Artificial Neural Networks and Machine Learning - ICANN 2021 : 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14-17, 2021, Proceedings, Part V / edited by Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter.

Format:

Book

Contributor:

Farkaš, Igor., Editor.

Masulli, Paolo, Editor.

Otte, Sebastian., Editor.

Wermter, Stefan, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Theoretical computer science and general issues 2512-2029 ; SL 1, 12895

Theoretical Computer Science and General Issues, 2512-2029 ; 12895

Language:

English

Subjects (All):

Artificial intelligence.

Application software.

Data mining.

Database management.

Social sciences-Data processing.

Computer vision.

Artificial Intelligence.

Computer and Information Systems Applications.

Data Mining and Knowledge Discovery.

Database Management.

Computer Application in Social and Behavioral Sciences.

Computer Vision.

Local Subjects:

Artificial Intelligence.

Computer and Information Systems Applications.

Data Mining and Knowledge Discovery.

Database Management.

Computer Application in Social and Behavioral Sciences.

Computer Vision.

Physical Description:

1 online resource (XXIV, 693 pages) : 24 illustrations, 1 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

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 representation learning, reservoir computing, semi- and unsupervised learning, spiking neural networks, text understanding, transfers and meta learning, and video processing. *The conference was held online 2021 due to the COVID-19 pandemic.

Contents:

Representation learning

SageDy: A Novel Sampling and Aggregating based Representation Learning Approach for Dynamic Networks

CuRL: Coupled Representation Learning of cards and merchants to detect transaction frauds

Revisiting Loss Functions for Person Re-Identification

Statistical Characteristics of Deep Representations: An Empirical Investigation

Reservoir computing

Unsupervised Pretraining of Echo State Networks for Onset Detection

Canary Song Decoder: Transduction and Implicit Segmentation with ESNs and LTSMs

Which Hype for my New Task? Hints and Random Search for Echo State Networks Hyperparameters

Semi- and Unsupervised learning

A new Nearest Neighbor Median Shift Clustering for Binary Data

Self-supervised Multi-view Clustering for Unsupervised Image Segmentation

Evaluate Pseudo Labeling and CNN for multi-variate time series classification in low-data regimes

Deep Variational Autoencoder with Shallow Parallel Path for Top-N Recommendation (VASP)

Short Text Clustering with A Deep Multi-Embedded Self-Supervised Model

Brain-like approaches to unsupervised learning of hidden representations - a comparative study

Spiking neural networks

A Subthreshold Spiking Neuron Circuit Based on the Izhikevich Model

SiamSNN: Siamese Spiking Neural Networks for Energy-Efficient Object Tracking

The principle of weight divergence facilitation for unsupervised pattern recognition in spiking neural networks

Algorithm For 3D-Chemotaxis Using Spiking Neural Network

Signal Denoising with Recurrent Spiking Neural Networks and Active Tuning

Dynamic Action Inference with Recurrent Spiking Neural Networks

End-to-end Spiking Neural Network for Speech Recognition Using Resonating Input Neurons

Text understanding I

Visual-Textual Semantic Alignment Network for Visual Question Answering

Which and Where to Focus: A Simple yet Accurate Framework for Arbitrary-Shaped Nearby Text Detection in Scene Images

STCP: An Efficient Model Combing Subject Triples and Constituency Parsing for Recognizing Textual Entailment

A Latent Variable Model with Hierarchical structure and GPT-2 for long text generation

A Scoring Model Assisted by Frequency for Multi-Document Summarization

A Strategy for Referential Problem in Low-Resource Neural Machine Translation

A Unified Summarization Model with Semantic Guide and Keyword Coverage Mechanism

Hierarchical Lexicon Embedding Architecture for Chinese Named Entity Recognition

Evidence Augment for Multiple-Choice Machine Reading Comprehension by Weak Supervision

Resolving Ambiguity in Hedge Detection by Automatic Generation of Linguistic Rules

Text understanding II

Detecting Scarce Emotions Using BERT and Hyperparameter Optimization

Design and Evaluation of Deep Learning Models for Real-Time Credibility Assessment in Twitter

T-Bert: A Spam Review Detection Model Combining Group Intelligence and Personalized Sentiment Information

Graph Enhanced BERT for Stance-aware Rumor Verification on Social Media

Deep Learning for Suicide and Depression Identification with Unsupervised Label Correction

Learning to Remove: Towards Isotropic Pre-trained BERT Embedding

ExBERT: An External Knowledge Enhanced BERT for Natural Language Inference

Multi-Features-Based Automatic Clinical Coding for Chinese ICD-9-CM-3

Style as Sentiment versus Style as Formality: the same or different?

Transfer and meta learning

Low-resource Neural Machine Translation Using XLNet Pre-training Model

Self-Learning for Received Signal Strength MapReconstruction with Neural Architecture Search

Propagation-aware Social Recommendation by Transfer Learning

Evaluation of Transfer Learning for Visual Road Condition Assessment

EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search

DVAMN: Dual Visual Attention Matching Network for Zero-Shot Action Recognition

Dynamic Tuning and Weighting of Meta-Learning for NMT Domain Adaptation

Improving Transfer Learning in Unsupervised Language Adaptation

Sample-Label View Transfer Active Learning for Time Series Classification

Video processing

Learning Traffic as Videos: A Spatio-Temporal VAE Approach for Traffic Data Imputation

Traffic Camera Calibration via Vehicle Vanishing Point Detection

Efficient Spatio-Temporal Network with Gated Fusion for Video Super-Resolution

Adaptive Correlation Filters Feature Fusion Learning for Visual Tracking

Dense video captioning for incomplete videos

Modeling Context-guided Visual and Linguistic Semantic Feature for Video Captioning.

Other Format:

Printed edition:

ISBN:

978-3-030-86383-8

9783030863838

Access Restriction:

Restricted for use by site license.