2 papers
cs.LG2026
Tree SAE: Learning Hierarchical Feature Structures in Sparse Autoencoders
Tue M. Cao, Hoang X. Nhat, Raed Alharbi +2
Learning hierarchical features in Sparse Autoencoders (SAEs) is essential for capturing the structured nature of real-world data and mitigating issues like feature absorption or sp…
cs.CV2025
NeurFlow: Interpreting Neural Networks through Neuron Groups and Functional Interactions
Tue M. Cao, Nhat X. Hoang, Hieu H. Pham +2
Understanding the inner workings of neural networks is essential for enhancing model performance and interpretability. Current research predominantly focuses on examining the conne…