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20242026
most citedAnalysis of Privacy Leakage in Federated Large Language Models

4 citations · 4 across the 9 of their papers we have counts for

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cs.LG2026

Weights Read and Write Features: Scalable Parameter Decomposition Grounded in Activation Space

Tue M. Cao, Lisiane Pruinelli, My T. Thai

Activation space and parameter space provide complementary views of model computation. Activations represent information, while weights read, transform, and write that information.…

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…