--- title: "Who cites Scaling Vision with Sparse Mixture of Experts" description: "103 papers of the Elamur Research library cite «Scaling Vision with Sparse Mixture of Experts», found in their bibliographies; the most cited first." canonical: "https://research.elamur.ai/free/en/papers/riquelme-2021-scaling-vision-sparse-mixture-experts/cited-by/" --- [Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/riquelme-2021-scaling-vision-sparse-mixture-experts/cited-by/) [home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Scaling Vision with Sparse Mixture of Experts](https://md.research.elamur.ai/free/en/papers/riquelme-2021-scaling-vision-sparse-mixture-experts/) / cited by # Who cites «Scaling Vision with Sparse Mixture of Experts» 103 papers of our library cite it (of the 149,439 whose bibliographies we read) · 14 with a page here, the most cited first · [← back to «Scaling Vision with Sparse Mixture of Experts»](https://md.research.elamur.ai/free/en/papers/riquelme-2021-scaling-vision-sparse-mixture-experts/) 1. [**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/) 2. [**Qwen2.5-VL Technical Report arXiv:2502.13923**Shuai Bai, Keqin Chen, Xuejing Liu, et al.*2025 · 1,031 citations*](https://md.research.elamur.ai/free/en/papers/bai-2025-qwen2-5-vl-technical/) 3. [**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/) 4. **[Reproducible scaling laws for contrastive language-image learning](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/) [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/)**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 769 citations* 5. [**PaLI: A Jointly-Scaled Multilingual Language-Image Model arXiv:2209.06794**Xi Chen, Xiao Wang, Soravit Changpinyo, et al.*2022 · 371 citations*](https://md.research.elamur.ai/free/en/papers/chen-2022-pali-jointly-scaled-multilingual-language/) 6. [**Tutel: Adaptive Mixture-of-Experts at Scale MLSys 2023**Changho Hwang, Wei Cui, Yifan Xiong, et al.*2022 · 168 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2022-tutel-adaptive-mixture-experts-scale/) 7. [**Sparse Upcycling: Training Mixture-of-Experts from Dense Checkpoints arXiv:2212.05055**Aran Komatsuzaki, Joan Puigcerver, James Lee-Thorp, et al.*2022 · 98 citations*](https://md.research.elamur.ai/free/en/papers/komatsuzaki-2022-sparse-upcycling-training-mixture-experts/) 8. **[LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://md.research.elamur.ai/free/en/papers/dou-2024-loramoe-alleviating-world-knowledge-forgetting/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Shihan Dou, Enyu Zhou, Yan Liu, et al.*2024 · 93 citations* 9. [**Mixture-of-Experts Meets Instruction Tuning:A Winning Combination for Large Language Models arXiv:2305.14705**Sheng Shen, Le Hou, Yanqi Zhou, et al.*2023 · 70 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-mixture-experts-meets-instruction-tuning/) 10. [**Pushing Mixture of Experts to the Limit: Extremely Parameter Efficient MoE for Instruction Tuning arXiv:2309.05444**Ted Zadouri, Ahmet Ustun, Arash Ahmadian, et al.*2023 · 69 citations*](https://md.research.elamur.ai/free/en/papers/zadouri-2023-pushing-mixture-experts-limit-extremely/) 11. [**OpenMoE: An Early Effort on Open Mixture-of-Experts Language Models arXiv:2402.01739**Fuzhao Xue, Zian Zheng, Yao Fu, et al.*2024 · 64 citations*](https://md.research.elamur.ai/free/en/papers/xue-2024-openmoe-early-effort-open-mixture/) 12. **[Inner Thinking Transformer: Leveraging Dynamic Depth Scaling to Foster Adaptive Internal Thinking](https://md.research.elamur.ai/free/en/papers/chen-2025-inner-thinking-transformer-leveraging-dynamic/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yilong Chen, Junyuan Shang, Zhenyu Zhang, et al.*2025 · 16 citations* 13. **[PROPER: A Progressive Learning Framework for Personalized Large Language Models with Group-Level Adaptation](https://md.research.elamur.ai/free/en/papers/zhang-2025-proper-progressive-learning-framework-personalized/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Linhai Zhang, Jialong Wu, Deyu Zhou, et al.*2025 · 6 citations* 14. **[Expert Calibration Lens for Pruning Mixture of Experts](https://md.research.elamur.ai/free/en/papers/salim-2026-expert-calibration-lens-pruning-mixture/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luis Frentzen Salim, Chia-Chun Wu, Tran Van Nhiem, et al.*2026* 89 more papers of our library cite it; they have no page here yet. - [← back to](https://md.research.elamur.ai/free/en/papers/riquelme-2021-scaling-vision-sparse-mixture-experts/) — Scaling Vision with Sparse Mixture of Experts