https://md.research.elamur.ai/free/en/papers/neubig-2017-dynet-dynamic-neural-network-toolkit/)
49. [**Orca: A Distributed Serving System for Transformer-Based Generative Models OSDI 2022**Gyeong-In Yu, Joo Seong Jeong, Geon-Woo Kim, et al.*2022 · 478 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-orca-distributed-serving-system-transformer/)
50. [**In-Datacenter Performance Analysis of a Tensor Processing Unit arXiv:1704.04760**Norman P. Jouppi; Cliff Young; Nishant Patil; David Patterson; Gaurav Agrawal; Raminder Bajwa; Sarah Bates; Suresh Bhatia; Nan Boden; Al Borchers; Rick Boyle; Pierre-luc Cantin; Clifford Chao; Chris Clark; Jeremy Coriell; Mike Daley; Matt Dau; Jeffrey Dean; Ben Gelb; Tara Vazir Ghaemmaghami; Rajendra Gottipati; William Gulland; Robert Hagmann; C. Richard Ho; Doug Hogberg; John Hu; Robert Hundt; Dan Hurt; Julian Ibarz; Aaron Jaffey; Alek Jaworski; Alexander Kaplan; Harshit Khaitan; Andy Koch; Naveen Kumar; Steve Lacy; James Laudon; James Law; Diemthu Le; Chris Leary; Zhuyuan Liu; Kyle Lucke; Alan Lundin; Gordon MacKean; Adriana Maggiore; Maire Mahony; Kieran Miller; Rahul Nagarajan; Ravi Narayanaswami; Ray Ni; Kathy Nix; Thomas Norrie; Mark Omernick; Narayana Penukonda; Andy Phelps; Jonathan Ross; Matt Ross; Amir Salek; Emad Samadiani; Chris Severn; Gregory Sizikov; Matthew Snelham; Jed Souter; Dan Steinberg; Andy Swing; Mercedes Tan; Gregory Thorson; Bo Tian; Horia Toma; Erick Tuttle; Vijay Vasudevan; Richard Walter; Walter Wang; Eric Wilcox;, Doe Hyun Yoon*2017 · 449 citations*](https://md.research.elamur.ai/free/en/papers/jouppi-2017-datacenter-performance-analysis-tensor-processing/)
51. [**Pay Less Attention with Lightweight and Dynamic Convolutions arXiv:1901.10430**Felix Wu, Angela Fan, Alexei Baevski, et al.*2019 · 437 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
52. [**Adversarial Training Methods for Semi-Supervised Text Classification arXiv:1605.07725**Takeru Miyato, Andrew M. Dai, Ian Goodfellow*2016 · 433 citations*](https://md.research.elamur.ai/free/en/papers/miyato-2016-adversarial-training-methods-semi-supervised/)
53. [**Hindsight Experience Replay arXiv:1707.01495**Marcin Andrychowicz, Filip Wolski, Alex Ray, et al.*2017 · 421 citations*](https://md.research.elamur.ai/free/en/papers/andrychowicz-2017-hindsight-experience-replay/)
54. **[Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning](https://md.research.elamur.ai/free/en/papers/gordon-2020-compressing-bert-studying-effects-weight/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Mitchell Gordon, Kevin Duh, Nicholas Andrews*2020 · 407 citations*
55. **[A Survey on Large Language Models for Code Generation](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/) [ACM Transactions on Software Engineering and Methodology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-software-engineering-and-methodology/)**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 367 citations*
56. **[Universal Neural Machine Translation for Extremely Low Resource Languages](https://md.research.elamur.ai/free/en/papers/gu-2018-universal-neural-machine-translation-extremely/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jiatao Gu, Hany Hassan, Jacob Devlin, et al.*2018 · 361 citations*
57. **[emoji2vec: Learning Emoji Representations from their Description](https://md.research.elamur.ai/free/en/papers/eisner-2016-emoji2vec-learning-emoji-representations-their/) [International Workshop on Natural Language Processing for Social Media](https://md.research.elamur.ai/free/en/venues/socialnlp/)**Ben Eisner, Tim Rocktäschel, Isabelle Augenstein, et al.*2016 · 360 citations*
58. [**Highly Scalable Deep Learning Training System with Mixed-Precision: Training ImageNet in Four Minutes arXiv:1807.11205**Xianyan Jia, Shutao Song, Wei He, et al.*2018 · 348 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-highly-scalable-deep-learning-training/)
59. [**Generating Wikipedia by Summarizing Long Sequences arXiv:1801.10198**Peter J. Liu, Mohammad Saleh, Etienne Pot, et al.*2018 · 334 citations*](https://md.research.elamur.ai/free/en/papers/liu-2018-generating-wikipedia-summarizing-long-sequences/)
60. [**Carbon Emissions and Large Neural Network Training arXiv:2104.10350**David Patterson, Joseph Gonzalez, Quoc Le, et al.*2021 · 323 citations*](https://md.research.elamur.ai/free/en/papers/patterson-2021-carbon-emissions-large-neural-network/)
61. [**Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions arXiv:1802.04730**Nicolas Vasilache, Oleksandr Zinenko, Theodoros Theodoridis, et al.*2018 · 323 citations*](https://md.research.elamur.ai/free/en/papers/vasilache-2018-tensor-comprehensions-framework-agnostic-high/)
62. **[How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al.*2023 · 317 citations*
63. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
64. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
65. **[MoEL: Mixture of Empathetic Listeners](https://md.research.elamur.ai/free/en/papers/lin-2019-moel-mixture-empathetic-listeners/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Zhaojiang Lin, Andrea Madotto, Jamin Shin, et al.*2019 · 301 citations*
66. [**Beyond Data and Model Parallelism for Deep Neural Networks SysML 2019**Zhihao Jia, Matei Zaharia, Alex Aiken*2018 · 280 citations*](https://md.research.elamur.ai/free/en/papers/jia-2018-beyond-data-model-parallelism-deep/)
67. [**Alpa: Automating Inter- and Intra-Operator Parallelism for Distributed Deep Learning OSDI 2022**Lianmin Zheng, Zhuohan Li, Hao Zhang, et al.*2022 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2022-alpa-automating-inter-intra-operator/)
68. [**Learning to Optimize Tensor Programs arXiv:1805.08166**Tianqi Chen, Lianmin Zheng, Eddie Yan, et al.*2019 · 269 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-learning-optimize-tensor-programs/)
69. [**DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale arXiv:2201.05596**Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.*2022 · 261 citations*](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/)
70. [**Chatlaw: A Multi-Agent Legal Assistant based on a Role-Aligned Mixture-of-Experts Architecture arXiv:2306.16092**Jiaxi Cui; Munan Ning; Zongjian Li; Bohua Chen; Yang Yan; Hao Li; Bin Ling; Yonghong Tian;, Li Yuan*2023 · 248 citations*](https://md.research.elamur.ai/free/en/papers/cui-2023-chatlaw-multi-agent-legal-assistant/)
71. [**CLUE: A Chinese Language Understanding Evaluation Benchmark arXiv:2004.05986**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
72. [**FigureQA: An Annotated Figure Dataset for Visual Reasoning arXiv:1710.07300**Samira Ebrahimi Kahou, Vincent Michalski, Adam Atkinson, et al.*2017 · 240 citations*](https://md.research.elamur.ai/free/en/papers/kahou-2017-figureqa-annotated-figure-dataset-visual/)
73. **[From Language to Programs: Bridging Reinforcement Learning and Maximum Marginal Likelihood](https://md.research.elamur.ai/free/en/papers/guu-2017-language-programs-bridging-reinforcement-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Kelvin Guu, Panupong Pasupat, Evan Liu, et al.*2017 · 236 citations*
74. **[Hurdles to Progress in Long-form Question Answering](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Kalpesh Krishna, Aurko Roy, Mohit Iyyer*2021 · 233 citations*
75. **[Entities as Experts: Sparse Memory Access with Entity Supervision](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 208 citations*
76. [**Mesh-TensorFlow: Deep Learning for Supercomputers arXiv:1811.02084**Noam Shazeer, Youlong Cheng, Niki Parmar, et al.*2018 · 203 citations*](https://md.research.elamur.ai/free/en/papers/shazeer-2018-mesh-tensorflow-deep-learning-supercomputers/)
77. [**Model Tells You What to Discard: Adaptive KV Cache Compression for LLMs arXiv:2310.01801**Suyu Ge, Yunan Zhang, Liyuan Liu, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/ge-2023-model-tells-discard-adaptive-kv/)
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79. [**End-to-End Differentiable Proving arXiv:1705.11040**Tim Rocktäschel, Sebastian Riedel*2017 · 188 citations*](https://md.research.elamur.ai/free/en/papers/rocktaschel-2017-end-end-differentiable-proving/)
80. [**Open Question Answering over Tables and Text arXiv:2010.10439**Wenhu Chen, Ming-Wei Chang, Eva Schlinger, et al.*2020 · 182 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-open-question-answering-over-tables/)
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82. [**Analysis of Large-Scale Multi-Tenant GPU Clusters for DNN Training Workloads arXiv:1901.05758**Myeongjae Jeon, Shivaram Venkataraman, Amar Phanishayee, et al.*2019 · 179 citations*](https://md.research.elamur.ai/free/en/papers/jeon-2019-analysis-large-scale-multi-tenant/)
83. [**BASE Layers: Simplifying Training of Large, Sparse Models arXiv:2103.16716**Mike Lewis, Shruti Bhosale, Tim Dettmers, et al.*2021 · 177 citations*](https://md.research.elamur.ai/free/en/papers/lewis-2021-base-layers-simplifying-training-large/)
84. [**Adaptive Input Representations for Neural Language Modeling arXiv:1809.10853**Alexei Baevski, Michael Auli*2018 · 177 citations*](https://md.research.elamur.ai/free/en/papers/baevski-2018-adaptive-input-representations-neural-language/)
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86. [**Capabilities of Gemini Models in Medicine arXiv:2404.18416**Khaled Saab, Tao Tu, Wei-Hung Weng, et al.*2024 · 170 citations*](https://md.research.elamur.ai/free/en/papers/saab-2024-capabilities-gemini-models-medicine/)
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90. [**PipeDream: Fast and Efficient Pipeline Parallel DNN Training arXiv:1806.03377**Aaron Harlap, Deepak Narayanan, Amar Phanishayee, et al.*2018 · 162 citations*](https://md.research.elamur.ai/free/en/papers/harlap-2018-pipedream-fast-efficient-pipeline-parallel/)
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94. [**MegaScale: Scaling Large Language Model Training to More Than 10,000 GPUs arXiv:2402.15627**Ziheng Jiang, Haibin Lin, Yinmin Zhong, et al.*2024 · 157 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-megascale-scaling-large-language-model/)
95. **[ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 156 citations*
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99. [**Diversity is All You Need: Learning Skills without a Reward Function arXiv:1802.06070**Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, et al.*2018 · 145 citations*](https://md.research.elamur.ai/free/en/papers/eysenbach-2018-diversity-all-need-learning-skills/)
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