Artificial Neural Networks and Machine Learning – ICANN 2021

30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I

AvIgor Farkaš,Paolo Masulli

Häftad, Engelska, 2021

Del 12891 i serien Lecture Notes in Computer Science

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The proceedings set LNCS 12891, LNCS 12892, LNCS 12893, LNCS 12894 and LNCS 12895 constitute the proceedings of the 30th International Conference on Artificial Neural Networks, ICANN 2021, held in Bratislava, Slovakia, in September 2021.* The total of 265 full papers presented in these proceedings was carefully reviewed and selected from 496 submissions, and organized in 5 volumes.In this volume, the papers focus on topics such as adversarial machine learning, anomaly detection, attention and transformers, audio and multimodal applications, bioinformatics and biosignal analysis, capsule networks and cognitive models. *The conference was held online 2021 due to the COVID-19 pandemic.

Produktinformation

Utgivningsdatum:2021-09-12
Mått:155 x 235 x 35 mm
Vikt:961 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:617
Förlag:Springer Nature Switzerland AG
ISBN:9783030863616

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Adversarial machine learning.- An Improved (Adversarial) Reprogramming Technique for Neural Networks.- Adversarial Robustness in Deep Learning: Attacks on Fragile Neurons.- How to compare adversarial robustness of classifiers from a global perspective.- Multiple-Model based Defense for Deep Reinforcement Learning against Adversarial Attack.- Neural Paraphrase Generation with Multi-Domain Corpus.- Leveraging Adversarial Training to Facilitate Grammatical Error Correction.- Statistical Certiﬁcation of Acceptable Robustness for Neural Networks.- Model Extraction and Adversarial Attacks on Neural Networks using Switching Power Information.- Anomaly detection.- o 0097 - CmaGraph: A TriBlocks Anomaly Detection Method in Dynamic Graph Using Evolutionary Community Representation Learning.- Falcon: Malware Detection and Categorization with Network Traffic Images.- Attention-based Bi-LSTM for Anomaly Detection on Time-Series Data.- Semi-supervised Graph Edge Convolutional Network for Anomaly Detection.- Feature Creation Towards the Detection of Non-Control-Flow Hijacking Attacks.- Attention and transformers I.- An Attention Module for Convolutional Neural Networks.- Attention-based 3D neural architectures for predicting cracks in designs.- Entity-aware Biaffine Attention for Constituent Parsing.- Attention-based Multi-View Feature Fusion for Cross-Domain Recommendation.- Say in Human-like Way: Hierarchical Cross-modal Information Abstraction and Summarization for Controllable Captioning.- DAEMA: Denoising Autoencoder with Mask Attention.- Spatial-Temporal Traffic Data Imputation via Graph Attention Convolutional Network.- EGAT: Edge-Featured Graph Attention Network.- Attention and transformers II.- Knowledge Graph Enhanced Transformer for Generative Question Answering Tasks.- GAttANet: Global attention agreement for convolutional neural networks.- Classification Models for Partially Ordered Sequences.- TINet: Multi-dimensional Traffic Data Imputation via Transformer Network.- Sequential Self-Attentive model for Knowledge Tracing.- Multi-Object Tracking based on Nearest Optimal Template Library.- TSTNet: A Sequence to Sequence Transformer Network for Spatial-temporal Traffic Prediction.- Audio and multimodal applications.- A multimode two-stream network for egocentric action recognition.- Behavior of Keyword Spotting Networks Under Noisy Conditions.- Robust Stroke Recognition via Vision and IMU in Robotic Table Tennis.- AMVAE: Asymmetric Multimodal Variational Autoencoder for Multi-view Representation.- Enhancing Separate Encoding with Multi-layer Feature Alignment for Image-Text Matching.- Bird Audio Diarization with Faster R-CNN.- Multi-Modal Chorus Recognition for Improving Song Search.- FaVoA: Face-Voice Association Favours Ambiguous Speaker Detection.- Bioinformatics and biosignal analysis.- Identification of Incorrect Karyotypes Using Deep Learning.- A Metagraph-Based Model for Predicting Drug-Target Interaction on Heterogeneous Network.- Evaluating Multiple-Concept Biomedical Hypotheses Based on Deep Sets.- A Network Embedding Based Approach to Drug-Target Interaction Prediction Using Additional Implicit Networks.- Capsule networks.- CNNapsule: A Lightweight Network with Fusion Features for Monocular Depth Estimation.- Learning Optimal Primary Capsules by Information Bottleneck.- Capsule Networks with Routing Annealing.- Training Deep Capsule Networks with Residual Connections.- Cognitive models.- Interpretable Visual Understanding with Cognitive Attention Network.- A Bio-Inspired Mechanism Based on Neural Threshold Regulation to Compensate Variability in Network Connectivity.- A Predictive Coding Account for Chaotic Itinerancy.- A Computational Model of the Effect of Short-Term Monocular Deprivation on Binocular Rivalry in the Context of Amblyopia.- Transitions among metastable states underlie context-dependent working memories in a multiple timescale network.

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Artificial Neural Networks and Machine Learning – ICANN 2021

30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I

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Artificial Neural Networks and Machine Learning – ICANN 2021

30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part I

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Produktinformation

Utforska kategorier

Innehållsförteckning