---
title: "Who cites Andriy Mnih — 558 papers of our library"
description: "558 papers of the Elamur Research library cite the works of Andriy Mnih, found in their bibliographies; the most cited first."
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---
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# Who cites Andriy Mnih

590 citations of their works from 558 papers of our library (of the 149,439 whose bibliographies we read) · 48 with a page here, the most cited first · [← Andriy Mnih](https://md.research.elamur.ai/free/en/authors/andriy-mnih/)

1. **[GloVe: Global Vectors for Word Representation](https://md.research.elamur.ai/free/en/papers/pennington-2014-glove-global-vectors-word-representation/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Jeffrey Pennington, Richard Socher, Christopher Manning*2014 · 38,706 citations*
2. [**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/)
3. [**Efficient Estimation of Word Representations in Vector Space arXiv:1301.3781**Tomas Mikolov, Kai Chen, Greg Corrado, et al.*2013 · 28,759 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-efficient-estimation-word-representations-vector/)
4. [**Distributed Representations of Words and Phrases and their Compositionality arXiv:1310.4546**Tomas Mikolov, Ilya Sutskever, Kai Chen, et al.*2013 · 21,623 citations*](https://md.research.elamur.ai/free/en/papers/mikolov-2013-distributed-representations-words-phrases-their/)
5. [**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/)
6. [**Categorical Reparameterization with Gumbel-Softmax arXiv:1611.01144**Eric Jang, Shixiang Gu, Ben Poole*2016 · 3,656 citations*](https://md.research.elamur.ai/free/en/papers/jang-2016-categorical-reparameterization-gumbel-softmax/)
7. [**Pre-trained Models for Natural Language Processing: A Survey arXiv:2003.08271 · SCIENCE CHINA Technological Sciences,2020, 63, 1872-1897**Xipeng Qiu, Tianxiang Sun, Yige Xu, et al.*2020 · 1,759 citations*](https://md.research.elamur.ai/free/en/papers/qiu-2020-pre-trained-models-natural-language/)
8. **[Analyzing Multi-Head Self-Attention: Specialized Heads Do the Heavy Lifting, the Rest Can Be Pruned](https://md.research.elamur.ai/free/en/papers/voita-2019-analyzing-multi-head-self-attention/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Elena Voita, David Talbot, Fedor Moiseev, et al.*2019 · 1,535 citations*
9. [**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/)
10. **[From Captions to Visual Concepts and Back](https://md.research.elamur.ai/free/en/papers/fang-2014-captions-visual-concepts-back/) [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/)**Hao Fang, Saurabh Gupta, Forrest Iandola, et al.*2014 · 1,450 citations*
11. **[FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search](https://md.research.elamur.ai/free/en/papers/wu-2018-fbnet-hardware-aware-efficient-convnet/) [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/)**Bichen Wu, Xiaoliang Dai, Peizhao Zhang, et al.*2018 · 1,405 citations*
12. **[A Primer on Neural Network Models for Natural Language Processing](https://md.research.elamur.ai/free/en/papers/goldberg-2015-primer-neural-network-models-natural/) [Journal of Artificial Intelligence Research](https://md.research.elamur.ai/free/en/venues/journal-of-artificial-intelligence-research/)**Yoav Goldberg*2015 · 1,303 citations*
13. **[On Using Very Large Target Vocabulary for Neural Machine Translation](https://md.research.elamur.ai/free/en/papers/jean-2015-very-large-target-vocabulary-neural/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Sébastien Jean, Kyunghyun Cho, Roland Memisevic, et al.*2015 · 1,013 citations*
14. **[Matching the Blanks: Distributional Similarity for Relation Learning](https://md.research.elamur.ai/free/en/papers/soares-2019-matching-blanks-distributional-similarity-relation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Livio Baldini Soares, Nicholas FitzGerald, Jeffrey Ling, et al.*2019 · 999 citations*
15. [**Style Transfer from Non-Parallel Text by Cross-Alignment**Tianxiao Shen, Tao Lei, Regina Barzilay, et al.*2017 · 979 citations*](https://md.research.elamur.ai/free/en/papers/shen-2017-style-transfer-non-parallel-text/)
16. **[Using the Output Embedding to Improve Language Models](https://md.research.elamur.ai/free/en/papers/press-2017-output-embedding-improve-language-models/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Ofir Press, Lior Wolf*2017 · 909 citations*
17. [**Curriculum Learning**Yoshua Bengio, Jérôme Louradour, Ronan Collobert, et al.*2009 · 724 citations*](https://md.research.elamur.ai/free/en/papers/bengio-2009-curriculum-learning/)
18. **[Pointing the Unknown Words](https://md.research.elamur.ai/free/en/papers/gulcehre-2016-pointing-unknown-words/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Caglar Gulcehre, Sungjin Ahn, Ramesh Nallapati, et al.*2016 · 651 citations*
19. **[Learning Word Vectors for 157 Languages](https://md.research.elamur.ai/free/en/papers/grave-2018-learning-word-vectors-157-languages/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Edouard Grave, Piotr Bojanowski, Prakhar Gupta, et al.*2018 · 646 citations*
20. **[Advances in Pre-Training Distributed Word Representations](https://md.research.elamur.ai/free/en/papers/mikolov-2017-advances-pre-training-distributed-word/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Tomas Mikolov, Edouard Grave, Piotr Bojanowski, et al.*2017 · 628 citations*
21. **[Representation Learning Using Multi-Task Deep Neural Networks for Semantic Classification and Information Retrieval](https://md.research.elamur.ai/free/en/papers/liu-2015-representation-learning-multi-task-deep/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Xiaodong Liu, Jianfeng Gao, Xiaodong He, et al.*2015 · 471 citations*
22. **[End-to-end learning of semantic role labeling using recurrent neural networks](https://md.research.elamur.ai/free/en/papers/zhou-2015-end-end-learning-semantic-role/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jie Zhou, Wei Xu*2015 · 466 citations*
23. [**Representation Learning: A Review and New Perspectives arXiv:1206.5538**Yoshua Bengio, Aaron Courville, Pascal Vincent*2012 · 383 citations*](https://md.research.elamur.ai/free/en/papers/bengio-2012-representation-learning-review-new-perspectives/)
24. **[Improving Lexical Embeddings with Semantic Knowledge](https://md.research.elamur.ai/free/en/papers/yu-2014-improving-lexical-embeddings-semantic-knowledge/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mo Yu, Mark Dredze*2014 · 371 citations*
25. **[Parameter-Efficient Transfer Learning with Diff Pruning](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Demi Guo, Alexander Rush, Yoon Kim*2021 · 367 citations*
26. **[Semi-supervised Multitask Learning for Sequence Labeling](https://md.research.elamur.ai/free/en/papers/rei-2017-semi-supervised-multitask-learning-sequence/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Marek Rei*2017 · 347 citations*
27. **[Decoding with Large-Scale Neural Language Models Improves Translation](https://md.research.elamur.ai/free/en/papers/vaswani-2013-decoding-large-scale-neural-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Ashish Vaswani, Yinggong Zhao, Victoria Fossum, et al.*2013 · 318 citations*
28. **[What Can Transformers Learn In-Context? A Case Study of Simple Function Classes](https://md.research.elamur.ai/free/en/papers/garg-2022-transformers-learn-context-case-study/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shivam Garg, Dimitris Tsipras, Percy Liang, et al.*2022 · 209 citations*
29. **[CharacterBERT: Reconciling ELMo and BERT for Word-Level Open-Vocabulary Representations From Characters](https://md.research.elamur.ai/free/en/papers/boukkouri-2020-characterbert-reconciling-elmo-bert-word/) [International Conference on Computational Linguistics (COLING)](https://md.research.elamur.ai/free/en/venues/coling/)**Hicham El Boukkouri, Olivier Ferret, Thomas Lavergne, et al.*2020 · 206 citations*
30. [**Efficient softmax approximation for GPUs arXiv:1609.04309**Edouard Grave, Armand Joulin, Moustapha Cisse, et al.*2016 · 205 citations*](https://md.research.elamur.ai/free/en/papers/grave-2016-efficient-softmax-approximation-gpus/)
31. [**A Neural Knowledge Language Model arXiv:1608.00318**Sungjin Ahn, Heeyoul Choi, Tanel Pärnamaa, et al.*2016 · 146 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2016-neural-knowledge-language-model/)
32. [**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/)
33. **[RADAR: Robust AI-Text Detection via Adversarial Learning](https://md.research.elamur.ai/free/en/papers/hu-2023-radar-robust-ai-text-detection/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xiaomeng Hu, Pin-Yu Chen, Tsung-Yi Ho*2023 · 134 citations*
34. **[Scaling Deep Contrastive Learning Batch Size under Memory Limited Setup](https://md.research.elamur.ai/free/en/papers/gao-2021-scaling-deep-contrastive-learning-batch/) [Workshop on Representation Learning for NLP](https://md.research.elamur.ai/free/en/venues/repl4nlp/)**Luyu Gao, Yunyi Zhang, Jiawei Han, et al.*2021 · 131 citations*
35. **[Controllable Natural Language Generation with Contrastive Prefixes](https://md.research.elamur.ai/free/en/papers/qian-2022-controllable-natural-language-generation-contrastive/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Qian, Li Dong, Yelong Shen, et al.*2022 · 130 citations*
36. **[Leakage-Adjusted Simulatability: Can Models Generate Non-Trivial Explanations of Their Behavior in Natural Language?](https://md.research.elamur.ai/free/en/papers/hase-2020-leakage-adjusted-simulatability-can-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Peter Hase, Shiyue Zhang, Harry Xie, et al.*2020 · 126 citations*
37. [**Online normalizer calculation for softmax arXiv 2018 (NVIDIA)**Maxim Milakov, Natalia Gimelshein*2018 · 123 citations*](https://md.research.elamur.ai/free/en/papers/milakov-2018-online-normalizer-calculation-softmax/)
38. [**User Behavior Simulation with Large Language Model based Agents arXiv:2306.02552**Lei Wang, Jingsen Zhang, Hao Yang, et al.*2023 · 64 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-user-behavior-simulation-large-language/)
39. **[Neural Lattice Language Models](https://md.research.elamur.ai/free/en/papers/buckman-2018-neural-lattice-language-models/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jacob Buckman, Graham Neubig*2018 · 35 citations*
40. **[Detecting Institutional Dialog Acts in Police Traffic Stops](https://md.research.elamur.ai/free/en/papers/prabhakaran-2018-detecting-institutional-dialog-acts-police/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Vinodkumar Prabhakaran, Camilla Griffiths, Hang Su, et al.*2018 · 20 citations*
41. **[MAGNET: Improving the Multilingual Fairness of Language Models with Adaptive Gradient-Based Tokenization](https://md.research.elamur.ai/free/en/papers/ahia-2024-magnet-improving-multilingual-fairness-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2024 · 19 citations*
42. [**Probabilistic Modelling of Morphologically Rich Languages**Jan A. Botha*2015 · 4 citations*](https://md.research.elamur.ai/free/en/papers/botha-2015-probabilistic-modelling-morphologically-rich-languages/)
43. **[DiaLex: A Benchmark for Evaluating Multidialectal Arabic Word Embeddings](https://md.research.elamur.ai/free/en/papers/mageed-2021-dialex-benchmark-evaluating-multidialectal-arabic/) [Arabic Natural Language Processing Workshop (WANLP)](https://md.research.elamur.ai/free/en/venues/wanlp/)**Muhammad Abdul-Mageed, Shady Elbassuoni, Jad Doughman, et al.*2021 · 3 citations*
44. **[Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives](https://md.research.elamur.ai/free/en/papers/xu-2026-modeling-annotator-disagreement-demographic-aware/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yinuo Xu, Veronica Derricks, Allison Earl, et al.*2026 · 1 citation*
45. **[Context-Conditioned Masked LoRA: Dynamic Rank Routing for Compute-Efficient Parameter-Efficient Fine-Tuning](https://md.research.elamur.ai/free/en/papers/rafiuddin-2026-context-conditioned-masked-lora-dynamic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rifat Rafiuddin, Rafae Abdullah*2026*
46. **[GRASPrune: Global Gating for Budgeted Structured Pruning of Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2026-grasprune-global-gating-budgeted-structured/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Ziyang Wang, Jiangfeng Xiao, Chuan Xiao, et al.*2026*
47. **[MaskLoRA: Low-Rank Subspace–Induced Token Masking for Efficient and Faithful Language Models](https://md.research.elamur.ai/free/en/papers/rafiuddin-2026-masklora-low-rank-subspace-induced/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Rifat Rafiuddin*2026*
48. **[Algorithmes à base d'échantillonage pour l'entraînement de modèles de langue neuronaux (Here the title in English)](https://md.research.elamur.ai/free/en/papers/labeau-2018-algorithmes-base-d-echantillonage-pour/) [JEP-TALN-RECITAL (the French-speaking conferences on speech and natural language processing)](https://md.research.elamur.ai/free/en/venues/jeptalnrecital/)**Matthieu Labeau, Alexandre Allauzen*2018*

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