Transfer Learning

Contents......Page 6
Preface......Page 9
PART I FOUNDATIONS OF TRANSFER LEARNING......Page 14
 1.1 AI, Machine Learning and Transfer Learning......Page 16
 1.2 Transfer Learning: A Definition......Page 20
 1.3 Relationship to Existing Machine Learning Paradigms......Page 24
 1.4 Fundamental Research Issues in Transfer Learning......Page 26
 1.5 Applications of Transfer Learning......Page 27
 1.6 Historical Notes......Page 30
 1.7 About This Book......Page 31
 2.1 Introduction......Page 36
 2.2 Instance-Based Noninductive Transfer Learning......Page 38
 2.3 Instance-Based Inductive Transfer Learning......Page 41
 3.1 Introduction......Page 47
 3.2 Minimizing the Domain Discrepancy......Page 48
 3.3 Learning Universal Features......Page 54
 3.4 Feature Augmentation......Page 56
 4.1 Introduction......Page 58
 4.2 Transfer through Shared Model Components......Page 60
 4.3 Transfer through Regularization......Page 63
 5.1 Introduction......Page 71
 5.3 Relation-Based Transfer Learning Based on MLNs......Page 74
 6.1 Introduction......Page 81
 6.2 The Heterogeneous Transfer Learning Problem......Page 83
 6.3 Methodologies......Page 84
 6.4 Applications......Page 103
 7.1 Introduction......Page 106
 7.2 Generative Adversarial Networks......Page 107
 7.3 Transfer Learning with Adversarial Models......Page 110
 7.4 Discussion......Page 117
 8.1 Introduction......Page 118
 8.2 Background......Page 120
 8.3 Inter-task Transfer Learning......Page 126
 8.4 Inter-domain Transfer Learning......Page 135
 9.1 Introduction......Page 139
 9.3 Multi-task Supervised Learning......Page 141
 9.4 Multi-task Unsupervised Learning......Page 150
 9.6 Multi-task Active Learning......Page 151
 9.8 Multi-task Online Learning......Page 152
 9.10 Parallel and Distributed Multi-task Learning......Page 153
 10.1 Introduction......Page 154
 10.2 Generalization Bounds for Multi-task Learning......Page 155
 10.3 Generalization Bounds for Supervised Transfer Learning......Page 158
 10.4 Generalization Bounds for Unsupervised Transfer Learning......Page 161
 11.1 Introduction......Page 164
 11.2 TTL over Mixed Graphs......Page 166
 11.3 TTL with Hidden Feature Representations......Page 171
 11.4 TTL with Deep Neural Networks......Page 175
 12.1 Introduction......Page 181
 12.2 The L2T Framework......Page 182
 12.3 Parameterizing What to Transfer......Page 183
 12.4 Learning from Experiences......Page 184
 12.6 Connections to Other Learning Paradigms......Page 187
 13.1 Introduction......Page 190
 13.2 Zero-Shot Learning......Page 191
 13.3 One-Shot Learning......Page 197
 13.4 Bayesian Program Learning......Page 200
 13.5 Poor Resource Learning......Page 203
 13.6 Domain Generalization......Page 206
 14.1 Introduction......Page 209
 14.2 Lifelong Machine Learning: A Definition......Page 210
 14.3 Lifelong Machine Learning through Invariant Knowledge......Page 211
 14.4 Lifelong Machine Learning in Sentiment Classification......Page 212
 14.5 Shared Model Components as Multi-task Learning......Page 216
 14.6 Never-Ending Language Learning......Page 217
 PART II APPLICATIONS OF TRANSFER LEARNING......Page 222
 15.1 Introduction......Page 224
 15.2 Differential Privacy......Page 225
 15.3 Privacy-Preserving Transfer Learning......Page 228
 16.1 Introduction......Page 234
 16.2 Overview......Page 235
 16.3 Transfer Learning for Medical Image Analysis......Page 242
 17.2 Transfer Learning in NLP......Page 247
 17.3 Transfer Learning in Sentiment Analysis......Page 254
 18.1 Introduction......Page 270
 18.3 Transfer Learning in Spoken Language Understanding......Page 272
 18.4 Transfer Learning in Dialogue State Tracker......Page 275
 18.5 Transfer Learning in DPL......Page 276
 18.6 Transfer Learning in Natural Language Generation......Page 281
 18.7 Transfer Learning in End-to-End Dialogue Systems......Page 282
 19.1 Introduction......Page 292
 19.2 What to Transfer in Recommendation......Page 293
 19.3 News Recommendation......Page 297
 19.4 VIP Recommendation in Social Networks......Page 301
 20.1 Introduction......Page 306
 20.2 Machine Learning Problems in Bioinformatics......Page 307
 20.3 Biological Sequence Analysis......Page 308
 20.5 Systems Biology......Page 312
 20.6 Biomedical Text and Image Mining......Page 314
 20.7 Deep Learning for Bioinformatics......Page 315
 21.2 Transfer Learning for Wireless Localization......Page 320
 21.3 Transfer Learning for Activity Recognition......Page 329
 22.1 Introduction......Page 337
 22.2 “What to Transfer” in Urban Computing......Page 338
 22.3 Key Issues of Transfer Learning in Urban Computing......Page 339
 22.4 Chain Store Recommendation......Page 340
 22.5 Air-Quality Prediction......Page 343
23 Concluding Remarks......Page 347
References......Page 349
Index......Page 390