199 citations · 202 across the 12 of their papers we have counts for
12 papers
ConceptFlow: Hierarchical and Fine-grained Concept-Based Explanation for Convolutional Neural Networks
Xinyu Mu, Hui Dou, Furao Shen +1
Concept-based interpretability for Convolutional Neural Networks (CNNs) aims to align internal model representations with high-level semantic concepts, but existing approaches larg…
RL-Selector: Reinforcement Learning-Guided Data Selection via Redundancy Assessment
Suorong Yang, Peijia Li, Furao Shen +1
Modern deep architectures often rely on large-scale datasets, but training on these datasets incurs high computational and storage overhead. Real-world datasets often contain subst…
SPAT: Sensitivity-based Multihead-attention Pruning on Time Series Forecasting Models
Suhan Guo, Jiahong Deng, Mengjun Yi +2
Attention-based architectures have achieved superior performance in multivariate time series forecasting but are computationally expensive. Techniques such as patching and adaptive…
Interactive Instance Annotation with Siamese Networks
Xiang Xu, Ruotong Li, Mengjun Yi +3
Annotating instance masks is time-consuming and labor-intensive. A promising solution is to predict contours using a deep learning model and then allow users to refine them. Howeve…
Explaining Model Overfitting in CNNs via GMM Clustering
Hui Dou, Xinyu Mu, Mengjun Yi +3
Convolutional Neural Networks (CNNs) have demonstrated remarkable prowess in the field of computer vision. However, their opaque decision-making processes pose significant challeng…
Estimating the treatment effect over time under general interference through deep learner integrated TMLE
Suhan Guo, Furao Shen, Ni Li
Understanding the effects of quarantine policies in populations with underlying social networks is crucial for public health, yet most causal inference methods fail here due to the…