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Researcher profile

Shuicheng Yan

6 papers in the PaperMetrix corpus

Publications

Papers by this author

  1. Mugs: A Multi-Granular Self-Supervised Learning Framework

    2022 · arXiv (Cornell University)

    In self-supervised learning, multi-granular features are heavily desired though rarely investigated, as different downstream tasks (e.g., general and fine-grained classification) often require different or multi-granular features, e.g.~fine- or coarse-grained one or their mixture. In this …

  2. Bag of Tricks for Training Data Extraction from Language Models

    2023 · arXiv (Cornell University)

    With the advance of language models, privacy protection is receiving more attention. Training data extraction is therefore of great importance, as it can serve as a potential tool to assess privacy leakage. However, due to …

  3. Group $K$-Means

    2015 · arXiv (Cornell University)

    We study how to learn multiple dictionaries from a dataset, and approximate any data point by the sum of the codewords each chosen from the corresponding dictionary. Although theoretically low approximation errors can be achieved …

  4. Human-Object Interaction Detection Collaborated with Large Relation-driven Diffusion Models

    2024 · arXiv (Cornell University)

    Prevalent human-object interaction (HOI) detection approaches typically leverage large-scale visual-linguistic models to help recognize events involving humans and objects. Though promising, models trained via contrastive learning on text-image pairs often neglect mid/low-level visual cues and …

  5. Dropping Experts, Recombining Neurons: Retraining-Free Pruning for Sparse Mixture-of-Experts LLMs

    2025 · arXiv (Cornell University)

    Sparse Mixture-of-Experts (SMoE) architectures are widely used in large language models (LLMs) due to their computational efficiency. However, though only a few experts are activated for each token, SMoE still requires loading all expert parameters, …

  6. ConvBERT: Improving BERT with Span-based Dynamic Convolution

    2020 · arXiv (Cornell University)

    Pre-trained language models like BERT and its variants have recently achieved impressive performance in various natural language understanding tasks. However, BERT heavily relies on the global self-attention block and thus suffers large memory footprint and …