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Review of algorithms for artificial intelligence on low memory devices

Dr Petar Radanliev, Professor David De Roure
<span title="">2021</span> <i title="Institute of Electrical and Electronics Engineers (IEEE)"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/q7qi7j4ckfac7ehf3mjbso4hne" style="color: black;">IEEE Access</a> </i> &nbsp;
This triggered a rebranding of neural nets into 'deep learning' [30] and new solutions emerged based on training a Restricted Boltzmann Machine (RBM) efficiently [31] .  ...  Concept: Review the basic fundamentals of AIe.g., 'The Boltzmann Machine', 'The Helmholtz Machine', The Elman network -and combine with state-of-the-art deep learning algorithms to discover novel methods  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3101579">doi:10.1109/access.2021.3101579</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/2sgiqisyrzdj7kcoxa6inbxdkq">fatcat:2sgiqisyrzdj7kcoxa6inbxdkq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20210810090618/https://ieeexplore.ieee.org/ielx7/6287639/6514899/09502714.pdf?tp=&amp;arnumber=9502714&amp;isnumber=6514899&amp;ref=" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/de/de/dede7b70cc7b0708da6133aac6eb8ab02c8d54e1.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1109/access.2021.3101579"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> ieee.com </button> </a>

Modular Simulation Framework for Process Variation Analysis of MRAM-based Deep Belief Networks [article]

Paul Wood, Hossein Pourmeidani, Ronald F. DeMara
<span title="2020-02-03">2020</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
Boltzmann Machines (RBMs).  ...  Magnetic Random-Access Memory (MRAM) based p-bit neuromorphic computing devices are garnering increasing interest as a means to compactly and efficiently realize machine learning operations in Restricted  ...  ACKNOWLEDGMENTS This work was supported in part by the Center for Probabilistic Spin Logic for Low-Energy Boolean and Non-Boolean Computing (CAPSL), one of the Nanoelectronic Computing Research (nCORE)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.00897v1">arXiv:2002.00897v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/va4nhhq6wff2zcixgqlsn7cfvq">fatcat:va4nhhq6wff2zcixgqlsn7cfvq</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200321152614/https://arxiv.org/ftp/arxiv/papers/2002/2002.00897.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/2002.00897v1" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

A Study of Complex Deep Learning Networks on High Performance, Neuromorphic, and Quantum Computers [article]

Thomas E. Potok, Catherine Schuman, Steven R. Young, Robert M. Patton, Federico Spedalieri, Jeremy Liu, Ke-Thia Yao, Garrett Rose, Gangotree Chakma
<span title="2017-07-13">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
to automatically determine network topology, and neuromorphic computing for a low-power hardware implementation.  ...  achieve optimal results, and 3) the implementation of neuron model is expensive in both cost and power.  ...  Department of Energy  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1703.05364v2">arXiv:1703.05364v2</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/s7ltbnkktnd7jovpjgpkfdozkm">fatcat:s7ltbnkktnd7jovpjgpkfdozkm</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200829162702/https://arxiv.org/pdf/1703.05364v2.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/2a/19/2a199c6c8eb06fcca383715d3c965fb89cf62e10.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1703.05364v2" title="arxiv.org access"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> arxiv.org </button> </a>

Research topic: neuromorphic engineering systems and applications. A snapshot of neuromorphic systems engineering

Tobi Delbruck, André van Schaik, Jennifer Hasler
<span title="2014-12-19">2014</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
They demonstrate that a DBN constructed from stacks of restricted Boltzmann machines (RBMs) is valuable for learning and computing sensor fusion.  ...  Two papers are on the hot topic (based on largest number of views) of event-driven computation in deep belief networks (DBNs) (O'Connor et al., 2013; Neftci et al., 2014) .  ...  and no conflict of interest exists.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2014.00424">doi:10.3389/fnins.2014.00424</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/25565952">pmid:25565952</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC4271593/">pmcid:PMC4271593</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/f4sgfccrnjgglcna4zn3thgs6e">fatcat:f4sgfccrnjgglcna4zn3thgs6e</a> </span>
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A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition

Charlyn Pushpa Latha, Mohana Priya
<span title="2016-11-01">2016</span> <i title="Institute of Advanced Engineering and Science"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hdpiucmugffofjsjdrgay6346e" style="color: black;">APTIKOM Journal on Computer Science and Information Technologies</a> </i> &nbsp;
Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), Deep Belief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively.  ...  It is also known as deep structured learning, hierarchical learning or deep machine learning. The term "deep learning" indicates the method used in training multi-layered neural networks.  ...  Deep Belief Networks (DBN) Deep belief networks [2] are highly complex directed acyclic graph, which are formed by a sequence of restricted Boltzmann Machine (RBM) architectures.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/aptikom.j.csit.118">doi:10.11591/aptikom.j.csit.118</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/gerpzx54qrgrtf3pqnnzgjywim">fatcat:gerpzx54qrgrtf3pqnnzgjywim</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200307011302/http://aptikomjournal.com/index.php/CSIT/article/download/118/52" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cf/e4/cfe4e2e4ce5eb80e69c430e49d613286eb397b28.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.11591/aptikom.j.csit.118"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

A Review on Deep Learning Algorithms for Speech and Facial Emotion Recognition

Charlyn Pushpa Latha, Mohana Priya
<span title="2020-01-17">2020</span> <i title="Pandawan"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/hdpiucmugffofjsjdrgay6346e" style="color: black;">APTIKOM Journal on Computer Science and Information Technologies</a> </i> &nbsp;
Some of the deep learning techniques discussed in this paper are Deep Boltzmann Machine (DBM), DeepBelief Networks (DBN), Convolutional Neural Networks (CNN) and Stacked Auto Encoders respectively.  ...  It is also known as deep structured learning,hierarchical learning or deep machine learning. The term "deep learning" indicates the method used in trainingmulti-layered neural networks.  ...  Deep Belief Networks (DBN) Deep belief networks [2] are highly complex directed acyclic graph, which are formed by a sequence of restricted Boltzmann Machine (RBM) architectures.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.34306/csit.v1i3.55">doi:10.34306/csit.v1i3.55</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/l2tska7j5ferna4wupt3f2jcp4">fatcat:l2tska7j5ferna4wupt3f2jcp4</a> </span>
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Adiabatic Quantum Computation Applied to Deep Learning Networks

Jeremy Liu, Federico Spedalieri, Ke-Thia Yao, Thomas Potok, Catherine Schuman, Steven Young, Robert Patton, Garrett Rose, Gangotree Chamka
<span title="2018-05-18">2018</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4d3elkqvznfzho6ki7a35bt47u" style="color: black;">Entropy</a> </i> &nbsp;
We illustrate a particular network topology that can be trained to classify MNIST data (an image dataset of handwritten digits) and neutrino detection data using a restricted form of adiabatic quantum  ...  for training deep learning networks.  ...  , such as spiking Hopfield networks and spiking restricted Boltzmann machines [43] .  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e20050380">doi:10.3390/e20050380</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/33265470">pmid:33265470</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/lo6zfpwuhfgqtplrxrz76kjcg4">fatcat:lo6zfpwuhfgqtplrxrz76kjcg4</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190224032306/http://pdfs.semanticscholar.org/51d3/747b941be86f6e9b4fa27cc682e2a1a0d559.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/51/d3/51d3747b941be86f6e9b4fa27cc682e2a1a0d559.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3390/e20050380"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> mdpi.com </button> </a>

The Latest Progress of Deep Learning
深度学习的最新进展

一鸣 陈
<span title="">2018</span> <i title="Hans Publishers"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/az52o5fkdnbcxn2sr2gndxelp4" style="color: black;">Computer Science and Application</a> </i> &nbsp;
This paper introduces the related concepts of deep learning, concise summary of the relevant work based on the relevant academic achievements obtained from domestic and overseas searches, 19 classic papers  ...  and the latest three books on the depth of the past three years.  ...  在 Acoustic modeling using deep belief networks [6在 Efficient learning of sparse representations with an energy-based model [在 Real-time classification and sensor fusion with a spiking deep belief network  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12677/csa.2018.84064">doi:10.12677/csa.2018.84064</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/hyw53az2sjbkhn3tivhseptqua">fatcat:hyw53az2sjbkhn3tivhseptqua</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20220309183810/https://www.hanspub.org/journal/PaperDownload.aspx?DOI=10.12677/CSA.2018.84064" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/f9/b1/f9b19882a10f5040f1f7e9aa01f1db80cc7d41bb.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.12677/csa.2018.84064"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="external alternate icon"></i> Publisher / doi.org </button> </a>

Understanding a Deep Learning Technique through a Neuromorphic System a Case Study with SpiNNaker Neuromorphic Platform

Indar Sugiarto, Felix Pasila, R.H. Setyobudi, E. Alasaarela, F. Pasila, G. Chan, S.-G. Lee
<span title="">2018</span> <i title="EDP Sciences"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/4vlgvitw6fcmbay5hkyo2s2ime" style="color: black;">MATEC Web of Conferences</a> </i> &nbsp;
Deep learning (DL) has been considered as a breakthrough technique in the field of artificial intelligence and machine learning.  ...  Some DL architectures such as deep neural networks, deep belief networks and recurrent neural networks have been developed and applied to many fields with incredible results, even comparable to human intelligence  ...  study and the discussion with the SpiNNaker team as well as with the experts in the field of neuromorphic technology.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/matecconf/201816401015">doi:10.1051/matecconf/201816401015</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/yenldx2mrrdzhhpcipuntbhl5y">fatcat:yenldx2mrrdzhhpcipuntbhl5y</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20180724034829/https://www.matec-conferences.org/articles/matecconf/pdf/2018/23/matecconf_icesti2018_01015.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/5b/ee/5beec5e8e69b14d8cb364a63bcbb440ba53c80c9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1051/matecconf/201816401015"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> Publisher / doi.org </button> </a>

Using Memristors for Robust Local Learning of Hardware Restricted Boltzmann Machines

Maxence Ernoult, Julie Grollier, Damien Querlioz
<span title="2019-02-12">2019</span> <i title="Springer Nature"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/tnqhc2x2aneavcd3gx5h7mswhm" style="color: black;">Scientific Reports</a> </i> &nbsp;
Restricted Boltzmann Machines (RBM), for their local learning rule and inherent tolerance to stochasticity, comply with both of these constraints and constitute a highly attractive algorithm towards achieving  ...  One of the biggest stakes in nanoelectronics today is to meet the needs of Artificial Intelligence by designing hardware neural networks which, by fusing computation and memory, process and learn from  ...  Acknowledgements This work was supported by the European Research Council Starting Grant NANOINFER (reference: 715872) and the Agence Nationale de la Recherche Grant MIRA (reference: ANR-15-CE24-023).  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1038/s41598-018-38181-3">doi:10.1038/s41598-018-38181-3</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30755662">pmid:30755662</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6372620/">pmcid:PMC6372620</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/tmgmval4ira2ldq5jkgru2vfci">fatcat:tmgmval4ira2ldq5jkgru2vfci</a> </span>
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Event-driven contrastive divergence for spiking neuromorphic systems

Emre Neftci, Srinjoy Das, Bruno Pedroni, Kenneth Kreutz-Delgado, Gert Cauwenberghs
<span title="">2014</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification  ...  Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time  ...  ACKNOWLEDGMENTS This work was partially funded by the National Science Foundation (NSF EFRI-1137279, CCF-1317560), the Office of Frontiers in Neuroscience | Neuromorphic Engineering January 2014 | Volume  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2013.00272">doi:10.3389/fnins.2013.00272</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/24574952">pmid:24574952</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC3922083/">pmcid:PMC3922083</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/erazltqd55ghffe2ge4db4ynny">fatcat:erazltqd55ghffe2ge4db4ynny</a> </span>
<a target="_blank" rel="noopener" href="https://web.archive.org/web/20190427053044/https://cloudfront.escholarship.org/dist/prd/content/qt30h9q6bf/qt30h9q6bf.pdf?t=olulzb" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/4e/f4/4ef4ab1766614b8a605cb3308a07bc68ac3b03c4.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2013.00272"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3922083" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

A Survey of Neuromorphic Computing and Neural Networks in Hardware [article]

Catherine D. Schuman, Thomas E. Potok, Robert M. Patton, J. Douglas Birdwell, Mark E. Dean, Garrett S. Rose, James S. Plank
<span title="2017-05-19">2017</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
The promise of the technology is to create a brain-like ability to learn and adapt, but the technical challenges are significant, starting with an accurate neuroscience model of how the brain works, to  ...  We begin with a 35-year review of the motivations and drivers of neuromorphic computing, then look at the major research areas of the field, which we define as neuro-inspired models, algorithms and learning  ...  An extremely common use of both neural networks and neuromorphic implementations has been on various image-based applications, including edge detection [220] , [339] , [520] , [783] , [829] , [922  ... 
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SNRA: A Spintronic Neuromorphic Reconfigurable Array for In-Circuit Training and Evaluation of Deep Belief Networks [article]

Ramtin Zand, Ronald F. DeMara
<span title="2019-01-08">2019</span> <i > arXiv </i> &nbsp; <span class="release-stage" >pre-print</span>
evaluation phases of restricted Boltzmann machines (RBMs).  ...  First, probabilistic spin logic devices are used to develop an RBM realization which is adapted to construct deep belief networks (DBNs) having one to three hidden layers of size 10 to 800 neurons each  ...  ACKNOWLEDGMENT This work was supported in part by the Center for Probabilistic Spin Logic for Low-Energy Boolean and Non-Boolean Computing (CAPSL), one of the Nanoelectronic Computing Research (nCORE)  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener" href="https://arxiv.org/abs/1901.02415v1">arXiv:1901.02415v1</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/eibgoznd4bfrvag3cbjlg2hnpu">fatcat:eibgoznd4bfrvag3cbjlg2hnpu</a> </span>
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Real-time classification and sensor fusion with a spiking deep belief network

Peter O'Connor, Daniel Neil, Shih-Chii Liu, Tobi Delbruck, Michael Pfeiffer
<span title="">2013</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems.  ...  Deep Belief Networks (DBNs) have recently shown impressive performance on a broad range of classification problems.  ...  Instead of speculating on Moore's FIGURE 1 | Boltzmann and Restricted Boltzmann Machines.  ... 
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<a target="_blank" rel="noopener" href="https://web.archive.org/web/20200219165417/https://www.zora.uzh.ch/id/eprint/91175/1/OConnor_et_al_Real-time_classification.pdf" title="fulltext PDF download" data-goatcounter-click="serp-fulltext" data-goatcounter-title="serp-fulltext"> <button class="ui simple right pointing dropdown compact black labeled icon button serp-button"> <i class="icon ia-icon"></i> Web Archive [PDF] <div class="menu fulltext-thumbnail"> <img src="https://blobs.fatcat.wiki/thumbnail/pdf/cb/2e/cb2e960df6d7654c43e6fd2ab8febeffee00f4d9.180px.jpg" alt="fulltext thumbnail" loading="lazy"> </div> </button> </a> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2013.00178"> <button class="ui left aligned compact blue labeled icon button serp-button"> <i class="unlock alternate icon" style="background-color: #fb971f;"></i> frontiersin.org </button> </a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3792559" title="pubmed link"> <button class="ui compact blue labeled icon button serp-button"> <i class="file alternate outline icon"></i> pubmed.gov </button> </a>

Deep Learning With Spiking Neurons: Opportunities and Challenges

Michael Pfeiffer, Thomas Pfeil
<span title="2018-10-25">2018</span> <i title="Frontiers Media SA"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/wrk3kouosrhcxiprcbguskdipu" style="color: black;">Frontiers in Neuroscience</a> </i> &nbsp;
This makes them interesting candidates for the efficient implementation of deep neural networks, the method of choice for many machine learning tasks.  ...  Neuromorphic approaches and conventional machine learning should not be considered simply two solutions to the same classes of problems, instead it is possible to identify and exploit their task-specific  ...  AUTHOR CONTRIBUTIONS All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.  ... 
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.3389/fnins.2018.00774">doi:10.3389/fnins.2018.00774</a> <a target="_blank" rel="external noopener" href="https://www.ncbi.nlm.nih.gov/pubmed/30410432">pmid:30410432</a> <a target="_blank" rel="external noopener" href="https://pubmed.ncbi.nlm.nih.gov/PMC6209684/">pmcid:PMC6209684</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/flcvj3c4tvfibhn2du3y6t3jvq">fatcat:flcvj3c4tvfibhn2du3y6t3jvq</a> </span>
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