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Preface | ||
1 | Introduction | 1 |
2 | Review of connectionist and distributed memory models | 25 |
3 | Holographic reduced representations | 93 |
4 | HRRs in the frequency domain | 145 |
5 | Using convolution-based storage in systems that learn | 153 |
6 | Estimating analogical similarity | 175 |
7 | Discussion | 221 |
App. A | Means and variances of similarities between bindings | 233 |
App. B | The capacity of a superposition memory | 235 |
App. C | A lower bound for the capacity of superposition memories | 245 |
App. D | The capacity of convolution-based associative memories | 249 |
App. E | A lower bound for the capacity of convolution memories | 259 |
App. F | Means and variances of a signal | 261 |
App. G | The effect of normalization on dot-products | 263 |
App. H | HRRs with circular vectors | 267 |
App. I | Arithmetic tables: an example of HRRs with many items in memory | 273 |
References | 277 | |
Subject Index | 289 | |
Author Index | 297 |
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Add Holographic Reduced Representation: Distributed Representation for Cognitive Structures, While neuroscientists garner success in identifying brain regions and in analyzing individual neurons, ground is still being broken at the intermediate scale of understanding how neurons combine to encode information. This book proposes a method of repres, Holographic Reduced Representation: Distributed Representation for Cognitive Structures to the inventory that you are selling on WonderClubX
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Add Holographic Reduced Representation: Distributed Representation for Cognitive Structures, While neuroscientists garner success in identifying brain regions and in analyzing individual neurons, ground is still being broken at the intermediate scale of understanding how neurons combine to encode information. This book proposes a method of repres, Holographic Reduced Representation: Distributed Representation for Cognitive Structures to your collection on WonderClub |