Computer Science > Machine Learning
arXiv:2410.08201 (cs)
[Submitted on 10 Oct 2024]
Title:Efficient Dictionary Learning with Switch Sparse Autoencoders
View a PDF of the paper titled Efficient Dictionary Learning with Switch Sparse Autoencoders, by Anish Mudide and Joshua Engels and Eric J. Michaud and Max Tegmark and Christian Schroeder de Witt
View PDFHTML (experimental)Abstract:Sparse autoencoders (SAEs) are a recent technique for decomposing neural network activations into human-interpretable features. However, in order for SAEs to identify all features represented in frontier models, it will be necessary to scale them up to very high width, posing a computational challenge. In this work, we introduce Switch Sparse Autoencoders, a novel SAE architecture aimed at reducing the compute cost of training SAEs. Inspired by sparse mixture of experts models, Switch SAEs route activation vectors between smaller "expert" SAEs, enabling SAEs to efficiently scale to many more features. We present experiments comparing Switch SAEs with other SAE architectures, and find that Switch SAEs deliver a substantial Pareto improvement in the reconstruction vs. sparsity frontier for a given fixed training compute budget. We also study the geometry of features across experts, analyze features duplicated across experts, and verify that Switch SAE features are as interpretable as features found by other SAE architectures.
| Comments: | Code available atthis https URL |
| Subjects: | Machine Learning (cs.LG) |
| Cite as: | arXiv:2410.08201 [cs.LG] |
| (orarXiv:2410.08201v1 [cs.LG] for this version) | |
| https://doi.org/10.48550/arXiv.2410.08201 arXiv-issued DOI via DataCite |
Full-text links:
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
- Other Formats
View a PDF of the paper titled Efficient Dictionary Learning with Switch Sparse Autoencoders, by Anish Mudide and Joshua Engels and Eric J. Michaud and Max Tegmark and Christian Schroeder de Witt
References & Citations
Bookmark
Bibliographic and Citation Tools
Bibliographic Explorer(What is the Explorer?)
Connected Papers(What is Connected Papers?)
Litmaps(What is Litmaps?)
scite Smart Citations(What are Smart Citations?)
Code, Data and Media Associated with this Article
alphaXiv(What is alphaXiv?)
CatalyzeX Code Finder for Papers(What is CatalyzeX?)
DagsHub(What is DagsHub?)
Gotit.pub(What is GotitPub?)
Hugging Face(What is Huggingface?)
Papers with Code(What is Papers with Code?)
ScienceCast(What is ScienceCast?)
Demos
Recommenders and Search Tools
Influence Flower(What are Influence Flowers?)
CORE Recommender(What is CORE?)
IArxiv Recommender(What is IArxiv?)
arXivLabs: experimental projects with community collaborators
arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website.
Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them.
Have an idea for a project that will add value for arXiv's community?Learn more about arXivLabs.