Learning representations by back-propagating errors View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

1986-10

AUTHORS

David E. Rumelhart, Geoffrey E. Hinton, Ronald J. Williams

ABSTRACT

N/A

PAGES

533-536

Journal

TITLE

Nature

ISSUE

6088

VOLUME

323

Related Patents

  • Method And Device For Detecting Defects Of At Least One Rotary Wing Aircraft Rotor
  • Computer Assisted Methods For Diagnosing Diseases
  • Recognition And Judgement Apparatus Having Various Learning Functions
  • Controller With Neural Network For Estimating Gas Turbine Internal Cycle Parameters
  • Identifying Or Measuring Selected Substances Or Toxins In A Subject Using Resonant Raman Signals
  • System And Method For Ai Controlling Waste-Water Treatment By Neural Network And Back-Propagation Algorithm
  • Neutral Network With Plural Weight Calculation Methods And Variation Of Plural Learning Parameters
  • Systems And Apparatus For Implementing Task-Specific Learning Using Spiking Neurons
  • Learning Student Dnn Via Output Distribution
  • Modulated Stochasticity Spiking Neuron Network Controller Apparatus And Methods
  • Architecture And The Training Method Of A Pa Dpd System With Space Mapping Applied In The Predistorter
  • Computing Numeric Representations Of Words In A High-Dimensional Space
  • Salivary Biomarker For Cancer, Method And Device For Assaying Same, And Method For Determining Salivary Biomarker For Cancer
  • Predicting Likelihoods Of Conditions Being Satisfied Using Recurrent Neural Networks
  • Classifying Data With Deep Learning Neural Records Incrementally Refined Through Expert Input
  • Analyzing Health Events Using Recurrent Neural Networks
  • Apparatus And Method For Partial Evaluation Of Synaptic Updates Based On System Events
  • Degree-Of-Stain Judging Device And Degree-Of-Stain Judging Method
  • Adaptive Critic Apparatus And Methods
  • Computerized Cluster Analysis Framework For Decorrelated Cluster Identification In Datasets
  • Apparatus And Methods For Gating Analog And Spiking Signals In Artificial Neural Networks
  • Predicting Likelihoods Of Conditions Being Satisfied Using Recurrent Neural Networks
  • Ultrasonic Gas Leak Detector With False Alarm Discrimination
  • Neural Network Processing System Using Semiconductor Memories
  • Method And Apparatus For Adjusting Read-Out Conditions And/Or Image Processing Conditions For Radiation Images, Radiation Image Read-Out Apparatus, And Radiation Image Analyzing Method And Apparatus
  • Fraud Detection Using Predictive Modeling
  • Method And Apparatus For Adjusting Read-Out Conditions And/Or Image
  • Method For Determining Attributes Using Neural Network And Fuzzy Logic
  • Proportional-Integral-Derivative Controller Effecting Expansion Kernels Comprising A Plurality Of Spiking Neurons Associated With A Plurality Of Receptive Fields
  • Multi Modality Brain Mapping System (Mbms) Using Artificial Intelligence And Pattern Recognition
  • Flame Detection System
  • Stochastic Artifical Neuron With Multilayer Training Capability
  • Stochastic Apparatus And Methods For Implementing Generalized Learning Rules
  • Multi-Spectral Flame Detector With Radiant Energy Estimation
  • Number Of Clusters Estimation
  • Image-Processing Method
  • System And Method For Addressing Overfitting In A Neural Network
  • Face Identification Method And System Using Thereof
  • Extrapolating Empirical Models For Control, Prediction, And Optimization Applications
  • Apparatus And Methods For Generalized State-Dependent Learning In Spiking Neuron Networks
  • Apparatus And Methods For Reinforcement-Guided Supervised Learning
  • Assessing Blood Brain Barrier Dynamics Or Identifying Or Measuring Selected Substances, Including Ethanol Or Toxins, In A Subject By Analyzing Raman Spectrum Signals
  • Enhanced Fraud Detection With Terminal Transaction-Sequence Processing
  • Information Management Apparatus Dealing With Waste And Waste Recycle Planning Supporting Apparatus
  • Neural Network Processing System Using Semiconductor Memories And Processing Paired Data In Parallel
  • Neural Network Processing System Using Semiconductor Memories
  • Score Based Decisioning
  • Degree-Of-Stain Judging Device And Degree-Of-Stain Judging Method
  • Semiconductor Integrated Circuit Device Comprising A Memory Array And A Processing Circuit
  • Spiking Neuron Classifier Apparatus And Methods Using Conditionally Independent Subsets
  • Nerve Equivalent Circuit, Synapse Equivalent Circuit And Nerve Cell Body Equivalent Circuit
  • Feature-Preserving Noise Removal
  • Neural Net System For Analyzing Chromatographic Peaks
  • Method And Apparatus For Indetification, Forecast, And Control Of A Non-Linear Flow On A Physical System Network Using A Neural Network
  • Online Domain Adaptation For Multi-Object Tracking
  • Computing Numeric Representations Of Words In A High-Dimensional Space
  • Apparatus And Methods For State-Dependent Learning In Spiking Neuron Networks
  • Spiking Neuron Network Adaptive Control Apparatus And Methods
  • Automated Loan Risk Assessment System And Method
  • Generating Author Vectors
  • Method For The Contactless Determination And Processing Of Sleep Movement Data
  • Automated Loan Risk Assessment System And Method
  • Neural Network With Selective Error Reduction To Increase Learning Speed
  • System And Method For Detecting Fraudulent Transactions
  • Computer Assisted Methods For Diagnosing Diseases
  • Neural Network Processing System Using Semiconductor Memories
  • Image-Processing Method
  • Information Recognition System And Control System Using Same
  • Data Processing Circuits In A Neural Network For Processing First Data Stored In Local Register Simultaneous With Second Data From A Memory
  • Pattern Recognition Neural Network
  • Optical Information Processing Apparatus Having A Neural Network For Inducing An Error Signal
  • Degree-Of-Stain Judging Device And Degree-Of-Stain Judging Method
  • System And Method For Estimating Remaining Useful Life
  • Risk Determination And Management Using Predictive Modeling And Transaction Profiles For Individual Transacting Entities
  • Single Layer Neural Network Circuit For Performing Linearly Separable And Non-Linearly Separable Logical Operations
  • Performance Of Artificial Neural Network Models In The Presence Of Instrumental Noise And Measurement Errors
  • Dynamically Reconfigurable Stochastic Learning Apparatus And Methods
  • System And Method For Task-Less Mapping Of Brain Activity
  • Information Recognition System And Control System Using Same
  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/323533a0

    DOI

    http://dx.doi.org/10.1038/323533a0

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1018367015


    Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
    Incoming Citations Browse incoming citations for this publication using opencitations.net

    JSON-LD is the canonical representation for SciGraph data.

    TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

    [
      {
        "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
        "author": [
          {
            "familyName": "Rumelhart", 
            "givenName": "David E.", 
            "id": "sg:person.011313517665.78", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011313517665.78"
            ], 
            "type": "Person"
          }, 
          {
            "familyName": "Hinton", 
            "givenName": "Geoffrey E.", 
            "id": "sg:person.0615147542.17", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0615147542.17"
            ], 
            "type": "Person"
          }, 
          {
            "familyName": "Williams", 
            "givenName": "Ronald J.", 
            "id": "sg:person.016024123731.46", 
            "sameAs": [
              "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016024123731.46"
            ], 
            "type": "Person"
          }
        ], 
        "citation": [
          {
            "id": "https://doi.org/10.21236/ad0256582", 
            "sameAs": [
              "https://app.dimensions.ai/details/publication/pub.1091822818"
            ], 
            "type": "CreativeWork"
          }
        ], 
        "datePublished": "1986-10", 
        "datePublishedReg": "1986-10-01", 
        "genre": "research_article", 
        "id": "sg:pub.10.1038/323533a0", 
        "inLanguage": [
          "en"
        ], 
        "isAccessibleForFree": false, 
        "isPartOf": [
          {
            "id": "sg:journal.1018957", 
            "issn": [
              "0090-0028", 
              "1476-4687"
            ], 
            "name": "Nature", 
            "type": "Periodical"
          }, 
          {
            "issueNumber": "6088", 
            "type": "PublicationIssue"
          }, 
          {
            "type": "PublicationVolume", 
            "volumeNumber": "323"
          }
        ], 
        "name": "Learning representations by back-propagating errors", 
        "pagination": "533-536", 
        "productId": [
          {
            "name": "readcube_id", 
            "type": "PropertyValue", 
            "value": [
              "57831ae6cbf238b5e934c7bcd40fac6cb6c06d9025e9bf47f2ca24fe73cf19e1"
            ]
          }, 
          {
            "name": "doi", 
            "type": "PropertyValue", 
            "value": [
              "10.1038/323533a0"
            ]
          }, 
          {
            "name": "dimensions_id", 
            "type": "PropertyValue", 
            "value": [
              "pub.1018367015"
            ]
          }
        ], 
        "sameAs": [
          "https://doi.org/10.1038/323533a0", 
          "https://app.dimensions.ai/details/publication/pub.1018367015"
        ], 
        "sdDataset": "articles", 
        "sdDatePublished": "2019-04-10T17:19", 
        "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
        "sdPublisher": {
          "name": "Springer Nature - SN SciGraph project", 
          "type": "Organization"
        }, 
        "sdSource": "s3://com-uberresearch-data-dimensions-target-20181106-alternative/cleanup/v134/2549eaecd7973599484d7c17b260dba0a4ecb94b/merge/v9/a6c9fde33151104705d4d7ff012ea9563521a3ce/jats-lookup/v90/0000000001_0000000264/records_8672_00000424.jsonl", 
        "type": "ScholarlyArticle", 
        "url": "http://www.nature.com/nature/journal/v323/n6088/full/323533a0.html"
      }
    ]
     

    Download the RDF metadata as:  json-ld nt turtle xml License info

    HOW TO GET THIS DATA PROGRAMMATICALLY:

    JSON-LD is a popular format for linked data which is fully compatible with JSON.

    curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/323533a0'

    N-Triples is a line-based linked data format ideal for batch operations.

    curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/323533a0'

    Turtle is a human-readable linked data format.

    curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/323533a0'

    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/323533a0'


     

    This table displays all metadata directly associated to this object as RDF triples.

    63 TRIPLES      19 PREDICATES      25 URIs      18 LITERALS      7 BLANK NODES

    Subject Predicate Object
    1 sg:pub.10.1038/323533a0 schema:author Na59258eca0eb48558dcfa6ee328ad0d6
    2 schema:citation https://doi.org/10.21236/ad0256582
    3 schema:datePublished 1986-10
    4 schema:datePublishedReg 1986-10-01
    5 schema:genre research_article
    6 schema:inLanguage en
    7 schema:isAccessibleForFree false
    8 schema:isPartOf N0ed7a35c21d84f16ac436dcb529f5fd0
    9 N43055be8e7854d2bb09cb80b3366106f
    10 sg:journal.1018957
    11 schema:name Learning representations by back-propagating errors
    12 schema:pagination 533-536
    13 schema:productId N660a1df1bf574c5fb5e82274d890c2cc
    14 N693d905324c0447f9425e4f735e97d55
    15 Ncf4c783d47a54344be0c180732c869fb
    16 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018367015
    17 https://doi.org/10.1038/323533a0
    18 schema:sdDatePublished 2019-04-10T17:19
    19 schema:sdLicense https://scigraph.springernature.com/explorer/license/
    20 schema:sdPublisher N6d344ae43cec49aebd52c59ec70401ad
    21 schema:url http://www.nature.com/nature/journal/v323/n6088/full/323533a0.html
    22 sgo:license sg:explorer/license/
    23 sgo:sdDataset articles
    24 rdf:type schema:ScholarlyArticle
    25 N0ed7a35c21d84f16ac436dcb529f5fd0 schema:issueNumber 6088
    26 rdf:type schema:PublicationIssue
    27 N2faa0667a3e3465f879ebb798d9f06a6 rdf:first sg:person.0615147542.17
    28 rdf:rest Na2526dfc1ff546fdb2a39f09c1682a9d
    29 N43055be8e7854d2bb09cb80b3366106f schema:volumeNumber 323
    30 rdf:type schema:PublicationVolume
    31 N660a1df1bf574c5fb5e82274d890c2cc schema:name dimensions_id
    32 schema:value pub.1018367015
    33 rdf:type schema:PropertyValue
    34 N693d905324c0447f9425e4f735e97d55 schema:name doi
    35 schema:value 10.1038/323533a0
    36 rdf:type schema:PropertyValue
    37 N6d344ae43cec49aebd52c59ec70401ad schema:name Springer Nature - SN SciGraph project
    38 rdf:type schema:Organization
    39 Na2526dfc1ff546fdb2a39f09c1682a9d rdf:first sg:person.016024123731.46
    40 rdf:rest rdf:nil
    41 Na59258eca0eb48558dcfa6ee328ad0d6 rdf:first sg:person.011313517665.78
    42 rdf:rest N2faa0667a3e3465f879ebb798d9f06a6
    43 Ncf4c783d47a54344be0c180732c869fb schema:name readcube_id
    44 schema:value 57831ae6cbf238b5e934c7bcd40fac6cb6c06d9025e9bf47f2ca24fe73cf19e1
    45 rdf:type schema:PropertyValue
    46 sg:journal.1018957 schema:issn 0090-0028
    47 1476-4687
    48 schema:name Nature
    49 rdf:type schema:Periodical
    50 sg:person.011313517665.78 schema:familyName Rumelhart
    51 schema:givenName David E.
    52 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011313517665.78
    53 rdf:type schema:Person
    54 sg:person.016024123731.46 schema:familyName Williams
    55 schema:givenName Ronald J.
    56 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016024123731.46
    57 rdf:type schema:Person
    58 sg:person.0615147542.17 schema:familyName Hinton
    59 schema:givenName Geoffrey E.
    60 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0615147542.17
    61 rdf:type schema:Person
    62 https://doi.org/10.21236/ad0256582 schema:sameAs https://app.dimensions.ai/details/publication/pub.1091822818
    63 rdf:type schema:CreativeWork
     




    Preview window. Press ESC to close (or click here)


    ...