Artificial Neural Networks and Machine Learning – ICANN 2019: Deep Learning

Table of Contents

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   Book Overview
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   Chapter 1 Adaptive Graph Fusion for Unsupervised Feature Selection
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   Chapter 2 Unsupervised Feature Selection via Local Total-Order Preservation
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   Chapter 3 Discrete Stochastic Search and Its Application to Feature-Selection for Deep Relational Machines
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   Chapter 4 Joint Dictionary Learning for Unsupervised Feature Selection
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   Chapter 5 Comparison Between Filter Criteria for Feature Selection in Regression
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   Chapter 6 CancelOut: A Layer for Feature Selection in Deep Neural Networks
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   Chapter 7 Adaptive- $$L_2$$ L 2 Batch Neural Gas
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   Chapter 8 Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network
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    Chapter 9 Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls
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    Chapter 10 Automatic Augmentation by Hill Climbing
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    Chapter 11 Learning Camera-Invariant Representation for Person Re-identification
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    Chapter 12 PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection
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    Chapter 13 Singular Value Decomposition and Neural Networks
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    Chapter 14 PCI: Principal Component Initialization for Deep Autoencoders
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    Chapter 15 Improving Weight Initialization of ReLU and Output Layers
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    Chapter 16 Post-synaptic Potential Regularization Has Potential
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    Chapter 17 A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training
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    Chapter 18 Sign Based Derivative Filtering for Stochastic Gradient Descent
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    Chapter 19 Architecture-Aware Bayesian Optimization for Neural Network Tuning
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    Chapter 20 Non-convergence and Limit Cycles in the Adam Optimizer
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    Chapter 21 Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network
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    Chapter 22 Using Feature Entropy to Guide Filter Pruning for Efficient Convolutional Networks
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    Chapter 23 Simultaneously Learning Architectures and Features of Deep Neural Networks
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    Chapter 24 Learning Sparse Hidden States in Long Short-Term Memory
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    Chapter 25 Multi-objective Pruning for CNNs Using Genetic Algorithm
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    Chapter 26 Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence
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    Chapter 27 Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation
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    Chapter 28 Local Normalization Based BN Layer Pruning
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    Chapter 29 On Practical Approach to Uniform Quantization of Non-redundant Neural Networks
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    Chapter 30 Residual Learning for FC Kernels of Convolutional Network
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    Chapter 31 A Novel Neural Network-Based Symbolic Regression Method: Neuro-Encoded Expression Programming
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    Chapter 32 Compute-Efficient Neural Network Architecture Optimization by a Genetic Algorithm
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    Chapter 33 Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures
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    Chapter 34 Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization
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    Chapter 35 Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates
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    Chapter 36 A Multitask Learning Neural Network for Short-Term Traffic Speed Prediction and Confidence Estimation
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    Chapter 37 Central-Diffused Instance Generation Method in Class Incremental Learning
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    Chapter 38 Marginal Replay vs Conditional Replay for Continual Learning
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    Chapter 39 Simplified Computation and Interpretation of Fisher Matrices in Incremental Learning with Deep Neural Networks
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    Chapter 40 Active Learning for Image Recognition Using a Visualization-Based User Interface
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    Chapter 41 Basic Evaluation Scenarios for Incrementally Trained Classifiers
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    Chapter 42 Embedding Complexity of Learned Representations in Neural Networks
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    Chapter 43 Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions
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    Chapter 44 Multi-task Sparse Regression Metric Learning for Heterogeneous Classification
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    Chapter 45 Fast Approximate Geodesics for Deep Generative Models
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    Chapter 46 Spatial Attention Network for Few-Shot Learning
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    Chapter 47 Routine Modeling with Time Series Metric Learning
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    Chapter 48 Leveraging Domain Knowledge for Reinforcement Learning Using MMC Architectures
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    Chapter 49 Conditions for Unnecessary Logical Constraints in Kernel Machines
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    Chapter 50 HiSeqGAN: Hierarchical Sequence Synthesis and Prediction
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    Chapter 51 DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting
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    Chapter 52 Transferable Adversarial Cycle Alignment for Domain Adaption
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    Chapter 53 Evaluation of Domain Adaptation Approaches for Robust Classification of Heterogeneous Biological Data Sets
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    Chapter 54 Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling
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    Chapter 55 Deep Domain Knowledge Distillation for Person Re-identification
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    Chapter 56 A Study on Catastrophic Forgetting in Deep LSTM Networks
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    Chapter 57 A Label-Specific Attention-Based Network with Regularized Loss for Multi-label Classification
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    Chapter 58 An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition
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    Chapter 59 Filter Method Ensemble with Neural Networks
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    Chapter 60 Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes
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    Chapter 61 Increasing the Generalisaton Capacity of Conditional VAEs
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    Chapter 62 Playing the Large Margin Preference Game