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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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