---
title: "Who cites Martín Abadi — 1,293 papers of our library"
description: "1,293 papers of the Elamur Research library cite the works of Martín Abadi, found in their bibliographies · page 1 of 2 · 1–100; the most cited first."
canonical: "https://research.elamur.ai/free/en/authors/martin-abadi/cited-by/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/authors/martin-abadi/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [authors](https://md.research.elamur.ai/free/en/authors/) / [Martín Abadi](https://md.research.elamur.ai/free/en/authors/martin-abadi/) / cited by

# Who cites Martín Abadi

1,301 citations of their works from 1,293 papers of our library (of the 149,439 whose bibliographies we read) · 103 with a page here, the most cited first · page 1 of 2 · 1–100 · [← Martín Abadi](https://md.research.elamur.ai/free/en/authors/martin-abadi/)

**1** [2](https://md.research.elamur.ai/free/en/authors/martin-abadi/cited-by/page/2/) [next →](https://md.research.elamur.ai/free/en/authors/martin-abadi/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. [**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/)
3. [**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/)
4. [**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/)
5. [**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/)
6. [**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/)
7. **[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*
8. [**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/)
9. **[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*
10. [**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/)
11. [**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/)
12. **[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*
13. [**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/)
14. [**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/)
15. **[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*
16. **[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*
17. [**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/)
18. [**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/)
19. **[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*
20. [**Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning arXiv:1709.00103**Victor Zhong, Caiming Xiong, Richard Socher*2017 · 1,226 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2017-seq2sql-generating-structured-queries-natural/)
21. [**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/)
22. [**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/)
23. **[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*
24. **[TaPas: Weakly Supervised Table Parsing via Pre-training](https://md.research.elamur.ai/free/en/papers/herzig-2020-tapas-weakly-supervised-table-parsing/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jonathan Herzig, Pawel Krzysztof Nowak, Thomas Müller, et al.*2020 · 847 citations*
25. [**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/)
26. [**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/)
27. [**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/)
28. [**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/)
29. **[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*
30. **[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*
31. [**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/)
32. [**TabFact: A Large-scale Dataset for Table-based Fact Verification arXiv:1909.02164**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 515 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
33. [**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/)
34. [**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/)
35. [**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/)
36. [**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/)
37. [**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/)
38. [**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/)
39. [**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/)
40. [**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/)
41. [**Deep Learning: A Critical Appraisal arXiv:1801.00631**Gary Marcus*2018 · 417 citations*](https://md.research.elamur.ai/free/en/papers/marcus-2018-deep-learning-critical-appraisal/)
42. **[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*
43. **[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*
44. [**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/)
45. **[Search-based Neural Structured Learning for Sequential Question Answering](https://md.research.elamur.ai/free/en/papers/iyyer-2017-search-based-neural-structured-learning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mohit Iyyer, Wen-tau Yih, Ming-Wei Chang*2017 · 330 citations*
46. [**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/)
47. [**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/)
48. [**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/)
49. [**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/)
50. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor arXiv:2107.07653**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 255 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
51. [**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/)
52. [**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/)
53. **[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*
54. **[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*
55. **[Did the Model Understand the Question?](https://md.research.elamur.ai/free/en/papers/mudrakarta-2018-did-model-understand-question/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, et al.*2018 · 214 citations*
56. **[Tensor2Tensor for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/vaswani-2018-tensor2tensor-neural-machine-translation/) [Conference of the Association for Machine Translation in the Americas (AMTA)](https://md.research.elamur.ai/free/en/venues/amta/)**Ashish Vaswani, Samy Bengio, Eugene Brevdo, et al.*2018 · 191 citations*
57. [**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/)
58. [**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/)
59. [**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/)
60. [**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/)
61. [**Unsupervised Text Style Transfer using Language Models as Discriminators arXiv:1805.11749**Zichao Yang, Zhiting Hu, Chris Dyer, et al.*2018 · 161 citations*](https://md.research.elamur.ai/free/en/papers/yang-2018-unsupervised-text-style-transfer-using/)
62. [**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/)
63. **[Effective Domain Mixing for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/britz-2017-effective-domain-mixing-neural-machine/) [Conference on Machine Translation (WMT)](https://md.research.elamur.ai/free/en/venues/wmt/)**Denny Britz, Quoc Le, Reid Pryzant*2017 · 156 citations*
64. **[SpotServe: Serving Generative Large Language Models on Preemptible Instances](https://md.research.elamur.ai/free/en/papers/miao-2023-spotserve-serving-generative-large-language/) [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/)**Xupeng Miao, Chunan Shi, Jiangfei Duan, et al.*2023 · 145 citations*
65. [**Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads arXiv:2008.09213**Deepak Narayanan, Keshav Santhanam, Fiodar Kazhamiaka, et al.*2020 · 141 citations*](https://md.research.elamur.ai/free/en/papers/narayanan-2020-heterogeneity-aware-cluster-scheduling-policies/)
66. [**Learning to Represent Programs with Graphs arXiv:1711.00740**Miltiadis Allamanis, Marc Brockschmidt, Mahmoud Khademi*2017 · 127 citations*](https://md.research.elamur.ai/free/en/papers/allamanis-2017-learning-represent-programs-graphs/)
67. [**Making Neural QA as Simple as Possible but not Simpler arXiv:1703.04816**Dirk Weissenborn, Georg Wiese, Laura Seiffe*2017 · 122 citations*](https://md.research.elamur.ai/free/en/papers/weissenborn-2017-making-neural-qa-simple-possible/)
68. **[ChID: A Large-scale Chinese IDiom Dataset for Cloze Test](https://md.research.elamur.ai/free/en/papers/zheng-2019-chid-large-scale-chinese/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chujie Zheng, Minlie Huang, Aixin Sun*2019 · 119 citations*
69. **[Entity Linking in 100 Languages](https://md.research.elamur.ai/free/en/papers/botha-2020-entity-linking-100-languages/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jan A. Botha, Zifei Shan, Daniel Gillick*2020 · 114 citations*
70. [**A Pretrainer's Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity arXiv:2305.13169**Shayne Longpre, Gregory Yauney, Emily Reif, et al.*2023 · 104 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-pretrainer-s-guide-training-data/)
71. [**Simulating Action Dynamics with Neural Process Networks arXiv:1711.05313**Antoine Bosselut, Omer Levy, Ari Holtzman, et al.*2017 · 102 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2017-simulating-action-dynamics-neural-process/)
72. [**TextGrad: Automatic "Differentiation" via Text arXiv:2406.07496**Mert Yuksekgonul, Federico Bianchi, Joseph Boen, et al.*2024 · 96 citations*](https://md.research.elamur.ai/free/en/papers/yuksekgonul-2024-textgrad-automatic-differentiation-text/)
73. **[Text Modular Networks: Learning to Decompose Tasks in the Language of Existing Models](https://md.research.elamur.ai/free/en/papers/khot-2021-text-modular-networks-learning-decompose/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Tushar Khot, Daniel Khashabi, Kyle Richardson, et al.*2021 · 95 citations*
74. [**Liger Kernel: Efficient Triton Kernels for LLM Training arXiv:2410.10989**Pin-Lun Hsu, Yun Dai, Vignesh Kothapalli, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/hsu-2024-liger-kernel-efficient-triton-kernels/)
75. **[Deep Models for Arabic Dialect Identification on Benchmarked Data](https://md.research.elamur.ai/free/en/papers/elaraby-2018-deep-models-arabic-dialect-identification/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Mohamed Elaraby, Muhammad Abdul-Mageed*2018 · 85 citations*
76. **[Language Modeling for Morphologically Rich Languages: Character-Aware Modeling for Word-Level Prediction](https://md.research.elamur.ai/free/en/papers/gerz-2018-language-modeling-morphologically-rich-languages/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Daniela Gerz, Ivan Vulić, Edoardo Ponti, et al.*2018 · 82 citations*
77. [**Large Language Models in Law: A Survey arXiv:2312.03718**Jinqi Lai, Wensheng Gan, Jiayang Wu, et al.*2023 · 80 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-large-language-models-law-survey/)
78. [**TASO: Optimizing Deep Learning Computation with Automatic Generation of Graph Substitutions SOSP 2019**Zhihao Jia, Oded Padon, James Thomas, et al.*2019 · 72 citations*](https://md.research.elamur.ai/free/en/papers/jia-2019-taso-optimizing-deep-learning-computation/)
79. [**Are you serious?: Rhetorical Questions and Sarcasm in Social Media Dialog**Shereen Oraby, Vrindavan Harrison, Amita Misra, et al.*2017 · 70 citations*](https://md.research.elamur.ai/free/en/papers/oraby-2017-are-you-serious-rhetorical-questions/)
80. **[Improving a Neural Semantic Parser by Counterfactual Learning from Human Bandit Feedback](https://md.research.elamur.ai/free/en/papers/lawrence-2018-improving-neural-semantic-parser-counterfactual/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Carolin Lawrence, Stefan Riezler*2018 · 68 citations*
81. **[Learning an Executable Neural Semantic Parser](https://md.research.elamur.ai/free/en/papers/cheng-2019-learning-executable-neural-semantic-parser/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Jianpeng Cheng, Siva Reddy, Vijay Saraswat, et al.*2019 · 63 citations*
82. **[Highly Effective Arabic Diacritization using Sequence to Sequence Modeling](https://md.research.elamur.ai/free/en/papers/mubarak-2019-highly-effective-arabic-diacritization-sequence/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Hamdy Mubarak, Ahmed Abdelali, Hassan Sajjad, et al.*2019 · 51 citations*
83. [**Cross-Domain Generalization of Neural Constituency Parsers**Daniel Fried, Nikita Kitaev, Dan Klein*2019 · 46 citations*](https://md.research.elamur.ai/free/en/papers/fried-2019-cross-domain-generalization-neural-constituency/)
84. [**Symbolic Learning Enables Self-Evolving Agents arXiv:2406.18532**Wangchunshu Zhou, Yixin Ou, Shengwei Ding, et al.*2024 · 34 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2024-symbolic-learning-enables-self-evolving/)
85. **[PropSegmEnt: A Large-Scale Corpus for Proposition-Level Segmentation and Entailment Recognition](https://md.research.elamur.ai/free/en/papers/chen-2023-propsegment-large-scale-corpus-proposition/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Sihao Chen, Senaka Buthpitiya, Alex Fabrikant, et al.*2023 · 31 citations*
86. **[A Survey on Asking Clarification Questions Datasets in Conversational Systems](https://md.research.elamur.ai/free/en/papers/rahmani-2023-survey-asking-clarification-questions-datasets/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Hossein A. Rahmani, Xi Wang, Yue Feng, et al.*2023 · 28 citations*
87. [**From Zero to Hero: Examining the Power of Symbolic Tasks in Instruction Tuning arXiv:2304.07995**Qian Liu, Fan Zhou, Zhengbao Jiang, et al.*2023 · 24 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-zero-hero-examining-power-symbolic/)
88. **[Predicting In-Game Actions from Interviews of NBA Players](https://md.research.elamur.ai/free/en/papers/oved-2020-predicting-game-actions-interviews-nba/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Nadav Oved, Amir Feder, Roi Reichart*2020 · 24 citations*
89. **[CRAFT Your Dataset: Task-Specific Synthetic Dataset Generation Through Corpus Retrieval and Augmentation](https://md.research.elamur.ai/free/en/papers/ziegler-2025-craft-your-dataset-task-specific/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Ingo Ziegler, Abdullatif Köksal, Desmond Elliott, et al.*2025 · 20 citations*
90. **[Deep Contextualized Pairwise Semantic Similarity for Arabic Language Questions](https://md.research.elamur.ai/free/en/papers/al-bataineh-2019-deep-contextualized-pairwise-semantic-similarity/) [Proceedings - International Conference on Tools with Artificial Intelligence, TAI](https://md.research.elamur.ai/free/en/venues/proceedings-international-conference-on-tools-with-artificial-intelligence-tai/)**Hesham Al-Bataineh, Wael Farhan, Ahmad Mustafa, et al.*2019 · 16 citations*
91. **[ARBML: Democratizing Arabic Natural Language Processing Tools](https://md.research.elamur.ai/free/en/papers/alyafeai-2020-arbml-democratizing-arabic-natural-language/) [Workshop for NLP Open Source Software (NLP-OSS)](https://md.research.elamur.ai/free/en/venues/nlposs/)**Zaid Alyafeai, Maged S. Al-Shaibani*2020 · 14 citations*
92. [**CodeS: Natural Language to Code Repository via Multi-Layer Sketch arXiv:2403.16443**Daoguang Zan, Ailun Yu, Wei Liu, et al.*2024 · 9 citations*](https://md.research.elamur.ai/free/en/papers/zan-2024-codes-natural-language-code-repository/)
93. [**A Characterwise Windowed Approach to Hebrew Morphological Segmentation**Amir Zeldes*2018 · 6 citations*](https://md.research.elamur.ai/free/en/papers/zeldes-2018-characterwise-windowed-approach-hebrew-morphological/)
94. **[Automatically Detecting Reduced-formed English Pronunciations by Using Deep Learning](https://md.research.elamur.ai/free/en/papers/chen-2022-automatically-detecting-reduced-formed-english/) [Workshop on Innovative Use of NLP for Building Educational Applications (BEA)](https://md.research.elamur.ai/free/en/venues/bea/)**Lei Chen, Chenglin Jiang, Yiwei Gu, et al.*2022 · 4 citations*
95. [**DeepSTL — From English Requirements to Signal Temporal Logic arXiv:2109.10294**Jie He, Ezio Bartocci, Dejan Ničkovic, et al.*2021 · 4 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-deepstl-english-requirements-signal-temporal/)
96. **[The University of Texas System Submission for the Code-Switching Workshop Shared Task 2018](https://md.research.elamur.ai/free/en/papers/janke-2018-university-texas-system-submission-code/) [Workshop on Computational Approaches to Linguistic Code-Switching](https://md.research.elamur.ai/free/en/venues/calcs/)**Florian Janke, Tongrui Li, Eric Rincón, et al.*2018 · 4 citations*
97. **[CuBridge: An LLM-Based Framework for Understanding and Reconstructing High-Performance Attention Kernels](https://md.research.elamur.ai/free/en/papers/ma-2026-cubridge-llm-based-framework-understanding/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Xing Ma, Yangjie Zhou, Wu Sun, et al.*2026 · 3 citations*
98. **[SiPOS: A Benchmark Dataset for Sindhi Part-of-Speech Tagging](https://md.research.elamur.ai/free/en/papers/ali-2021-sipos-benchmark-dataset-sindhi-part/) [Recent Advances in Natural Language Processing (RANLP)](https://md.research.elamur.ai/free/en/venues/ranlp/)**Wazir Ali, Zenglin Xu, Jay Kumar*2021 · 3 citations*
99. [**Attention-based sequence-to-sequence model for speech recognition: development of state-of-the-art system on LibriSpeech and its application to non-native English arXiv:1810.13088**Yan Yin, Ramon Prieto, Bin Wang, et al.*2018 · 2 citations*](https://md.research.elamur.ai/free/en/papers/yin-2018-attention-based-sequence-sequence-model/)
100. **[DeepPavlov Strikes Back: A Toolkit for Improving LLM Reliability and Trustworthiness](https://md.research.elamur.ai/free/en/papers/nikolaev-2026-deeppavlov-strikes-back-toolkit-improving/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Evgenii Nikolaev, Timur Ionov, Anna Korzanova, et al.*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/authors/martin-abadi/) — Martín Abadi