Artificial Neural Networks and Machine Learning – ICANN 2020 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I
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 2...
| Other Authors: | , , |
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| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2020, 2020
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| Edition: | 1st ed. 2020 |
| Series: | Theoretical Computer Science and General Issues
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| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- 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
- 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
- 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
- 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