The proceedings set LNCS 11727, 11728, 11729, 11730, and 11731 constitute the proceedings of the 28th International Conference on Artificial Neural Networks, ICANN 2019, held in Munich, Germany, in September 2019. The total of 277 full papers and 43 short papers presented in these proceedings was carefully reviewed and selected from 494 submissions. They were organized in 5 volumes focusing on theoretical neural computation; deep learning; image processing; text and time series; and workshop and special sessions. 

Table of Contents:
A Reservoir Computing Framework for Continuous Gesture Recognition.- Using conceptors to transfer between long-term and short-term Memory.- Bistable Perception in Conceptor Networks.- Continual Learning exploiting Structure of Fractal Reservoir Computing.- Continuous Blood Pressure Estimation through Optimized Echo State Networks.- Reservoir Topology in Deep Echo State Networks.- Multiple Pattern Generations and Chaotic Itinerant dynamics in Reservoir Computing.- Echo State Network with Adversarial Training.- Hyper-spherical reservoirs for Echo State Networks.- Echo State vs. LSTM Networks for Word Sense Disambiguation.- Echo State Networks for Named Entity Recognition.- Efficient Cross-Validation of Echo State Networks.- Echo State Property of Neuronal Cell Cultures.- Overview on the PHRESCO project:  PHotonic REServoir COmputing.- Classification of Human Actions in Videos with a Large-Scale Photonic Reservoir Computer.- A power-effcient architecture for on-chip reservoir computing.- Time Series Processing with VCSEL-based Reservoir Computer.- Optoelectronic reservoir computing using a mixed digital-analog hardware implementation.- Comparison of Feature Extraction Techniques for Handwritten Digit Recognition with a Photonic Reservoir Computer.- Polarization dynamics of VCSELs improves reservoir computing performance..- Reservoir-size dependent learning in analogue neural networks.- Transferring reservoir computing: formulation and application to  fluid physics.- Investigation of EEG-based Graph-theoretic Analysis for Automatic Diagnosis of Alcohol Use Disorder .- EchoQuan-Net: Direct Quantification of Echo Sequence for Left Ventricle Multidimensional Indices via Global-Local Learning, Geometric Adjustment, and multi-target relation learning.- An attention-based ID-CNNs-CRF model for named entity recognition on clinical electronic medical records.- Deep Text Prior: Weakly Supervised Learning for Assertion Classification.- Inter-region SynchronizationAnalysis based on Heterogeneous Matrix Similarity Measurement.- Bi-ResNet: Fully automated classification of unregistered contralateral mammograms.- Pattern Recognition for COPD Diagnostics Using an Artificial Neural Network and Its Potential Integration on Hardware-based Neuromorphic Platforms.- Quantifying Structural Heterogeneity of Healthy and Cancerous Mitochondria using a Combined Segmentation and Classification USK-Net.- Breast Cancer Classification on Histopathological Images Affected by Data Imbalance Using Active Learning and Deep Convolutional Neural Network.- Measuring the Angle of Hallux Valgus Using Segmentation of Bones on X-ray Images.- Human Body Posture Recognition Using Wearable Devices.- Collaborative Denoising Autoencoder for High Glycated Haemoglobin Prediction.- On Chow-Liu forest based regularization of deep belief networks.- Prototypes within Minimum Enclosing Balls.- Exploring Local Transformation Shared Weights in Convolutional Neural Networks.- The Good, theBad and the Ugly: augmenting a black-box model with expert knowledge.- Hierarchical Attentional Hybrid Neural Networks for Document Classification.- Reinforcement learning informed by optimal control.- Explainable Anomaly Detection via Feature-Based Localization.- Bayesian Automatic Relevance Determination for Feature selection in Credit Default Modelling.- TSXplain: Demystification of DNN Decisions for Time-Series using Natural Language and Statistical Features.- DeepMimic: Mentor-Student Unlabeled Data Based Training.- Evaluation of tag clusterings for user profiling in movie recommendation.- A Sparse Filtering-based Approach for Non-Blind Deep Image Denoising.- Hybrid Attention Driven Text-to-Image Synthesis via Generative Adversarial Networks.- Hypernetwork functional image representation.- Instance-based Segmentation for Boundary Detection of Neuropathic Ulcers through Mask-RCNN.- Capsule Networks for attention under occlusion.- IP-GAN: Learning Identity and Pose Disentanglementin Generative Adversarial Networks.- Hypernetwork Knowledge Graph Embeddings.- Signed Graph Attention Networks.- Graph Classification with 2D Convolutional Neural Networks.- Community Detection via Joint Graph Convolutional Network Embedding in Attribute Network.- Temporal Coding of Neural Stimuli.- Heterogeneous Information Network Embedding with Meta-path-based Graph Attention Networks.- Dual-FOFE-net Neural Models for Entity Linking with PageRank.- Spatial-Temporal Graph Convolutional Networks for Sign Language Recognition.- Graph Convolutional Networks Improve the Prediction of Cancer Driver Genes.- CNN-Based Semantic Change Detection in Satellite Imagery.- Axiomatic Kernels on Graphs for Support Vector Machines.- Multitask Learning on Graph Neural Networks: Learning Multiple Graph Centrality Measures with a Unified Network.- Neural Network Guided Tree-Search Policies for Synthesis Planning .- LSTM and 1-D Convolutional Neural Networks for predictive monitoring of the anaerobic digestion process.- Progressive Docking - a Deep Learning Based Approach for Accelerated Virtual Screening.- Predictive Power of Time-series Based Machine Learning Models for DMPK Measurements in Drug Discovery.- Improving Deep Generative Models with Randomized SMILES.- Attention and Edge Memory Convolution for Bioactivity Prediction.- Application of materials informatics tools to the analysis of combinatorial libraries of all metal-oxides photovoltaic cells.- Analysis and Modelling of False Positives in GPCR Assays.- Characterization of Quantum Derived Electronic Properties of Molecules: A Computational Intelligence Approach.- Using an Autoencoder for Dimensionality Reduction in Quantum Dynamics.- Conformational Oversampling as Data Augmentation for Molecules.- Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks.- Deep Neural Network Architecture for Drug-Target Interaction Prediction.- Mol-CycleGAN - a generative model for molecular optimization.- A TRANSFORMER MODEL FOR RETROSYNTHESIS.- Augmentation is What You Need!.- Diversify Libraries Using Generative Topographic Mapping.- Detection of Frequent-Hitters across various HTS Technologies.- Message Passing Neural Networks scoring functions for structure-based drug discovery.