Papers by Gleb Gerasimov
Train One Sparse Autoencoder Across Multiple Sparsity Budgets to Preserve Interpretability and Accuracy (2025.emnlp-main)
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Nikita Balagansky, Yaroslav Aksenov, Daniil Laptev, Vadim Kurochkin, Gleb Gerasimov, Nikita Koriagin, Daniil Gavrilov
| Challenge: | Sparse Autoencoders (SAEs) are powerful tools for interpreting neural networks . conventional SAEs are constrained by the fixed sparsity level chosen during training . |
| Approach: | They propose a training objective that trains a single SAE to optimise reconstructions across multiple sparsity levels simultaneously. |
| Outcome: | The proposed objective achieves Pareto-optimal trade-offs between sparsity and explained variance, outperforming traditional SAEs trained at individual sparsities. |