Increasing the semantic storage density of sparse distributed memory

DOI10.1007/s10559-022-00465-yOpenAlexW4294307921MaRDI QIDQ2103732

R. Vdovychenko, V. G. Tul'chinskij

Publication date: 9 December 2022

Published in: Cybernetics and Systems Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s10559-022-00465-y

zbMATH Keywords

neural networksassociative memoryGPUcompressive sensing (CS)binary sparse distributed representationssparse distributed memory (SDM)

Mathematics Subject Classification ID

Artificial intelligence (68Txx) Theory of data (68Pxx) Mathematical biology in general (92Bxx)

Uses Software

• SciPy
• CoSaMP
• Python
• GitHub

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• CoSaMP: Iterative signal recovery from incomplete and inaccurate samples
• A simple proof of the restricted isometry property for random matrices
• Tensor product variable binding and the representation of symbolic structures in connectionist systems
• Binding and Normalization of Binary Sparse Distributed Representations by Context-Dependent Thinning
• Matching pursuits with time-frequency dictionaries
• Neural networks and physical systems with emergent collective computational abilities.
• Stable signal recovery from incomplete and inaccurate measurements

This page was built for publication: Increasing the semantic storage density of sparse distributed memory