1 citations · 1 across the 2 of their papers we have counts for
7 papers
Enhancing Pre-trained Representation Classifiability can Boost its Interpretability
Shufan Shen, Zhaobo Qi, Junshu Sun +3
The visual representation of a pre-trained model prioritizes the classifiability on downstream tasks, while the widespread applications for pre-trained visual models have posed new…
Kernelized Sparse Fine-Tuning with Bi-level Parameter Competition for Vision Models
Shufan Shen, Junshu Sun, Shuhui Wang +1
Parameter-efficient fine-tuning (PEFT) aims to adapt pre-trained vision models to downstream tasks. Among PEFT paradigms, sparse tuning achieves remarkable performance by adjusting…
Edit Less, Achieve More: Dynamic Sparse Neuron Masking for Lifelong Knowledge Editing in LLMs
Jinzhe Liu, Junshu Sun, Shufan Shen +2
Lifelong knowledge editing enables continuous, precise updates to outdated knowledge in large language models (LLMs) without computationally expensive full retraining. However, exi…
VL-SAE: Interpreting and Enhancing Vision-Language Alignment with a Unified Concept Set
Shufan Shen, Junshu Sun, Qingming Huang +1
The alignment of vision-language representations endows current Vision-Language Models (VLMs) with strong multi-modal reasoning capabilities. However, the interpretability of the a…
Relieving the Over-Aggregating Effect in Graph Transformers
Junshu Sun, Wanxing Chang, Chenxue Yang +2
Graph attention has demonstrated superior performance in graph learning tasks. However, learning from global interactions can be challenging due to the large number of nodes. In th…
Towards Dynamic Message Passing on Graphs
Junshu Sun, Chenxue Yang, Xiangyang Ji +2
Message passing plays a vital role in graph neural networks (GNNs) for effective feature learning. However, the over-reliance on input topology diminishes the efficacy of message p…