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

Quantizationof Non-redundant Neural Networks.- Residual learning for FC kernels of convolutional network.- A Novel Neural Network-based Symbolic Regression Method: Neuro-Encoded Expression Programming.- Compute-efficient neural network architecture optimization by a genetic algorithm.- Controlling Model Complexity in Probabilistic Model-Based Dynamic Optimization of Neural Network Structures.- Predictive Uncertainty Estimation with Temporal Convolutional Networks for Dynamic Evolutionary Optimization.- Sparse Recurrent Mixture Density Networks for Forecasting High Variability Time Series with Confidence Estimates.- A multitask learning neural network for short-term traffic speed prediction and confidence estimation.- Central-diffused Instance Generation Method in Class Incremental Learning.- Marginal Replay vs Conditional Replay for Continual Learning.- Simplified computation and interpretation of Fisher matrices in incremental learning with deep neural networks.- Active Learning for Image Recognition using a Visualization-Based User Interface.- Basic Evaluation Scenarios for Incrementally Trained Classifiers.- Embedding Complexity of Learned Representations in Neural Networks.- Joint Metric Learning on Riemannian Manifold of Global Gaussian Distributions.- Multi-Task Sparse Regression Metric Learning for Heterogeneous Classification.- Fast Approximate Geodesics for Deep Generative Models.- Spatial Attention Network for Few-Shot Learning.- Routine Modeling with Time Series Metric Learning.- Leveraging Domain Knowledge for Reinforcement Learning using MMC Architectures.- Conditions for Unnecessary Logical Constraints in Kernel Machines.- HiSeqGAN: Hierarchical Sequence Synthesis and Prediction.- DeepEX: Bridging the Gap Between Knowledge and Data Driven Techniques for Time Series Forecasting.- Transferable Adversarial Cycle Alignment for Domain Adaption.- Evaluation of domain adaptation approaches for robust classification of heterogeneous biological data sets.- Named Entity Recognition for Chinese Social Media with Domain Adversarial Training and Language Modeling.- Deep Domain Knowledge Distillation for Person Re-identification.- A study on catastrophic forgetting in deep LSTM networks.- A Label-specific Attention-based Network with Regularized Loss for Multi-label Classification.- An Empirical Study of Multi-domain and Multi-task Learning in Chinese Named Entity Recognition.- Filter Method Ensemble with Neural Networks.- Dynamic Centroid Insertion and Adjustment for Data Sets with Multiple Imbalanced Classes.- Increasing the Generalisaton Capacity of Conditional VAEs.- Playing the Large Margin Preference Game.