A unifying framework from neural superposition to sparse interpretable codes

2026/07/14 by David Klindt, Charles O’Neill, Patrik Reizinger +2 · 2 voices
Computer Science · Neuroscience · #Artificial neural network #Deep neural networks #Embodied and Extended Cognition #Explainable Artificial Intelligence (XAI) #Face Recognition and Perception #Identifiability #Interpretability #Neural coding #Perspective (graphical) #Representation (politics) #Superposition principle

paper · doi:10.1038/s42256-026-01259-z

openalex publication_date 2026/07/14 · openalex created_date 2026/07/15 · openalex updated_date 2026/08/02

Abstract

Nature Machine Intelligence, Published online: 14 July 2026; doi:10.1038/s42256-026-01259-z Kindt et al. present a unifying framework for superposition in neural networks. Their three-step approach clarifies how latent features can be identified, disentangled and assessed.

Citations

• Compressed sensing
• Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures

Discussions

• Read more: www.nature.com/articles/s42... @geometric-intel.bsky.social @neurreps.bsky.social @ai-ucsb.bsky.social @ucsbece.bsky.social @ucsbengineering.bsky.social [bsky, 7 points, 1 comments]
• A unifying framework from neural superposition to sparse interpretable codes ->Nature | More on "Neural networks sparse interpretable codes" at BigEarthData.ai [bsky, 0 points, 0 comments]

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