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Convolutional deep belief network

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50:, though it has been used in other domains too. The salient features of the model include the fact that it scales well to high-dimensional images and is translation-invariant. 146: 207: 65:. Depending on whether the network is to be used for discrimination or generative tasks, it is then "fine tuned" or trained with either 178: 57:
to reduce the dimensions in higher layers in the network. Training of the network involves a pre-training stage accomplished in a
93: 212: 35: 32: 28: 111: 139: 87:"Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations" 62: 140:"Unsupervised feature learning for audio classification using convolutional deep belief networks" 47: 58: 43: 39: 17: 124: 66: 171: 86: 201: 25: 54: 69:
or the up–down algorithm (contrastive–divergence), respectively.
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stacked together. Alternatively, it is a hierarchical
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Lee, Honglak; Yan Largman; Peter Pham; Andrew Y. Ng.
42:for deep learning, which is highly effective in 8: 85:Lee, Honglak; Grosse, Ranganath; Andrew Ng. 53:CDBNs use the technique of probabilistic 77: 120: 109: 7: 172:"Convolutional Deep Belief Networks" 61:layer-wise manner, similar to other 14: 22:convolutional deep belief network 184:from the original on 2014-04-07 152:from the original on 2023-01-28 99:from the original on 2014-04-07 31:composed of multiple layers of 1: 36:restricted Boltzmann machines 229: 208:Artificial neural networks 29:artificial neural network 119:Cite journal requires 213:Probabilistic models 170:Coviello, Emanuele. 63:deep belief networks 24:(CDBN) is a type of 48:object recognition 220: 193: 192: 190: 189: 183: 176: 167: 161: 160: 158: 157: 151: 144: 135: 129: 128: 122: 117: 115: 107: 105: 104: 98: 91: 82: 67:back-propagation 44:image processing 40:generative model 18:computer science 228: 227: 223: 222: 221: 219: 218: 217: 198: 197: 196: 187: 185: 181: 174: 169: 168: 164: 155: 153: 149: 142: 137: 136: 132: 118: 108: 102: 100: 96: 89: 84: 83: 79: 75: 12: 11: 5: 226: 224: 216: 215: 210: 200: 199: 195: 194: 162: 130: 121:|journal= 76: 74: 71: 13: 10: 9: 6: 4: 3: 2: 225: 214: 211: 209: 206: 205: 203: 180: 173: 166: 163: 148: 141: 134: 131: 126: 113: 95: 88: 81: 78: 72: 70: 68: 64: 60: 56: 51: 49: 45: 41: 37: 34: 33:convolutional 30: 27: 23: 19: 186:. Retrieved 165: 154:. Retrieved 133: 112:cite journal 101:. Retrieved 80: 52: 21: 15: 55:max-pooling 202:Categories 188:2014-04-01 156:2019-08-25 103:2014-04-01 73:References 179:Archived 147:Archived 94:Archived 59:greedy 182:(PDF) 175:(PDF) 150:(PDF) 143:(PDF) 97:(PDF) 90:(PDF) 125:help 46:and 26:deep 20:, a 16:In 204:: 177:. 145:. 116:: 114:}} 110:{{ 92:. 191:. 159:. 127:) 123:( 106:.

Index

computer science
deep
artificial neural network
convolutional
restricted Boltzmann machines
generative model
image processing
object recognition
max-pooling
greedy
deep belief networks
back-propagation
"Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations"
Archived
cite journal
help
"Unsupervised feature learning for audio classification using convolutional deep belief networks"
Archived
"Convolutional Deep Belief Networks"
Archived
Categories
Artificial neural networks
Probabilistic models

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