arXiv AI
Oct 15

On the Sparsity-Storage-Accuracy Tradeoff in Parsimoniously Activated Dictionary Learning

The paper revisits parsimoniously activated dictionary learning (PADL), a method that imposes a global regularization on the number of activated dictionary atoms. It shows that PADL can be reformulated as a maximum a posteriori estimation under a structured generative model, enabling derivation of generalization guarantees and an analytical tradeoff between sparsity, storage cost, and reconstruction accuracy. Leveraging this insight, the authors propose an efficient PADL algorithm that removes manual hyperparameter tuning, improves reconstruction performance on visual benchmarks, and accelerates inference for vision‑language models.

By Zihui Zhao, Yuanbo Tang, Yang Li
arXiv AI
Oct 19

Beyond SAEs: Consistent Concept Discovery with Sparse Coding

The paper introduces Consistent Sparse Dictionary Learning (ConSDL), a method that replaces the linear encoder in sparse autoencoders with per‑sample sparse coding to improve dictionary learning. Synthetic experiments show ConSDL achieves near‑perfect recovery of dictionaries and coefficients, and in real vision and language tasks it boosts cross‑seed consistency by 17.6% for dictionaries and 35.9% for coefficients compared to six SAE baselines.

By Jinho Choi, Hyesu Lim, Jaegul Choo, Steffen Schneider