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Keep grinding, keep winning
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Keep grinding, keep winning

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Yusen-Peng/README.md

My primary research interests lie in vision-language models. I always investigate different approaches to designing such models with the emphasis on the following three aspects:

  1. Capability: design robust, generalizable vision-language models that are data-adaptive and task-versatile
  2. Efficiency: engineer computational efficient vision-language models with good performance tradeoffs
  3. Interpretability: develop transparent vision-language models that explain their decision-making process

This is my personal website:
https://yusen-peng.github.io

Additionally, for more details about my research, You can find my CV here:
Yusen_Peng_CV.pdf

You can also find my unofficial transcript so far - I have been maintaining 4.0 GPA:
Transcript.pdf

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  1. CE-Bench CE-Bench Public

    [accepted at BlackboxNLP Workshop @ EMNLP, 2025] CE-Bench: A Contrastive Evaluation Benchmark of LLM Interpretability with Sparse Autoencoders

    Jupyter Notebook 3

  2. CascadeFormer CascadeFormer Public

    [arXiv preprint] 🌊CascadeFormer: A Family of Two-stage Cascading Transformers for Skeleton-based Human Action Recognition

    Python 6

  3. DRIP DRIP Public

    DRIP💧: Dynamic token Reduction vIsion transformer via Pooling for efficient multimodal learning

    Python 4

  4. SVD-Pi3 SVD-Pi3 Public

    🔥SVD-π3: Efficient Visual Geometry Learning via Singular Value Decomposition

    Python 1

  5. CascadeFormer-AD-Agent CascadeFormer-AD-Agent Public

    [demo for undergraduate thesis] 🏷️ CascadeFormer-based Agentic Anomaly Detection

    Python 1

  6. Yusen-Peng.github.io Yusen-Peng.github.io Public

    Yusen Peng's academic webisite

    HTML 1