community
active
leiden_hybrid_concepts
label: sonnet
community:leiden_hybrid_concepts-run2-c46Virtually Planned Decomposition interpretability
VPD as a bottom-up method for identifying real computational structure in neural networks
5 members. Each node is clickable.
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The papers/notes whose extracted claims & findings make up this cluster.
Bridges (6)
Other communities that share members with this one — cross-cutting threads or papers that sit at the seam between two themes.
- Mechanistic interpretability & model evaluation4 shared
- Mechanistic interpretability via parameter decomposition4 shared
- Vector Product Decomposition for neural interpretability3 shared
- Neural network mechanistic interpretability via attribution decomposition1 shared
- Design principles for care-centered systems1 shared
- Precision-weighted hierarchical active inference1 shared
Claims (5)
- Future interpretability techniques will fundamentally resemble VPDPrediction/hypothesis about the direction of the field.
- The ability to make precise edits demonstrates that VPD identifies real computational machineryClaim that editing success validates VPD's decomposition.
- VPD can be arbitrarily applied to any neural network architectureClaim of generality, highlighted as a key strength.
- VPD identifies real, computational structure in neural network parametersCentral claim that VPD successfully uncovers genuine mechanisms.
- VPD is a meaningful step toward bottom-up interpretabilityPositioning of VPD as advancing the paradigm of explaining computation in the model's terms.