PCA Visualization

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

• Principal components analysis (PCA)method0.831
  Statistical method used to analyze neural activity data.
• Feature Visualizationmethod0.801
  Method of optimizing input to cause a neuron to fire maximally, used to characterize what a neuron detects; establishes causal link
• PCA of Emotion Feature Activationsmethod0.777
  PCA on 171 emotion probe activations across all tokens to produce ordered linear combinations and test if lower PCs are more persistent
• PCA is the appropriate dimensionality reduction technique for constructing the RN because it preserves global structure and provides deterministic, interpretable projections.claim0.767
  Justifies PCA choice over UMAP or t-SNE for the node-structured RN model.
• PCA visualizations of LLaMA-2-13B and 70B representations of curated datasets show clear linear structure, with true statements separating from false ones in the top two principal componentsfinding0.762
  Primary visual evidence for linear truth representations in large LLMs
• Mind's eye visualizationmethod0.755
  Technique of building a fluid, three-dimensional vision by closing one's eyes, relying on words and feeling to avoid arbitrary graphical over-specification.
• Interactive Circuit Visualizationmethod0.755
  Interactive tool for visualizing and inspecting learned binary logic circuits using modified DigitalJS library
• NCA for Image Segmentationconcept0.739
  One of several applications of NCA cited to show breadth of the NCA framework