97 citations · 312 across the 45 of their papers we have counts for
8 papers · 1 filter
Training-free Heterogeneous Model Merging
Zhengqi Xu, Han Zheng, Jie Song +2
Model merging has attracted significant attention as a powerful paradigm for model reuse, facilitating the integration of task-specific models into a singular, versatile framework…
A Large-scale Universal Evaluation Benchmark For Face Forgery Detection
Yijun Bei, Hengrui Lou, Jinsong Geng +5
With the rapid development of AI-generated content (AIGC) technology, the production of realistic fake facial images and videos that deceive human visual perception has become poss…
A Comprehensive Study of Structural Pruning for Vision Models
Changhao Li, Haoling Li, Mengqi Xue +6
Structural pruning has emerged as a promising approach for producing more efficient models. Nevertheless, the community suffers from a lack of standardized benchmarks and metrics,…
Transmission Interface Power Flow Adjustment: A Deep Reinforcement Learning Approach based on Multi-task Attribution Map
Shunyu Liu, Wei Luo, Yanzhen Zhou +5
Transmission interface power flow adjustment is a critical measure to ensure the security and economy operation of power systems. However, conventional model-based adjustment schem…
On the Concept Trustworthiness in Concept Bottleneck Models
Qihan Huang, Jie Song, Jingwen Hu +3
Concept Bottleneck Models (CBMs), which break down the reasoning process into the input-to-concept mapping and the concept-to-label prediction, have garnered significant attention…
Training-Free Pretrained Model Merging
Zhengqi Xu, Ke Yuan, Huiqiong Wang +3
Recently, model merging techniques have surfaced as a solution to combine multiple single-talent models into a single multi-talent model. However, previous endeavors in this field…
Disentangled Condensation for Large-scale Graphs
Zhenbang Xiao, Yu Wang, Shunyu Liu +4
Graph condensation has emerged as an intriguing technique to save the expensive training costs of Graph Neural Networks (GNNs) by substituting a condensed small graph with the orig…
Powerformer: A Section-adaptive Transformer for Power Flow Adjustment
Kaixuan Chen, Wei Luo, Shunyu Liu +6
In this paper, we present a novel transformer architecture tailored for learning robust power system state representations, which strives to optimize power dispatch for the power f…