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A unifying framework from neural superposition to sparse interpretable codes - Nature
A unifying framework from neural superposition to sparse interpretable codes Nature
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- PossiblePossibly related (embedding) · 52%When Structured Sparse Autoencoders Learn Consistent Concepts Across Modalities →
- PossiblePossibly related (embedding) · 51%Requential Coding: Pushing the Limits of Model Compression with Self-Generated Training Data →
- PossiblePossibly related (embedding) · 48%The Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training Methodology →
- PossiblePossibly related (embedding) · 48%Cross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse Autoencoders →
- PossiblePossibly related (embedding) · 47%NeuralInverse/neuralinverse →
- PossiblePossibly related (embedding) · 49%Leveraging unlabelled data for generalizable neural population decoding →
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paperWhen Structured Sparse Autoencoders Learn Consistent Concepts Across ModalitiespaperRequential Coding: Pushing the Limits of Model Compression with Self-Generated Training DatapaperThe Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training MethodologypaperCross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse AutoencodersrepoNeuralInverse/neuralinverse
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paperWhen Structured Sparse Autoencoders Learn Consistent Concepts Across ModalitiespaperCross-seed explainability using Procrustes-conditioned Joint End-to-end Top-K Sparse AutoencodersrepoNeuralInverse/neuralinversepaperRequential Coding: Pushing the Limits of Model Compression with Self-Generated Training DatapaperThe Model Organism Lottery: Model Organism Interpretability Strongly Depends on Training MethodologypaperLeveraging unlabelled data for generalizable neural population decoding