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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 V de Igor Farkaš

Artificial Neural Networks and Machine Learning – ICANN 2021: 30th International Conference on Artificial Neural Networks, Bratislava, Slovakia, September 14–17, 2021, Proceedings, Part V: Lecture Notes in Computer Science, cartea 12895

Editat de Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter
en Limba Engleză Paperback – 11 sep 2021
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.

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Specificații

ISBN-13: 9783030863821
ISBN-10: 3030863824
Pagini: 693
Ilustrații: XXIV, 693 p. 24 illus., 1 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.99 kg
Ediția:1st ed. 2021
Editura: Springer International Publishing
Colecția Springer
Seriile Lecture Notes in Computer Science, Theoretical Computer Science and General Issues

Locul publicării:Cham, Switzerland

Cuprins

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 andWeighting 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.