VISTA: A Panoramic View of Neural Representations
This addresses the problem of neural network interpretability for researchers and practitioners, though it appears incremental as it builds on existing visualization techniques.
The researchers tackled the challenge of analyzing complex neural network representations by developing VISTA, a pipeline that maps these representations into a semantic 2D space for visual exploration, and demonstrated its utility by applying it to sparse autoencoder latents to uncover new properties.
We present VISTA (Visualization of Internal States and Their Associations), a novel pipeline for visually exploring and interpreting neural network representations. VISTA addresses the challenge of analyzing vast multidimensional spaces in modern machine learning models by mapping representations into a semantic 2D space. The resulting collages visually reveal patterns and relationships within internal representations. We demonstrate VISTA's utility by applying it to sparse autoencoder latents uncovering new properties and interpretations. We review the VISTA methodology, present findings from our case study ( https://got.drib.net/latents/ ), and discuss implications for neural network interpretability across various domains of machine learning.