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Artificial Neural Networks and Machine Learning – ICANN 2020

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Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 On the Security Relevance of Initial Weights in Deep Neural Networks
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    Chapter 2 Fractal Residual Network for Face Image Super-Resolution
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    Chapter 3 From Imbalanced Classification to Supervised Outlier Detection Problems: Adversarially Trained Auto Encoders
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    Chapter 4 Generating Adversarial Texts for Recurrent Neural Networks
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    Chapter 5 Enforcing Linearity in DNN Succours Robustness and Adversarial Image Generation
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    Chapter 6 Computational Analysis of Robustness in Neural Network Classifiers
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    Chapter 7 Convolutional Neural Networks with Reusable Full-Dimension-Long Layers for Feature Selection and Classification of Motor Imagery in EEG Signals
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    Chapter 8 Compressing Genomic Sequences by Using Deep Learning
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    Chapter 9 Learning Tn5 Sequence Bias from ATAC-seq on Naked Chromatin
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    Chapter 10 Tucker Tensor Decomposition of Multi-session EEG Data
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    Chapter 11 Reactive Hand Movements from Arm Kinematics and EMG Signals Based on Hierarchical Gaussian Process Dynamical Models
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    Chapter 12 Investigating Efficient Learning and Compositionality in Generative LSTM Networks
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    Chapter 13 Fostering Event Compression Using Gated Surprise
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    Chapter 14 Physiologically-Inspired Neural Circuits for the Recognition of Dynamic Faces
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    Chapter 15 Hierarchical Modeling with Neurodynamical Agglomerative Analysis
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    Chapter 16 Deep and Wide Neural Networks Covariance Estimation
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    Chapter 17 Monotone Deep Spectrum Kernels
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    Chapter 18 Permutation Learning in Convolutional Neural Networks for Time-Series Analysis
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    Chapter 19 GTFNet: Ground Truth Fitting Network for Crowd Counting
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    Chapter 20 Evaluation of Deep Learning Methods for Bone Suppression from Dual Energy Chest Radiography
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    Chapter 21 Multi-person Absolute 3D Human Pose Estimation with Weak Depth Supervision
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    Chapter 22 Solar Power Forecasting Based on Pattern Sequence Similarity and Meta-learning
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    Chapter 23 Analysis and Prediction of Deforming 3D Shapes Using Oriented Bounding Boxes and LSTM Autoencoders
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    Chapter 24 Novel Sketch-Based 3D Model Retrieval via Cross-domain Feature Clustering and Matching
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    Chapter 25 Multi-objective Cuckoo Algorithm for Mobile Devices Network Architecture Search
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    Chapter 26 DeepED: A Deep Learning Framework for Estimating Evolutionary Distances
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    Chapter 27 Interpretable Machine Learning Structure for an Early Prediction of Lane Changes
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    Chapter 28 Convex Density Constraints for Computing Plausible Counterfactual Explanations
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    Chapter 29 Identifying Critical States by the Action-Based Variance of Expected Return
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    Chapter 30 Explaining Concept Drift by Mean of Direction
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    Chapter 31 Context Adaptive Metric Model for Meta-learning
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    Chapter 32 Ensemble-Based Deep Metric Learning for Few-Shot Learning
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    Chapter 33 More Attentional Local Descriptors for Few-Shot Learning
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    Chapter 34 Implementation of Siamese-Based Few-Shot Learning Algorithms for the Distinction of COPD and Asthma Subjects
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    Chapter 35 Few-Shot Learning for Medical Image Classification
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    Chapter 36 Adversarial Defense via Attention-Based Randomized Smoothing
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    Chapter 37 Learning to Learn from Mistakes: Robust Optimization for Adversarial Noise
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    Chapter 38 Unsupervised Anomaly Detection with a GAN Augmented Autoencoder
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    Chapter 39 An Efficient Blurring-Reconstruction Model to Defend Against Adversarial Attacks
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    Chapter 40 EdgeAugment: Data Augmentation by Fusing and Filling Edge Maps
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    Chapter 41 Face Anti-spoofing with a Noise-Attention Network Using Color-Channel Difference Images
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    Chapter 42 Variational Autoencoder with Global- and Medium Timescale Auxiliaries for Emotion Recognition from Speech
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    Chapter 43 Improved Classification Based on Deep Belief Networks
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    Chapter 44 Temporal Anomaly Detection by Deep Generative Models with Applications to Biological Data
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    Chapter 45 Inferring, Predicting, and Denoising Causal Wave Dynamics
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    Chapter 46 PART-GAN: Privacy-Preserving Time-Series Sharing
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    Chapter 47 EvoNet: A Neural Network for Predicting the Evolution of Dynamic Graphs
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    Chapter 48 Facial Expression Recognition Method Based on a Part-Based Temporal Convolutional Network with a Graph-Structured Representation
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    Chapter 49 Generating Facial Expressions Associated with Text
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    Chapter 50 Bilinear Fusion of Commonsense Knowledge with Attention-Based NLI Models
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    Chapter 51 Neural-Symbolic Relational Reasoning on Graph Models: Effective Link Inference and Computation from Knowledge Bases
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    Chapter 52 Tell Me Why You Feel That Way: Processing Compositional Dependency for Tree-LSTM Aspect Sentiment Triplet Extraction (TASTE)
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    Chapter 53 SOM-Based System for Sequence Chunking and Planning
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    Chapter 54 Bilinear Models for Machine Learning
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    Chapter 55 Enriched Feature Representation and Combination for Deep Saliency Detection
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    Chapter 56 Spectral Graph Reasoning Network for Hyperspectral Image Classification
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    Chapter 57 Salient Object Detection with Edge Recalibration
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    Chapter 58 Multi-Scale Cross-Modal Spatial Attention Fusion for Multi-label Image Recognition
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    Chapter 59 A New Efficient Finger-Vein Verification Based on Lightweight Neural Network Using Multiple Schemes
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    Chapter 60 SU-Net: An Efficient Encoder-Decoder Model of Federated Learning for Brain Tumor Segmentation
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    Chapter 61 Synthesis of Registered Multimodal Medical Images with Lesions
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    Chapter 62 ACE-Net: Adaptive Context Extraction Network for Medical Image Segmentation
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    Chapter 63 Wavelet U-Net for Medical Image Segmentation
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    Chapter 64 Character-Based LSTM-CRF with Semantic Features for Chinese Event Element Recognition
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    Chapter 65 Sequence Prediction Using Spectral RNNs
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    Chapter 66 Attention Based Mechanism for Load Time Series Forecasting: AN-LSTM
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    Chapter 67 DartsReNet: Exploring New RNN Cells in ReNet Architectures
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    Chapter 68 On Multi-modal Fusion for Freehand Gesture Recognition
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    Chapter 69 Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data
Attention for Chapter 12: Investigating Efficient Learning and Compositionality in Generative LSTM Networks
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Chapter title
Investigating Efficient Learning and Compositionality in Generative LSTM Networks
Chapter number 12
Book title
Artificial Neural Networks and Machine Learning – ICANN 2020
Published by
Springer, Cham, October 2020
DOI 10.1007/978-3-030-61609-0_12
Book ISBNs
978-3-03-061608-3, 978-3-03-061609-0
Authors

Sarah Fabi, Sebastian Otte, Jonas Gregor Wiese, Martin V. Butz, Fabi, Sarah, Otte, Sebastian, Wiese, Jonas Gregor, Butz, Martin V.

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 3 60%
Researcher 1 20%
Student > Master 1 20%
Readers by discipline Count As %
Computer Science 3 60%
Psychology 2 40%