I am Huyu Wu(伍胡宇), a graduate student at the Institute of Computing Technology, Chinese Academy of Sciences (ICT). I’m currently interning on the Search Algorithms team at Xiaohongshu (RED) and am open to connecting and collaboration. I received my bachelor’s degree in Artificial Intelligence from the School of Computer Science.

My research interests lie in the MLLM and Dataset Distillation. You can find my main research interests on

🔥 News

  • 2025.10:  🎉🎉 One paper is accepted by Transactions on Machine Learning Research.
  • 2025.09:  🎉🎉 One paper is accepted by Neural Networks.
  • 2024.10:  🎉🎉 One paper is accepted by Applied Soft Computing.
  • 2024.06:  🎉🎉 One paper is accepted by Expert Systems with Applications.
  • 2024.03:  🎉🎉 One paper is accepted by IJCNN 2024.

📝 Publications

  • Wu H, Su D, Hou J, Li G. Dataset Condensation with Color Compensation[J]. Transactions on Machine Learning Research, 2025. (First Author)
  • Wu H, Jia B, Yuan X M. LLM-Led Vision-Spectral Fusion: A Zero-Shot Approach to Temporal Fruit Image Classification[J]. Neural Networks, 2025: 108155. (First Author, CCF-B)
  • Jia B, Guo Z, Huang T, Guo F, & Wu H. A generalized Lorenz system-based initialization method for deep neural networks[J]. Applied Soft Computing, 2024, 167: 112316. (Last Author, 中科院一区TOP)
  • Jia B, Wu H, Guo K. Chaos theory meets deep learning: A new approach to time series forecasting[J]. Expert Systems with Applications, 2024, 255: 124533. (Co-First Author, 中科院一区TOP)
  • Wu H, Jia B, Sheng G. Early-Late Dropout for DivideMix: Learning with Noisy Labels in Deep Neural Networks[C]//2024 International Joint Conference on Neural Networks (IJCNN). IEEE, 2024: 1-8. (First Author, CCF-C)

📖 Educations

  • 2025.09 – Present, M.S., Institute of Computing Technology, University of Chinese Academy of Sciences
  • 2021.09 – 2025.06, B.S., College of Computer Science, Sichuan University
  • 2020.09 – 2021.06, Business School, Business Administration, Sichuan University

💻 Internships

📙 Projects

2025.03 - 2025.09
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Diffusion Models As Dataset Distillation Priors
Dataset distillation seeks to create compact yet informative datasets, but balancing diversity, generalization, and representativeness remains challenging. We propose Diffusion As Priors (DAP), which leverages the inherent representativeness prior in diffusion models by quantifying synthetic–real data similarity in feature space with a Mercer kernel and guiding the reverse diffusion process without retraining.

2025.05 - 2025.08
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When Language Overrules: Revealing Text Dominance in Multimodal Large Language Models
Multimodal Large Language Models (MLLMs) achieve strong results but suffer from text dominance, where textual inputs outweigh other modalities. We present the first systematic study of this issue across images, videos, audio, time-series, and graphs, introducing two metrics—the Modality Dominance Index (MDI) and Attention Efficiency Index (AEI)—to quantify the imbalance. Our analysis reveals its pervasive nature and key causes, and we propose a simple token compression method that rebalances attention, reducing LLaVA-7B’s MDI from 10.23 to 0.86. This work offers insights and methods for building more balanced multimodal models.

2018.09 - 2019.09
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VEX Robotics Competition
Led the school’s VEX Robotics club, responsible for programming and debugging of the robotic systems.
Attained Gold Awards at China Zone Selections, the Asia Championships, Asia Open and the World Championships in the United States during the 2018 season.

🎖 Honors and Awards

  • 2024.10 Huang Qianheng Scholarship
  • 2023.06 Champion, 3rd Youth Campus Volleyball League of Sichuan Province.
  • 2022.11 Second Prize, Asia-Pacific Undergraduate Mathematical Contest in Modeling.
  • 2022.06 Third Prize, 2th Youth Campus Volleyball League of Sichuan Province.

📚 Academic Services

  • Conference Reviewer: IJCNN 2024, ACL 2024, CaLM @NeurIPS 2024, ICLR 2025, AAAI 2026

  • Journal Reviewer: IEEE Transactions on Neural Networks and Learning Systems(IEEE TNNLS)

🌏 Visitor Map