國立虎尾科技大學 |

Artificial Neural Networks and Machine Learning – ICANN 2020 = 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Artificial Neural Networks and Machine Learning – ICANN 2020/ edited by Igor Farkaš, Paolo Masulli, Stefan Wermter.
Reminder of title:	29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I /
other author:	Farkaš, Igor.
Description:	XXVII, 891 p. 348 illus., 260 illus. in color.online resource. :
Contained By:	Springer Nature eBook
Subject:	Artificial intelligence. -
Online resource:	https://doi.org/10.1007/978-3-030-61609-0
ISBN:	9783030616090

Artificial Neural Networks and Machine Learning – ICANN 2020 = 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I /
Artificial Neural Networks and Machine Learning – ICANN 202029th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I /[electronic resource] :edited by Igor Farkaš, Paolo Masulli, Stefan Wermter. - 1st ed. 2020. - XXVII, 891 p. 348 illus., 260 illus. in color.online resource. - Theoretical Computer Science and General Issues ;12396. - Theoretical Computer Science and General Issues ;9163.

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 Convolutional 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 Efficient Encoder-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.

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. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.

ISBN: 9783030616090

Standard No.: 10.1007/978-3-030-61609-0doiSubjects--Topical Terms:

559380
Artificial intelligence.

LC Class. No.: Q334-342

Dewey Class. No.: 006.3

Artificial Neural Networks and Machine Learning – ICANN 2020 = 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I /
LDR:07929nam a22004095i 4500 001 1030634
003 DE-He213
005 20201019203215.0
007 cr nn 008mamaa
008 210318s2020 gw | s |||| 0|eng d
020 $a 9783030616090 $9 978-3-030-61609-0
024 7 $a 10.1007/978-3-030-61609-0 $2 doi
035 $a 978-3-030-61609-0
050 4 $a Q334-342
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
245 1 0 $a Artificial Neural Networks and Machine Learning – ICANN 2020 $h [electronic resource] : $b 29th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 15–18, 2020, Proceedings, Part I / $c edited by Igor Farkaš, Paolo Masulli, Stefan Wermter.
250 $a 1st ed. 2020.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2020.
300 $a XXVII, 891 p. 348 illus., 260 illus. in color. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Theoretical Computer Science and General Issues ; $v 12396
505 0 $a 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 Convolutional 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 Efficient Encoder-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.
520 $a 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. They were organized in 2 volumes focusing on topics such as adversarial machine learning, bioinformatics and biosignal analysis, cognitive models, neural network theory and information theoretic learning, and robotics and neural models of perception and action. *The conference was postponed to 2021 due to the COVID-19 pandemic.
650 0 $a Artificial intelligence. $3 559380
650 0 $a Computers. $3 565115
650 0 $a Optical data processing. $3 639187
650 0 $a Application software. $3 528147
650 0 $a Computer organization. $3 596298
650 1 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Information Systems and Communication Service. $3 669203
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 671334
650 2 4 $a Computing Milieux. $3 669921
650 2 4 $a Computer Applications. $3 669785
650 2 4 $a Computer Systems Organization and Communication Networks. $3 669309
700 1 $a Farkaš, Igor. $e editor. $1 https://orcid.org/0000-0003-3503-2080 $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1327514
700 1 $a Masulli, Paolo. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 1112400
700 1 $a Wermter, Stefan. $4 edt $4 http://id.loc.gov/vocabulary/relators/edt $3 675297
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783030616083
776 0 8 $i Printed edition: $z 9783030616106
830 0 $a Theoretical Computer Science and General Issues ; $v 9163 $3 1253524
856 4 0 $u https://doi.org/10.1007/978-3-030-61609-0
912 $a ZDB-2-SCS
912 $a ZDB-2-SXCS
912 $a ZDB-2-LNC
950 $a Computer Science (SpringerNature-11645)
950 $a Computer Science (R0) (SpringerNature-43710)