Artificial Neural Networks and Machine Learning - Icann 2019: Deep Learning: 28th International Conference on Artificial Neural Networks, Munich, Germ

ISBN-13: 9783030304836 / Angielski / Miękka / 2019 / 807 str.

Igor V. Tetko; Věra Kůrkova; Pavel Karpov

Spis treści

Adaptive Graph Fusion for Unsupervised Feature Selection.- Unsupervised Feature Selection via Local Total-order Preservation.- Discrete Stochastic Search and its Application to Feature-Selection for Deep Relational Machines.- Joint Dictionary Learning for Unsupervised Feature Selection.- Comparison between Filter Criteria for Feature Selection in Regression.- CancelOut: A layer for feature selection in deep neural networks.- Adaptive-L2 Batch Neural Gas.- Application of Self Organizing Map to Preprocessing Input Vectors for Convolutional Neural Network.- Hierarchical Reinforcement Learning with Unlimited Recursive Subroutine Calls.- Automatic Augmentation by Hill Climbing.- Learning Camera-invariant Representation for Person Re-identification.- PA-RetinaNet: Path Augmented RetinaNet for Dense Object Detection.- Singular Value Decomposition and Neural Networks.- PCI: Principal Component Initialization for Deep Autoencoders.- Improving Weight Initialization of ReLU and Output Layers.- Post-synaptic potential regularization has potential.- A Novel Modification on the Levenberg-Marquardt Algorithm for Avoiding Overfitting in Neural Network Training.- Sign Based Derivative Filtering for Stochastic Gradient Descent.- Architecture-aware Bayesian Optimization for Neural Network Tuning.- Non-Convergence and Limit Cycles in the Adam Optimizer.- Learning Internal Dense But External Sparse Structures of Deep Convolutional Neural Network.- Using feature entropy to guide filter pruning for efficient convolutional networks.- Simultaneously Learning Architectures and Features of Deep Neural Networks.- Learning Sparse Hidden States in Long Short-Term Memory.- Multi-objective Pruning for CNNs using Genetic Algorithm.- Dynamically Sacrificing Accuracy for Reduced Computation: Cascaded Inference Based on Softmax Confidence.- Light-Weight Edge Enhanced Network for On-orbit Semantic Segmentation.- Local Normalization Based BN Layer Pruning.- On Practical Approach to Uniform Quantization of 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.