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cs.LG2025
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…
cs.LG2025
RAM: Replace Attention with MLP for Efficient Multivariate Time Series Forecasting
Suhan Guo, Jiahong Deng, Yi Wei +3
Attention-based architectures have become ubiquitous in time series forecasting tasks, including spatio-temporal (STF) and long-term time series forecasting (LTSF). Yet, our unders…
cs.LG2024
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…