Sparse Dictionary Learning

Entities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.

• Sparse Feature Dictionaryconcept0.855
  The extracted set of sparse interpretable features from model embeddings via SAEs
• The examples of features found in language models suggest they are highly sparse variables, consistent with dictionary learning being applicablehypothesis0.799
  Motivation for using sparsity-based dictionary learning on language models
• Dictionary Learning for Neural Network Interpretabilitymethod0.769
  Bricken et al.'s method for decomposing language models into interpretable features; cited as AI alignment interpretability relevant to consciousness detection
• Sparse autoencoders are preferable to stronger iterative dictionary learning methods because they cannot recover features the model itself cannot accessclaim0.767
  Rationale for using simpler sparse autoencoders rather than NP-hard compressed sensing algorithms
• Sparse Autoencoderframework0.765
  Interpretability framework used to decompose layer-40 activations into sparse feature sets for studying emotional alignment and persistence
• Learningconcept0.755
  Inference of parameters encoding contingencies of the world (e.g., likelihood matrix A) at slower timescale than perception.
• Sparse and smooth codingconcept0.754
  Coding scheme where qualities are represented by few neurons with continuous similarity relations.
• Learned features reflect the functionality of the model and not just the data distribution, as evidenced by interpretable downstream effects not used in dictionary learningclaim0.748
  Authors argue features are model properties because logit effects and ablations are consistent with feature interpretations