[Mova](https://md.mova.elamur.ai/free/en/) · [this page in HTML](https://mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning/)

[home](https://md.mova.elamur.ai/free/en/) / [docs](https://md.mova.elamur.ai/free/en/docs/) / [papers](https://md.mova.elamur.ai/free/en/docs/papers/) / [cited-by](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/) / Who cites «Curriculum Learning»

# Who cites «Curriculum Learning»

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/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-7/)

1. [A Review of Incorporating Psychological Theories in LLMs](https://aclanthology.org/2026.eacl-long.350/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [Adaptive Teacher Exposure for Self-Distillation in LLM Reasoning](https://arxiv.org/abs/2605.11458) *— 2026 · arXiv:2605.11458*
3. [Anchoring the Judge: Curriculum-Based Adaptation and Reference-Anchored MQM for LLM-Based Machine Translation of an Unseen Low-Resource Language - A Case of Nupe](https://aclanthology.org/2026.loreslm-1.19/) *— 2026 · Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)*
4. [Annotation Entropy Predicts Per-Example Learning Dynamics in LoRA Fine-Tuning](https://aclanthology.org/2026.acl-srw.11/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
5. [Baichuan-M3: Modeling Clinical Inquiry for Reliable Medical Decision-Making](https://arxiv.org/abs/2602.06570) *— 2026 · arXiv:2602.06570*
6. [Beyond Random Sampling: Efficient Language Model Pretraining via Curriculum Learning](https://aclanthology.org/2026.eacl-long.271/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [CRPS: Curriculum Replay via Progressive Suffixes from Successful Trajectories for Long-Horizon LLM Agents](https://aclanthology.org/2026.findings-acl.680/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [Calibrated Progressive Distillation: Co-Designing Curriculum and Target Mixing for Knowledge Distillation of Large Language Models](https://aclanthology.org/2026.findings-acl.335/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
9. [Communication Enables Cooperation in LLM Agents: A Comparison with Curriculum-Based Approaches](https://aclanthology.org/2026.eacl-short.23/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)*
10. [Compatibility-Aware Dynamic Fine-Tuning for Large Language Models](https://aclanthology.org/2026.acl-long.1383/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [Curriculum Learning based Hierarchical Scoring and Analysis Framework for Question Answering Task Evaluation](https://aclanthology.org/2026.findings-acl.332/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
12. [Curriculum-RLAIF: Curriculum Alignment with Reinforcement Learning from AI Feedback](https://aclanthology.org/2026.findings-acl.1685/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
13. [DMRetriever: A Family of Models for Improved Text Retrieval in Disaster Management](https://aclanthology.org/2026.acl-long.722/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
14. [DSMentor: Curriculum-Guided Inference with Online Memory for Data-Science LLM Agents](https://aclanthology.org/2026.surgellm-1.12/) *— 2026 · Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026)*
15. [Data-Centric Approach at the LoResMT 2026 Turkic Translation Challenge: Russian-Kyrgyz](https://aclanthology.org/2026.loresmt-1.22/) *— 2026 · Proceedings for the Ninth Workshop on Technologies for Machine Translation of Low Resource Languages (LoResMT 2026)*
16. [Do large language models and humans follow similar learning stages? Assessing GPT-2's order of Swedish grammar acquisition within the Processability Theory framework](https://aclanthology.org/2026.cdl-1.10/) *— 2026 · Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)*
17. [Dynamic Cheatsheet: Test-Time Learning with Adaptive Memory](https://aclanthology.org/2026.eacl-long.333/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [DynamicFocalPO: Adaptive Focusing Strategy for Preference Optimization](https://aclanthology.org/2026.findings-acl.1009/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
19. [Enhancing Linguistic Competence of Language Models through Pre-training with Language Learning Tasks](https://aclanthology.org/2026.acl-short.27/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
20. [EvaHan 2026 Ancient Books Multimodal OCR and Layout Analysis System Technical Report](https://aclanthology.org/2026.lt4hala-1.28/) *— 2026 · Proceedings of the Fourth Workshop on Language Technologies for Historical and Ancient Languages (LT4HALA 2026) @ LREC 2026*
21. [Explain in Your Own Words: Improving Reasoning via Token-Selective Dual Knowledge Distillation](https://arxiv.org/abs/2603.13260) *— 2026 · arXiv:2603.13260*
22. [From Answers to Arguments: Toward Trustworthy Clinical Diagnostic Reasoning with Toulmin-Guided Curriculum Goal-Conditioned Learning](https://aclanthology.org/2026.acl-long.637/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [From Past To Path: Masked History Learning for Next-Item Prediction in Generative Recommendation](https://aclanthology.org/2026.acl-long.475/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [HEBATRON: A Hebrew-Specialized Open-Weight Mixture-of-Experts Language Model](https://md.mova.elamur.ai/free/en/docs/papers/langstream-2026-kayzer-hebatron-hebrew-specialized-open-weight/) *— our card · 2026 · arXiv:2605.11255*
25. [How to Allocate, How to Learn? Dynamic Rollout Allocation and Advantage Modulation for Policy Optimization](https://aclanthology.org/2026.findings-acl.724/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
26. [Learning from Evolving Training Dynamics: An Entropy-Maximizing Data Curation Strategy for LLM Supervised Post-Training](https://aclanthology.org/2026.acl-long.505/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [Learning from Mistakes: Negative Reasoning Samples Enhance Out-of-Domain Generalization](https://aclanthology.org/2026.acl-long.1370/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [LoRE: Enhancing Search Relevance with Progressive Chain-of-Thought and Preference Alignment](https://aclanthology.org/2026.findings-acl.1536/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
29. [Making Synthetic Questions More Child-Directed: Prompting and Sampling Effects](https://aclanthology.org/2026.cdl-1.18/) *— 2026 · Proceedings of the 1st Workshop on Computational Developmental Linguistics (CDL)*
30. [Multi-Task Instruction Tuning via Data Scheduling for Low-Resource Arabic SpeechLLMs](https://arxiv.org/abs/2601.12494) *— 2026 · arXiv:2601.12494*
31. [One Refiner to Unlock Them All: Inference-Time Reasoning Elicitation via Reinforcement Query Refinement](https://aclanthology.org/2026.acl-long.1807/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [Progressive Re-ranking for Multimodal Retrieval-Augmented Generation via Curriculum Learning](https://aclanthology.org/2026.findings-acl.2045/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
33. [Rethinking Data Mixture for Large Language Models: A Comprehensive Survey and New Perspectives](https://aclanthology.org/2026.findings-eacl.15/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
34. [Revisiting Self-Play Preference Optimization: On the Role of Prompt Difficulty](https://aclanthology.org/2026.findings-acl.144/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
35. [SSR-A: Spatial- and Semantic-Aware Instructions and Curriculum Reinforcement for Advertisement Compliant Rectification](https://aclanthology.org/2026.acl-industry.23/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)*
36. [SeCuRepair: Semantics-Aligned, Curriculum-Driven, and Reasoning-Enhanced Vulnerability Repair Framework](https://aclanthology.org/2026.acl-long.1481/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [TAROT: Test-driven and Capability-adaptive Curriculum Reinforcement Fine-tuning for Code Generation with Large Language Models](https://arxiv.org/abs/2602.15449) *— 2026 · arXiv:2602.15449*
38. [TIP: Token Importance in On-Policy Distillation](https://arxiv.org/abs/2604.14084) *— 2026 · arXiv:2604.14084*
39. [Toward Cognitive Alignment in Large Language Models: Integrating Linguistic Theory and Human Data](https://aclanthology.org/2026.cmcl-1.26/) *— 2026 · Proceedings of the 15th Workshop on Cognitive Modeling and Computational Linguistics*
40. [What Makes a Good Curriculum? Disentangling the Effects of Data Ordering on LLM Mathematical Reasoning](https://aclanthology.org/2026.acl-long.1591/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
41. [Why Supervised Fine-Tuning Fails to Learn: A Systematic Study of Incomplete Learning in Large Language Models](https://aclanthology.org/2026.acl-long.1393/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [Writing-RL: Advancing Long-form Writing via Adaptive Curriculum Reinforcement Learning](https://aclanthology.org/2026.acl-long.255/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [高效提升多模态大语言模型推理能力的级联强化学习策略](https://www.aas.net.cn/cn/article/doi/10.16383/j.aas.c250515) *— 2026 · 自动化学报 (Acta Automatica Sinica)*
44. [A Chain-of-Task Framework for Instruction Tuning of LLMs Based on Chinese Grammatical Error Correction](https://aclanthology.org/2025.coling-main.577/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
45. [A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence](https://arxiv.org/abs/2507.21046) *— 2025 · arXiv:2507.21046*
46. [Align-SLM: Textless Spoken Language Models with Reinforcement Learning from AI Feedback](https://aclanthology.org/2025.acl-long.997/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
47. [Alignment with Fill-In-the-Middle for Enhancing Code Generation](https://aclanthology.org/2025.emnlp-main.419/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
48. [Autonomous Data Selection with Zero-shot Generative Classifiers for Mathematical Texts](https://aclanthology.org/2025.findings-acl.216/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
49. [Babies Learn to Look Ahead: Multi-Token Prediction in Small LMs](https://aclanthology.org/2025.babylm-main.41/) *— 2025 · Proceedings of the First BabyLM Workshop*
50. [Batch-wise Convergent Pre-training: Step-by-Step Learning Inspired by Child Language Development](https://aclanthology.org/2025.babylm-main.36/) *— 2025 · Proceedings of the First BabyLM Workshop*
51. [Being Strong Progressively! Enhancing Knowledge Distillation of Large Language Models through a Curriculum Learning Framework](https://arxiv.org/abs/2506.05695) *— 2025 · arXiv:2506.05695*
52. [Between Circuits and Chomsky: Pre-pretraining on Formal Languages Imparts Linguistic Biases](https://aclanthology.org/2025.acl-long.478/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [Beyond Accuracy: Dissecting Mathematical Reasoning for LLMs Under Reinforcement Learning](https://arxiv.org/abs/2506.04723) *— 2025 · arXiv:2506.04723*
54. [Beyond Repetition: Text Simplification and Curriculum Learning for Data-Constrained Pretraining](https://aclanthology.org/2025.babylm-main.19/) *— 2025 · Proceedings of the First BabyLM Workshop*
55. [Can Large Reasoning Models Self-Train?](https://arxiv.org/abs/2505.21444) *— 2025 · arXiv:2505.21444*
56. [ChemAgent: Self-updating Library in Large Language Models Improves Chemical Reasoning](https://arxiv.org/abs/2501.06590) *— 2025 · arXiv:2501.06590*
57. [Code-Switching Curriculum Learning for Multilingual Transfer in LLMs](https://aclanthology.org/2025.findings-acl.407/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
58. [Courtroom-LLM: A Legal-Inspired Multi-LLM Framework for Resolving Ambiguous Text Classifications](https://aclanthology.org/2025.coling-main.493/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
59. [Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning](https://arxiv.org/abs/2506.06632) *— 2025 · arXiv:2506.06632*
60. [Estimating Machine Translation Difficulty](https://aclanthology.org/2025.findings-emnlp.1317/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
61. [Evaluating and Improving Graph to Text Generation with Large Language Models](https://aclanthology.org/2025.naacl-long.513/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
62. [Influence-driven Curriculum Learning for Pre-training on Limited Data](https://aclanthology.org/2025.babylm-main.26/) *— 2025 · Proceedings of the First BabyLM Workshop*
63. [Investigating the Zone of Proximal Development of Language Models for In-Context Learning](https://aclanthology.org/2025.findings-naacl.362/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
64. [Jr. AI Scientist and Its Risk Report: Autonomous Scientific Exploration from a Baseline Paper](https://arxiv.org/abs/2511.04583) *— 2025 · arXiv:2511.04583*
65. [Language models can learn implicit multi-hop reasoning, but only if they have lots of training data](https://aclanthology.org/2025.emnlp-main.490/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
66. [Learning Like Humans: Advancing LLM Reasoning Capabilities via Adaptive Difficulty Curriculum Learning and Expert-Guided Self-Reformulation](https://aclanthology.org/2025.emnlp-main.336/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
67. [Less is More: Selective Reflection for Compatible and Efficient Knowledge Distillation in Large Language Models](https://arxiv.org/abs/2508.06135) *— 2025 · arXiv:2508.06135*
68. [Masked Diffusion Language Models with Frequency-Informed Training](https://aclanthology.org/2025.babylm-main.38/) *— 2025 · Proceedings of the First BabyLM Workshop*
69. [MrGuard: A Multilingual Reasoning Guardrail for Universal LLM Safety](https://aclanthology.org/2025.emnlp-main.1392/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
70. [NL2Lean: Translating Natural Language into Lean 4 through Multi-Aspect Reinforcement Learning](https://aclanthology.org/2025.emnlp-main.1586/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
71. [PA-RAG: RAG Alignment via Multi-Perspective Preference Optimization](https://aclanthology.org/2025.naacl-long.459/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
72. [Problem-Solving Logic Guided Curriculum In-Context Learning for LLMs Complex Reasoning](https://aclanthology.org/2025.findings-acl.440/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
73. [RAVEN++: Pinpointing Fine-Grained Violations in Advertisement Videos with Active Reinforcement Reasoning](https://aclanthology.org/2025.emnlp-industry.1/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track*
74. [RAVEN: Robust Advertisement Video Violation Temporal Grounding via Reinforcement Reasoning](https://aclanthology.org/2025.acl-industry.3/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)*
75. [Re-Examine Distantly Supervised NER: A New Benchmark and a Simple Approach](https://aclanthology.org/2025.coling-main.727/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
76. [Re-TASK: Revisiting LLM Tasks from Capability, Skill, and Knowledge Perspectives](https://aclanthology.org/2025.findings-acl.254/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
77. [RedStar: Does Scaling Long-CoT Data Unlock Better Slow-Reasoning Systems?](https://arxiv.org/abs/2501.11284) *— 2025 · arXiv:2501.11284*
78. [ReflectionCoder: Learning from Reflection Sequence for Enhanced One-off Code Generation](https://aclanthology.org/2025.acl-long.494/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [Scaling Instruction-Based Video Editing with a High-Quality Synthetic Dataset](https://arxiv.org/abs/2510.15742) *— 2025 · arXiv:2510.15742*
80. [Self-Evolving Curriculum for LLM Reasoning](https://arxiv.org/abs/2505.14970) *— 2025 · arXiv:2505.14970*
81. [Should Code Models Learn Pedagogically? A Preliminary Evaluation of Curriculum Learning for Real-World Software Engineering Tasks](https://arxiv.org/abs/2502.03806) *— 2025 · arXiv:2502.03806*
82. [SynLexLM: Scaling Legal LLMs with Synthetic Data and Curriculum Learning](https://arxiv.org/abs/2504.18762) *— 2025 · arXiv:2504.18762*
83. [Towards Effective and Efficient Continual Pre-training of Large Language Models](https://aclanthology.org/2025.acl-long.289/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
84. [WebCoT: Enhancing Web Agent Reasoning by Reconstructing Chain-of-Thought in Reflection, Branching, and Rollback](https://aclanthology.org/2025.findings-emnlp.276/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
85. [What is the Best Sequence Length for BabyLM?](https://aclanthology.org/2025.babylm-main.10/) *— 2025 · Proceedings of the First BabyLM Workshop*
86. [A Novel Cartography-Based Curriculum Learning Method Applied on RoNLI: The First Romanian Natural Language Inference Corpus](https://aclanthology.org/2024.acl-long.15/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [A surprisal oracle for when every layer counts](https://aclanthology.org/2024.conll-babylm.21/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
88. [Active Learning for Abstractive Text Summarization via LLM-Determined Curriculum and Certainty Gain Maximization](https://aclanthology.org/2024.findings-emnlp.523/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
89. [AutoWebGLM: A Large Language Model-based Web Navigating Agent](https://arxiv.org/abs/2404.03648) *— 2024 · arXiv:2404.03648*
90. [Automatic Pair Construction for Contrastive Post-training](https://aclanthology.org/2024.findings-naacl.11/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
91. [Automatic Quality Estimation for Data Selection and Curriculum Learning](https://aclanthology.org/2024.conll-babylm.18/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
92. [Balanced Data Sampling for Language Model Training with Clustering](https://aclanthology.org/2024.findings-acl.833/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
93. [CEPT: A Contrast-Enhanced Prompt-Tuning Framework for Emotion Recognition in Conversation](https://aclanthology.org/2024.lrec-main.263/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
94. [Choosy Babies Need One Coach: Inducing Mode-Seeking Behavior in BabyLlama with Reverse KL Divergence](https://aclanthology.org/2024.conll-babylm.8/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
95. [CoELM: Construction-Enhanced Language Modeling](https://aclanthology.org/2024.acl-long.542/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
96. [CommonIT: Commonality-Aware Instruction Tuning for Large Language Models via Data Partitions](https://aclanthology.org/2024.emnlp-main.561/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
97. [Conifer: Improving Complex Constrained Instruction-Following Ability of Large Language Models](https://arxiv.org/abs/2404.02823) *— 2024 · arXiv:2404.02823*
98. [CuSINeS: Curriculum-driven Structure Induced Negative Sampling for Statutory Article Retrieval](https://aclanthology.org/2024.lrec-main.381/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
99. [Curriculum Learning for Small Code Language Models](https://arxiv.org/abs/2407.10194) *— 2024 · arXiv:2407.10194*
100. [Data-driven Discovery with Large Generative Models](https://arxiv.org/abs/2402.13610) *— 2024 · arXiv:2402.13610*

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-3/) · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-6/) · [7](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-7/)

- [← previous Who cites «The First Surface Realisation Shared Task: Overview and Evaluation Results»](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/belz-2011-first-surface-realisation-shared-task/)
- [next → Who cites «Curriculum Learning» · page 2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/bengio-2009-curriculum-learning-2/)
- [↑ back to papers/cited-by/](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/)