Adversarial Machine Learning.- On the security relevance of initial weights in deep neural networks.- Fractal Residual Network for Face Image Super-Resolution.- From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders.- Generating Adversarial Texts for Recurrent Neural Networks.- Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation.- Computational Analysis of Robustness in Neural Network Classifiers.- Bioinformatics and Biosignal Analysis.- Convolutional neural networks with reusable full-dimension-long layers for feature selection and classification of motor imagery in EEG signals.- Compressing Genomic Sequences by Using Deep Learning.- Learning Tn5 sequence bias from ATAC-seq on naked chromatin.- Tucker tensor decomposition of multi-session EEG data.- Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models.- Cognitive Models.- Investigating Efficient Learning and Compositionality in Generative LSTM Networks.- Fostering Event Compression using Gated Surprise.- Physiologically-inspired Neural Circuits for the Recognition of Dynamic Faces.- Hierarchical Modeling with Neurodynamical Agglomerative Analysis.- Convolutional Neural Networks and Kernel Methods.- Deep and Wide Neural Networks Covariance Estimation.- Monotone deep Spectrum Kernels.- Permutation Learning in Convolutional Neural Networks for Time Series Analysis.- Deep Learning Applications I.- GTFNet: Ground Truth Fitting Network for Crowd Counting.- Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography.- Multi-Person Absolute 3D Human Pose Estimation with Weak Depth Supervision.- Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-Learning.- Analysis and Prediction of Deforming 3D Shapes using Oriented Bounding Boxes and LSTM Autoencoders.- Deep Learning Applications II.- Novel Sketch-based 3D Model Retrieval via Cross-domain Feature Clustering and Matching.- Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search.- DeepED: a Deep Learning Framework for Estimating Evolutionary Distances.- Interpretable Machine Learning Structure for an Early Prediction of Lane Changes.- Explainable Methods.- Convex Density Constraints for Computing Plausible Counterfactual Explanations.- Identifying Critical States by the Action-Based Variance of Expected Return.- Explaining Concept Drift by Means of Direction.- Few-shot Learning.- Context Adaptive Metric Model for Meta-Learning.- Ensemble-Based Deep Metric Learning for Few-Shot Learning.- More Attentional Local Descriptors for Few-shot Learning.- Implementation of Siamese-based Few-shot Learning Algorithms for the Distinction of COPD and Asthma Subjects.- Few-Shot Learning for Medical Image Classification.- Generative Adversarial Network.- Adversarial Defense via Attention-based Randomized Smoothing.- Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise.- Unsupervised Anomaly Detection with a GAN Augmented Autoencoder.- An Efficient Blurring-Reconstruction Model to Defend against Adversarial Attacks.- EdgeAugment: Data Augmentation by Fusing and Filling Edge Map.- Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images.- Generative and Graph Models.- Variational Autoencoder with Global- and Medium Timescale Auxiliaries for Emotion Recognition from Speech.- Improved Classification Based on Deep Belief Networks.- Temporal Anomaly Detection by Deep Generative Models with Applications to Biological Data.- Inferring, Predicting, and Denoising Causal Wave Dynamics.- PART-GAN: Privacy-Preserving Time-Series Sharing.- EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs.- Hybrid Neural-symbolic Architectures.- Facial Expression Recognition Method based on a Part-based Temporal