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# Who cites «Curriculum Learning» · page 5

659 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/bengio-2009-curriculum-learning/)

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1. [Legal NERC with ontologies, Wikipedia and curriculum learning](https://aclanthology.org/E17-2041/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 2, Short Papers*
2. [MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels](https://arxiv.org/abs/1712.05055) *— 2017 · arXiv:1712.05055 · published at ICML 2018*
3. [Neural Semantic Encoders](https://aclanthology.org/E17-1038/) *— 2017 · Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers*
4. [Repeat before Forgetting: Spaced Repetition for Efficient and Effective Training of Neural Networks](https://aclanthology.org/D17-1255/) *— 2017 · Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing*
5. [Results of the WMT17 Neural MT Training Task](https://aclanthology.org/W17-4757/) *— 2017 · Proceedings of the Second Conference on Machine Translation*
6. [Sub-domain Modelling for Dialogue Management with Hierarchical Reinforcement Learning](https://aclanthology.org/W17-5512/) *— 2017 · Proceedings of the 18th Annual SIGdial Meeting on Discourse and Dialogue*
7. [Super-Convergence: Very Fast Training of Neural Networks Using Large Learning Rates](https://arxiv.org/abs/1708.07120) *— 2017 · arXiv:1708.07120*
8. [Teacher-Student Curriculum Learning](https://arxiv.org/abs/1707.00183) *— 2017 · arXiv:1707.00183*
9. [Towards Black-box Iterative Machine Teaching](https://arxiv.org/abs/1710.07742) *— 2017 · arXiv:1710.07742*
10. [An overview of gradient descent optimization algorithms](https://arxiv.org/abs/1609.04747) *— 2016 · arXiv:1609.04747*
11. [Easy Questions First? A Case Study on Curriculum Learning for Question Answering](https://aclanthology.org/P16-1043/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Extensions and Limitations of the Neural GPU](https://arxiv.org/abs/1611.00736) *— 2016 · arXiv:1611.00736*
13. [Going deeper with deep knowledge tracing](https://eric.ed.gov/?id=ED592679) *— 2016*
14. [Investigating LSTMs for Joint Extraction of Opinion Entities and Relations](https://aclanthology.org/P16-1087/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Learning the Curriculum with Bayesian Optimization for Task-Specific Word Representation Learning](https://aclanthology.org/P16-1013/) *— 2016 · Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [Memory-Bounded Left-Corner Unsupervised Grammar Induction on Child-Directed Input](https://aclanthology.org/C16-1092/) *— 2016 · Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers*
17. [A Roadmap towards Machine Intelligence](https://arxiv.org/abs/1511.08130) *— 2015 · arXiv:1511.08130*
18. [Action-Conditional Video Prediction using Deep Networks in Atari Games](https://arxiv.org/abs/1507.08750) *— 2015 · arXiv:1507.08750*
19. [Context-Dependent Translation Selection Using Convolutional Neural Network](https://aclanthology.org/P15-2088/) *— 2015 · Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
20. [Deep Speech 2: End-to-End Speech Recognition in English and Mandarin](https://arxiv.org/abs/1512.02595) *— 2015 · arXiv:1512.02595*
21. [FaceNet: A Unified Embedding for Face Recognition and Clustering](https://arxiv.org/abs/1503.03832) *— 2015 · arXiv:1503.03832*
22. [Incremental Adaptation Strategies for Neural Network Language Models](https://aclanthology.org/W15-4006/) *— 2015 · Proceedings of the 3rd Workshop on Continuous Vector Space Models and their Compositionality*
23. [Machine Teaching: An Inverse Problem to Machine Learning and an Approach Toward Optimal Education](https://pages.cs.wisc.edu/~jerryzhu/pub/MachineTeachingAAAI15.pdf) *— 2015 · AAAI 2015*
24. [Neural Random-Access Machines](https://arxiv.org/abs/1511.06392) *— 2015 · arXiv:1511.06392*
25. [Reinforcement Learning Neural Turing Machines - Revised](https://arxiv.org/abs/1505.00521) *— 2015 · arXiv:1505.00521*
26. [Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks](https://arxiv.org/abs/1506.03099) *— 2015 · arXiv:1506.03099*
27. [Specializing Word Embeddings for Similarity or Relatedness](https://aclanthology.org/D15-1242/) *— 2015 · Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing*
28. [Unsupervised Text Normalization Using Distributed Representations of Words and Phrases](https://aclanthology.org/W15-1502/) *— 2015 · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing*
29. [Unsupervised Topic Modeling for Short Texts Using Distributed Representations of Words](https://aclanthology.org/W15-1526/) *— 2015 · Proceedings of the 1st Workshop on Vector Space Modeling for Natural Language Processing*
30. [Exploring Compositional Architectures and Word Vector Representations for Prepositional Phrase Attachment](https://md.mova.elamur.ai/free/en/docs/papers/tacl-2014-belinkov-exploring-compositional-architectures-word-vector/) *— our card · 2014 · Transactions of the Association for Computational Linguistics*
31. [Learning Representations for Weakly Supervised Natural Language Processing Tasks](https://aclanthology.org/J14-1004/) *— 2014 · Computational Linguistics*
32. [Learning to Execute](https://arxiv.org/abs/1410.4615) *— 2014 · arXiv:1410.4615*
33. [Transduction Recursive Auto-Associative Memory: Learning Bilingual Compositional Distributed Vector Representations of Inversion Transduction Grammars](https://aclanthology.org/W14-4013/) *— 2014 · Proceedings of SSST-8, Eighth Workshop on Syntax, Semantics and Structure in Statistical Translation*
34. [Aggregating Continuous Word Embeddings for Information Retrieval](https://aclanthology.org/W13-3212/) *— 2013 · Proceedings of the Workshop on Continuous Vector Space Models and their Compositionality*
35. [Bilingual Word Embeddings for Phrase-Based Machine Translation](https://aclanthology.org/D13-1141/) *— 2013 · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing*
36. [Breaking Out of Local Optima with Count Transforms and Model Recombination: A Study in Grammar Induction](https://aclanthology.org/D13-1204/) *— 2013 · Proceedings of the 2013 Conference on Empirical Methods in Natural Language Processing*
37. [Polyglot: Distributed Word Representations for Multilingual NLP](https://aclanthology.org/W13-3520/) *— 2013 · Proceedings of the Seventeenth Conference on Computational Natural Language Learning*
38. [Stagger: an Open-Source Part of Speech Tagger for Swedish](https://nejlt.ep.liu.se/article/view/1653) *— 2013 · Northern European Journal of Language Technology*
39. [Behavioral Factors in Interactive Training of Text Classifiers](https://aclanthology.org/N12-1066/) *— 2012 · Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
40. [Practical recommendations for gradient-based training of deep architectures](https://arxiv.org/abs/1206.5533) *— 2012 · arXiv:1206.5533*
41. [Representation Learning: A Review and New Perspectives](https://arxiv.org/abs/1206.5538) *— 2012 · arXiv:1206.5538*
42. [Three Dependency-and-Boundary Models for Grammar Induction](https://aclanthology.org/D12-1063/) *— 2012 · Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning*
43. [Compositional Matrix-Space Models for Sentiment Analysis](https://aclanthology.org/D11-1016/) *— 2011 · Proceedings of the 2011 Conference on Empirical Methods in Natural Language Processing*
44. [Language Models as Representations for Weakly Supervised NLP Tasks](https://aclanthology.org/W11-0315/) *— 2011 · Proceedings of the Fifteenth Conference on Computational Natural Language Learning*
45. [Natural Language Processing (almost) from Scratch](https://arxiv.org/abs/1103.0398) *— 2011 · arXiv:1103.0398*
46. [From Baby Steps to Leapfrog: How ``Less is More'''' in Unsupervised Dependency Parsing](https://aclanthology.org/N10-1116/) *— 2010 · Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics*
47. [Word Representations: A Simple and General Method for Semi-Supervised Learning](https://aclanthology.org/P10-1040/) *— 2010 · Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics*
48. abdessaied-2022-video-language-co-attention-multimodal
49. ahuja-2020-no-gestures-left-behind-learning
50. ai-2026-trajectory-learnability-offline-policy-distillation
51. ailneni-2026-utd-hltri-semeval-2026-task
52. akdemir-2020-research-task-discovery-transfer-learning
53. arslan-2026-malto-semeval-2026-task-13
54. askarian-2021-curriculum-learning-effectively-improves-low
55. atreja-2026-pioneer-agent-continual-improvement-small
56. atrio-2021-iict-yverdon-system-wmt-2021
57. aulamo-2026-challenge-finding-robust-efficient-strategies
58. aynetdinov-2025-pre-training-curriculum-multi-token
59. bai-2026-dice-diffusion-large-language-models
60. banerjee-2025-hits-disrpt-2025-discourse-segmentation
61. bar-2025-swiss-german-speech-translation-curse
62. bhadury-2026-soft-curriculum-learning-optimizing-fresh
63. bhasin-2024-how-does-multi-task-training
64. bi-2026-logical-structure-as-knowledge-enhancing
65. bondarenko-2025-pisets-robust-speech-recognition-system
66. both-2025-small-models-smarter-learning-power
67. brei-2025-classifying-unreliable-narrators-large-language
68. brunato-2025-learning-impairment-leveraging-insights-clinical
69. bui-2020-how-state-art-models-can
70. cai-2025-k-order-ranking-preference-optimization
71. chan-2026-statistical-properties-king-wen-sequence
72. chaudhury-2024-dacl-disfluency-augmented-curriculum-learning
73. chen-2019-quantifying-similarity-between-relations-fact
74. chen-2025-enhancing-attributed-question-answering-tailored
75. chen-2025-muon-accelerated-attention-distillation-real
76. chen-2025-temporal-action-detection-model-compression
77. chen-2026-data-centric-sample-centric-enhancing
78. chen-2026-hybrid-quantum-classical-pinns-scientific
79. chen-2026-trainee-trainer-llm-designed-training
80. chong-2024-category-wise-fine-tuning-resisting
81. christopoulou-2022-training-dynamics-curriculum-learning-study
82. conde-2024-real-time-4k-super-resolution
83. cordier-2023-few-shot-structured-policy-learning
84. dai-2026-demystifying-data-organization-enhanced-llm
85. dan-2021-compositional-data-task-augmentation-instruction
86. das-2025-zero-shot-grammar-competency-estimation
87. desai-2019-adaptive-ensembling-unsupervised-domain-adaptation
88. do-2026-space-unlocking-sample-efficient-large
89. doan-2024-dual-module-denoising-approach-curriculum
90. dragomir-2026-clewr-curriculum-learning-restarts-machine
91. duan-2024-restoring-ancient-ideograph-multimodal-multitask
92. ek-2023-vector-norms-as-approximation-syntactic
93. evgin-2025-metninozu-biolaysumm2025-text-summarization-reverse
94. fan-2026-dual-modality-framework-ancient-document
95. fekete-2023-gradual-language-model-adaptation-fine
96. feng-2023-multi-agent-language-learning-symbolic
97. feng-2025-your-pretrained-model-tells-difficulty
98. frydenlund-2025-language-models-graph-searching-supervision
99. fu-2025-easy-hard-curriculum-make-reasoning
100. gao-2023-easy-hard-learning-information-extraction

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