文件名称:
Learning Deep Architectures for AI--content
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上传时间: 2015-05-20
详细说明： Contents 1 Introduction 2 1.1 How do We Train Deep Architectures? 5 1.2 Intermediate Representations: Sharing Features and Abstractions Across Tasks 7 1.3 Desiderata for Learning AI 10 1.4 Outline of the Paper 11 2 Theoretical Advantages of Deep Architectures 13 2.1 Computational Complexity 16 2.2 Informal Arguments 18 3 Local vs Non-Local Generalization 21 3.1 The Limits of Matching Local Templates 21 3.2 Learning Distributed Representations 27 4 Neural Networks for Deep Architectures 30 4.1 Multi-Layer Neural Networks 30 4.2 T he Challenge of Training Deep Neural Networks 31 4.3 Unsupervised Learning for Deep Architectures 39 4.4 Deep Generative Architectures 40 4.5 Convolutional Neural Networks 43 4.6 Auto-Encoders 45 5 Energy-Based Models and Boltzmann Machines 48 5.1 Energy-Based Models and Products of Experts 48 5.2 Boltzmann Machines 53 5.3 Restricted Boltzmann Machines 55 5.4 Contrastive Divergence 59 6 Greedy Layer-Wise Training of Deep Architectures 68 6.1 Layer-Wise Training of Deep Belief Networks 68 6.2 Training Stacked Auto-Encoders 71 6.3 Semi-Supervised and Partially Supervised Training 72 7 Variants of RBMs and Auto-Encoders 74 7.1 Sparse Representations in Auto-Encoders and RBMs 74 7.2 Denoising Auto-Encoders 80 7.3 Lateral Connections 82 7.4 Conditional RBMs and Temporal RBMs 83 7.5 Factored RBMs 85 7.6 Generalizing RBMs and Contrastive Divergence 86 8 Stochastic Variational Bounds for Joint Optimization of DBN Layers 89 8.1 Unfolding RBMs into Infinite Directed Belief Networks 90 8.2 Variational Justification of Greedy Layer-wise Training 92 8.3 Joint Unsupervised Training of All the Layers 95 9 Looking Forward 99 9.1 Global Optimization Strategies 99 9.2 Why Unsupervised Learning is Important 105 9.3 Open Questions 106 10 Conclusion 110 Acknowledgments 112 References 113 ...展开收缩
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