1009:
1967:
1264:
with a certain structure: that of a linear chain. Whereas recursive neural networks operate on any hierarchical structure, combining child representations into parent representations, recurrent neural networks operate on the linear progression of time, combining the previous time step and a hidden
1168:
This architecture, with a few improvements, has been used for successfully parsing natural scenes, syntactic parsing of natural language sentences, and recursive autoencoding and generative modeling of 3D shape structures in the form of cuboid abstractions.
873:
1128:
911:
868:
858:
699:
1163:
1538:
Bianucci, Anna Maria; Micheli, Alessio; Sperduti, Alessandro; Starita, Antonina (2000). "Application of
Cascade Correlation Networks for Structures to Chemistry".
906:
1687:
Hammer, Barbara; Micheli, Alessio; Sperduti, Alessandro; Strickert, Marc (2004-03-01). "A general framework for unsupervised processing of structured data".
863:
714:
1016:
In the most simple architecture, nodes are combined into parents using a weight matrix that is shared across the whole network, and a non-linearity such as
445:
946:
749:
2008:
1773:
Hammer, Barbara; Micheli, Alessio; Sperduti, Alessandro (2005-05-01). "Universal
Approximation Capability of Cascade Correlation for Structures".
825:
374:
2032:
1341:
979:. Recursive neural networks, sometimes abbreviated as RvNNs, have been successful, for instance, in learning sequence and tree structures in
883:
646:
181:
2027:
901:
1177:
RecCC is a constructive neural network approach to deal with tree domains with pioneering applications to chemistry and extension to
734:
709:
658:
1757:
1219:
782:
777:
430:
440:
78:
1581:
Micheli, A.; Sona, D.; Sperduti, A. (2004-11-01). "Contextual processing of structured data by recursive cascade correlation".
835:
1642:
Hammer, Barbara; Micheli, Alessio; Sperduti, Alessandro; Strickert, Marc (2004). "Recursive self-organizing network models".
939:
599:
420:
1314:
Goller, C.; KĂĽchler, A. (1996). "Learning task-dependent distributed representations by backpropagation through structure".
1724:
Socher, Richard; Perelygin, Alex; Y. Wu, Jean; Chuang, Jason; D. Manning, Christopher; Y. Ng, Andrew; Potts, Christopher.
810:
512:
288:
1294:
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1223:
767:
704:
614:
592:
435:
425:
2001:
1215:
980:
918:
830:
815:
276:
98:
1411:
Frasconi, P.; Gori, M.; Sperduti, A. (1998-09-01). "A general framework for adaptive processing of data structures".
805:
988:
878:
555:
450:
238:
171:
131:
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1261:
1227:
932:
538:
306:
176:
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An efficient approach to implement recursive neural networks is given by the Tree Echo State
Network within the
1257:
1252:
560:
480:
403:
321:
151:
113:
108:
68:
63:
20:
1974:
507:
356:
256:
83:
1368:
Sperduti, A.; Starita, A. (1997-05-01). "Supervised neural networks for the classification of structures".
1994:
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1319:
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565:
326:
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73:
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463:
415:
271:
186:
58:
975:
over variable-size input structures, or a scalar prediction on it, by traversing a given structure in
1290:
570:
520:
1787:
1656:
1595:
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1324:
1274:
1008:
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311:
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141:
93:
53:
1943:
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1347:
992:
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Li, Jun; Xu, Kai; Chaudhuri, Siddhartha; Yumer, Ersin; Zhang, Hao; Guibas, Leonadis (2017).
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772:
525:
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369:
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146:
136:
34:
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Micheli, A. (2009-03-01). "Neural
Network for Graphs: A Contextual Constructive Approach".
800:
604:
470:
410:
1487:
984:
820:
351:
88:
1725:
995:. Models and general frameworks have been developed in further works since the 1990s.
2021:
1896:
1855:
Scarselli, F.; Gori, M.; Tsoi, A. C.; Hagenbuchner, M.; Monfardini, G. (2009-01-01).
964:
739:
668:
550:
281:
166:
1947:
1804:
1628:
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1238:
Universal approximation capability of RNN over trees has been proved in literature.
1351:
1831:
1710:
1665:
1966:
1818:
Gallicchio, Claudio; Micheli, Alessio (2013-02-04). "Tree Echo State
Networks".
545:
39:
1726:"Recursive Deep Models for Semantic Compositionality Over a Sentiment Treebank"
1201:
networks use one, tensor-based composition function for all nodes in the tree.
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694:
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316:
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1923:
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1466:"Parsing Natural Scenes and Natural Language with Recursive Neural Networks"
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853:
634:
1939:
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1856:
1673:
1620:
1450:
1397:
1040:-dimensional vector representation of nodes, their parent will also be an
1840:
629:
1434:
1381:
1198:
380:
1464:
Socher, Richard; Lin, Cliff; Ng, Andrew Y.; Manning, Christopher D.
1316:
Proceedings of
International Conference on Neural Networks (ICNN'96)
1507:
1218:(SGD) is used to train the network. The gradient is computed using
1265:
representation into the representation for the current time step.
1007:
624:
619:
346:
1473:
The 28th
International Conference on Machine Learning (ICML 2011)
983:, mainly phrase and sentence continuous representations based on
1018:
1488:"GRASS: Generative Recursive Autoencoders for Shape Structures"
1189:
A framework for unsupervised RNN has been introduced in 2004.
1293:(GNN), Neural Network for Graphs (NN4G), and more recently
912:
List of datasets in computer vision and image processing
1982:
1142:
1052:
1157:
1122:
16:Type of neural network which utilizes recursion
1012:A simple recursive neural network architecture
907:List of datasets for machine-learning research
2002:
940:
8:
1123:{\displaystyle p_{1,2}=\tanh \left(W\right)}
987:. RvNNs have first been introduced to learn
967:created by applying the same set of weights
2009:
1995:
947:
933:
25:
1839:
1786:
1700:
1655:
1594:
1506:
1424:
1323:
1141:
1106:
1093:
1057:
1051:
1749:Learning with Recurrent Neural Networks
1306:
33:
971:over a structured input, to produce a
1173:Recursive cascade correlation (RecCC)
7:
1963:
1961:
1912:IEEE Transactions on Neural Networks
1861:IEEE Transactions on Neural Networks
1583:IEEE Transactions on Neural Networks
1413:IEEE Transactions on Neural Networks
1370:IEEE Transactions on Neural Networks
1363:
1361:
1044:-dimensional vector, calculated as
902:Glossary of artificial intelligence
1981:. You can help Knowledge (XXG) by
14:
1318:. Vol. 1. pp. 347–352.
1220:backpropagation through structure
1965:
1857:"The Graph Neural Network Model"
1746:Hammer, Barbara (2007-10-03).
1112:
1086:
322:Relevance vector machine (RVM)
1:
2033:Artificial intelligence stubs
1295:convolutional neural networks
811:Computational learning theory
375:Expectation–maximization (EM)
1832:10.1016/j.neucom.2012.08.017
1711:10.1016/j.neucom.2004.01.008
1666:10.1016/j.neunet.2004.06.009
1495:ACM Transactions on Graphics
1224:backpropagation through time
768:Coefficient of determination
615:Convolutional neural network
327:Support vector machine (SVM)
1216:stochastic gradient descent
1210:Stochastic gradient descent
989:distributed representations
981:natural language processing
919:Outline of machine learning
816:Empirical risk minimization
2049:
2028:Artificial neural networks
1960:
1262:artificial neural networks
1250:
1158:{\displaystyle n\times 2n}
556:Feedforward neural network
307:Artificial neural networks
18:
1258:Recurrent neural networks
1247:Recurrent neural networks
1228:recurrent neural networks
539:Artificial neural network
1924:10.1109/TNN.2008.2010350
1873:10.1109/TNN.2008.2005605
1797:10.1162/0899766053491878
1334:10.1109/ICNN.1996.548916
1269:Tree Echo State Networks
1253:Recurrent neural network
961:recursive neural network
848:Journals and conferences
795:Mathematical foundations
705:Temporal difference (TD)
561:Recurrent neural network
481:Conditional random field
404:Dimensionality reduction
152:Dimensionality reduction
114:Quantum machine learning
109:Neuromorphic engineering
69:Self-supervised learning
64:Semi-supervised learning
21:recurrent neural network
19:Not to be confused with
1975:artificial intelligence
1605:10.1109/TNN.2004.837783
1552:10.1023/A:1008368105614
1517:10.1145/3072959.3073613
1179:directed acyclic graphs
257:Apprenticeship learning
1977:-related article is a
1159:
1124:
1013:
991:of structure, such as
806:Bias–variance tradeoff
688:Reinforcement learning
664:Spiking neural network
74:Reinforcement learning
1222:(BPTS), a variant of
1160:
1125:
1011:
973:structured prediction
642:Neural radiance field
464:Structured prediction
187:Structured prediction
59:Unsupervised learning
1540:Applied Intelligence
1291:graph neural network
1140:
1050:
831:Statistical learning
729:Learning with humans
521:Local outlier factor
1281:Extension to graphs
1275:reservoir computing
965:deep neural network
674:Electrochemical RAM
581:reservoir computing
312:Logistic regression
231:Supervised learning
217:Multimodal learning
192:Feature engineering
137:Generative modeling
99:Rule-based learning
94:Curriculum learning
54:Supervised learning
29:Part of a series on
1775:Neural Computation
1650:(8–9): 1061–1085.
1155:
1120:
1014:
242: •
157:Density estimation
1990:
1989:
1435:10.1109/72.712151
1382:10.1109/72.572108
1343:978-0-7803-3210-2
1197:Recursive neural
977:topological order
957:
956:
762:Model diagnostics
745:Human-in-the-loop
588:Boltzmann machine
501:Anomaly detection
297:Linear regression
212:Ontology learning
207:Grammar induction
182:Semantic analysis
177:Association rules
162:Anomaly detection
104:Neuro-symbolic AI
2040:
2011:
2004:
1997:
1969:
1962:
1952:
1951:
1907:
1901:
1900:
1852:
1846:
1845:
1843:
1815:
1809:
1808:
1790:
1781:(5): 1109–1159.
1770:
1764:
1763:
1743:
1737:
1736:
1730:
1721:
1715:
1714:
1704:
1684:
1678:
1677:
1659:
1639:
1633:
1632:
1598:
1589:(6): 1396–1410.
1578:
1572:
1571:
1546:(1–2): 117–147.
1535:
1529:
1528:
1510:
1492:
1483:
1477:
1476:
1470:
1461:
1455:
1454:
1428:
1408:
1402:
1401:
1365:
1356:
1355:
1327:
1311:
1185:Unsupervised RNN
1164:
1162:
1161:
1156:
1129:
1127:
1126:
1121:
1119:
1115:
1111:
1110:
1098:
1097:
1068:
1067:
949:
942:
935:
896:Related articles
773:Confusion matrix
526:Isolation forest
471:Graphical models
250:
249:
202:Learning to rank
197:Feature learning
35:Machine learning
26:
2048:
2047:
2043:
2042:
2041:
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2018:
2017:
2016:
2015:
1958:
1956:
1955:
1909:
1908:
1904:
1854:
1853:
1849:
1817:
1816:
1812:
1788:10.1.1.138.2224
1772:
1771:
1767:
1760:
1745:
1744:
1740:
1728:
1723:
1722:
1718:
1686:
1685:
1681:
1657:10.1.1.129.6155
1644:Neural Networks
1641:
1640:
1636:
1596:10.1.1.135.8772
1580:
1579:
1575:
1537:
1536:
1532:
1490:
1485:
1484:
1480:
1468:
1463:
1462:
1458:
1410:
1409:
1405:
1367:
1366:
1359:
1344:
1313:
1312:
1308:
1303:
1283:
1271:
1255:
1249:
1244:
1236:
1212:
1207:
1195:
1187:
1175:
1165:weight matrix.
1138:
1137:
1102:
1089:
1082:
1078:
1053:
1048:
1047:
1035:
1028:
1006:
1001:
953:
924:
923:
897:
889:
888:
849:
841:
840:
801:Kernel machines
796:
788:
787:
763:
755:
754:
735:Active learning
730:
722:
721:
690:
680:
679:
605:Diffusion model
541:
531:
530:
503:
493:
492:
466:
456:
455:
411:Factor analysis
406:
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379:
342:
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331:
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127:
119:
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84:Online learning
49:
37:
24:
17:
12:
11:
5:
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2044:
2036:
2035:
2030:
2020:
2019:
2014:
2013:
2006:
1999:
1991:
1988:
1987:
1970:
1954:
1953:
1918:(3): 498–511.
1902:
1847:
1820:Neurocomputing
1810:
1765:
1758:
1738:
1716:
1689:Neurocomputing
1679:
1634:
1573:
1530:
1478:
1456:
1426:10.1.1.64.2580
1419:(5): 768–786.
1403:
1376:(3): 714–735.
1357:
1342:
1325:10.1.1.52.4759
1305:
1304:
1302:
1299:
1285:Extensions to
1282:
1279:
1270:
1267:
1260:are recursive
1251:Main article:
1248:
1245:
1243:
1242:Related models
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985:word embedding
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821:Occam learning
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778:Learning curve
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262:Decision trees
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239:classification
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132:Classification
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89:Batch learning
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4:
3:
2:
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2012:
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1759:9781846285677
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1200:
1192:
1190:
1184:
1182:
1180:
1172:
1170:
1166:
1152:
1149:
1146:
1143:
1136:is a learned
1135:
1130:
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1107:
1103:
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1090:
1083:
1079:
1075:
1072:
1069:
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1043:
1039:
1032:
1025:
1021:
1020:
1010:
1003:
999:Architectures
998:
996:
994:
993:logical terms
990:
986:
982:
978:
974:
970:
966:
963:is a kind of
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950:
945:
943:
938:
936:
931:
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928:
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917:
913:
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969:recursively
710:Multi-agent
647:Transformer
546:Autoencoder
302:Naive Bayes
40:data mining
2022:Categories
1733:EMNLP 2013
1508:1705.02090
1301:References
1277:paradigm.
1234:Properties
695:Q-learning
593:Restricted
391:Mean shift
340:Clustering
317:Perceptron
245:regression
147:Clustering
142:Regression
1932:1045-9227
1897:206756462
1881:1045-9227
1783:CiteSeerX
1697:CiteSeerX
1652:CiteSeerX
1613:1045-9227
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1560:0924-669X
1501:(4): 52.
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1390:1045-9227
1320:CiteSeerX
1226:used for
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854:ECML PKDD
836:VC theory
783:ROC curve
715:Self-play
635:DeepDream
476:Bayes net
267:Ensembles
48:Paradigms
1948:17486263
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1289:include
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652:Vision
508:RANSAC
386:OPTICS
381:DBSCAN
365:-means
172:AutoML
1973:This
1944:S2CID
1893:S2CID
1801:S2CID
1729:(PDF)
1625:S2CID
1564:S2CID
1521:S2CID
1503:arXiv
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1348:S2CID
1022:. If
1004:Basic
874:IJCAI
700:SARSA
659:Mamba
625:LeNet
620:U-Net
446:t-SNE
370:Fuzzy
347:BIRCH
1979:stub
1936:PMID
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1338:ISBN
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869:ICLR
864:ICML
750:RLHF
566:LSTM
352:CURE
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1920:doi
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