Beyond SAEs: Consistent Concept Discovery with Sparse Coding
Replaces the linear SAE encoder with per-sample sparse coding to learn dictionaries that stay consistent across training runs.
jinho-choi
Steffen Schneider
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1 min read
ConceptScope: Characterizing Dataset Bias via Disentangled Visual Concepts
Characterizes dataset bias by disentangling visual concepts learned by sparse autoencoders on vision models.
jinho-choi
CytoSAE: Interpretable Cell Embeddings for Hematology
Sparse autoencoders for interpretable single-cell embeddings in hematology.
muhammed-furkan-dasdelen
Sparse Autoencoders Reveal Selective Remapping of Visual Concepts During Adaptation
Sparse autoencoders expose how visual concepts are selectively remapped when vision models adapt to new domains.
hyesu-lim