Artificial Neural Networks and Machine Learning – ICANN 2020

29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I

Häftad, Engelska, 2020

Av Igor Farkaš, Paolo Masulli, Stefan Wermter, Igor Farkas, Igor Farka, Igor Farka¿

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The proceedings set LNCS 12396 and 12397 constitute the proceedings of the 29th International Conference on Artificial Neural Networks, ICANN 2020, held in Bratislava, Slovakia, in September 2020.*The total of 139 full papers presented in these proceedings was carefully reviewed and selected from 249 submissions.

Produktinformation

Utgivningsdatum2020-10-20
Mått155 x 235 x 49 mm
Vikt1 364 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor891
FörlagSpringer Nature Switzerland AG
ISBN9783030616083

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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 TemporalConvolutional Network with a Graph-Structured Representation.- Generating Facial Expressions Associated with Text.- Image Processing.- Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models.- Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases.- Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE).- SOM-based System for Sequence Chunking and Planning.- Bilinear Models for Machine Learning.- Enriched Feature Representation and Combination for Deep Saliency Detection.- Spectral Graph Reasoning Network for Hyperspectral Image Classification.- Salient Object Detection with Edge Recalibration.- Multi-Scale Cross-Modal Spatial Attention Fusion for Multi-label Image Recognition.- A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes.- Medical Image Processing.- SU-Net: An EfficientEncoder-Decoder Model of Federated Learning for Brain Tumor Segmentation.- Synthesis of Registered Multimodal Medical Images with Lesions.- ACE-Net: Adaptive Context Extraction Network for Medical Image Segmentation.- Wavelet U-Net for Medical Image Segmentation.- Recurrent Neural Networks.- Character-based LSTM-CRF with semantic features for Chinese Event Element Recognition.- Sequence Prediction using Spectral RNNs.- Attention Based Mechanism for Energy Load Time Series Forecasting: AN-LSTM.- DartsReNet: Exploring new RNN cells in ReNet architectures.- On Multi-modal Fusion for Freehand Gesture Recognition.- Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data.