Content deleted Content added
Citation bot (talk | contribs) Removed proxy/dead URL that duplicated identifier. | Use this bot. Report bugs. | #UCB_CommandLine |
m →Implementation: HTTP to HTTPS for SourceForge |
||
| (4 intermediate revisions by 3 users not shown) | |||
Line 12:
==Definition==
Human memory has a tendency to [[Multiple trace theory|congregate memories]] based on similarities between them (although they may not be related), such as "firetrucks are red and apples are red".<ref name=ship>{{cite web|title=General Psychology|url=http://webspace.ship.edu/cgboer/memory.html|publisher=Shippensburg University|author=C. George Boeree|year=2002}}</ref> Sparse distributed memory is a mathematical representation of human memory, and uses [[Clustering high-dimensional data|high-dimensional space]] to help model the large amounts of memory that mimics that of the human neural network.<ref name=psu>{{cite journal|title=Sparse Distributed Memory and Related Models|pages=50–76|citeseerx=10.1.1.2.8403|publisher=Pennsylvania State University|author=Pentti Kanerva|year=1993}}</ref><ref name=stanford>{{
important property of such high dimensional spaces is that two randomly chosen vectors are relatively far away from each other, meaning that they are uncorrelated.<ref name=integerSDM/> SDM can be considered a realization of [[locality-sensitive hashing]].
Line 240:
===Reinforcement learning===
SDMs provide a linear, local [[function approximation]] scheme, designed to work when a very large/high-dimensional input (address) space has to be mapped into a much smaller [[Computer memory|physical memory]]. In general, local architectures, SDMs included, can be subject to the [[curse of dimensionality]], as some target functions may require, in the worst case, an exponential number of local units to be approximated accurately across the entire input space. However, it is widely believed that most [[Decision support system|decision-making systems]] need high accuracy only around low-dimensional [[manifolds]] of the [[state space]], or important state "highways".<ref>Ratitch, Bohdana, Swaminathan Mahadevan, and [[Doina Precup]]. "Sparse distributed memories in reinforcement learning: Case studies." Proc. of the Workshop on Learning and Planning in Markov Processes-Advances and Challenges. 2004.</ref> The work in Ratitch et al.<ref>Ratitch, Bohdana, and Doina Precup. "[http://www.cs.mcgill.ca/~dprecup/temp/ecml2004.pdf Sparse distributed memories for on-line value-based reinforcement learning] {{Webarchive|url=https://web.archive.org/web/20150824061329/http://www.cs.mcgill.ca/~dprecup/temp/ecml2004.pdf |date=2015-08-24 }}." Machine Learning: ECML 2004. Springer Berlin Heidelberg, 2004. 347-358.</ref> combined the SDM memory model with the ideas from [[instance-based learning|memory-based learning]], which provides an approximator that can dynamically adapt its structure and resolution in order to locate regions of the state space that are "more interesting"<ref>Bouchard-Côté, Alexandre. "[https://www.stat.ubc.ca/~bouchard/pub/report-ml.pdf Sparse Memory Structures Detection]." (2004).</ref> and allocate proportionally more memory resources to model them accurately.
===Object indexing in computer vision===
Line 256:
==Related patents==
* Method and apparatus for a sparse distributed memory system US 5113507 A, [[Universities Space Research Association]], 1992<ref>Method and apparatus for a sparse distributed memory system US 5113507 A, by Louis A. Jaeckel, Universities Space Research Association, 1992, URL:
* Method and device for storing and recalling information implementing a kanerva memory system US 5829009 A, [[Texas Instruments]], 1998<ref>Method and device for storing and recalling information implementing a kanerva memory system US 5829009 A, by Gary A. Frazier, Texas Instruments Incorporated, 1998, URL: https://
* Digital memory, Furber, Stephen. US 7512572 B2, 2009<ref>Furber, Stephen B. "Digital memory." U.S. Patent No. 7,512,572. 31 Mar. 2009.___URL: https://
==Implementation==
{{external links|section|date=February 2023}}
* C Binary Vector Symbols (CBVS): includes SDM implementation in [[C (programming language)|C]] as a part of [[vector symbolic architecture]]<ref>{{cite journal|doi=10.1016/j.bica.2014.11.015|title=Vector space architecture for emergent interoperability of systems by learning from demonstration|journal=Biologically Inspired Cognitive Architectures|volume=11|pages=53–64|year=2015|last1=Emruli|first1=Blerim|last2=Sandin|first2=Fredrik|last3=Delsing|first3=Jerker|url=http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-4068 }}</ref> developed by EISLAB at [[Luleå University of Technology]]: http://pendicular.net/cbvs.php {{Webarchive|url=https://web.archive.org/web/20150925123906/http://pendicular.net/cbvs.php |date=2015-09-25 }}<ref>{{cite journal | last1 = Emruli | first1 = Blerim | last2 = Sandin | first2 = Fredrik | year = 2014 | title = Analogical mapping with sparse distributed memory: A simple model that learns to generalize from examples | url = http://urn.kb.se/resolve?urn=urn:nbn:se:ltu:diva-14994| journal = Cognitive Computation | volume = 6 | issue = 1| pages = 74–88 | doi=10.1007/s12559-013-9206-3| s2cid = 12139021 }}</ref>
* CommonSense ToolKit (CSTK) for realtime sensor data processing developed at the [[Lancaster University]] includes implementation of SDM in [[C++]]:
*[[Julia (programming language)|Julia]] implementation by [[Brian Hayes (scientist)|Brian Hayes]]: https://github.com/bit-player/sdm-julia <ref>The Mind Wanders by B. Hayes, 2018. url: http://bit-player.org/2018/the-mind-wanders</ref>
* [[LIDA (cognitive architecture)|Learning Intelligent Distribution Agent (LIDA)]] developed by [[Stan Franklin]]'s lab at the [[University of Memphis]] includes implementation of SDM in [[Java (programming language)|Java]]: http://ccrg.cs.memphis.edu/framework.html
| |||