Artificial Neural Networks and Machine Learning - Icann 2019: Text and Time Series
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Artificial Neural Networks and Machine Learning - Icann 2019: Text and Time Series

Artificial Neural Networks and Machine Learning - Icann 2019: Text and Time Series


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About the Book

An ensemble model for winning a Chinese machine reading comprehension competition.- Dependent Multilevel Interaction Network for Natural Language Inference.- Learning to Explain Chinese Slang Words.- Attention-Based Improved BLSTM-CNN for Relation Classification.- An Improved Method of Applying a Machine Translation Model to a Chinese Word Segmentation Task.- Interdependence Model for Multi-label Classification.- Combining deep learning and (structural) feature-based classification methods for copyright-protected PDF documents.- Collaborative Attention Network with Word and N-gram Sequences Modeling for Sentiment Classification.- Targeted Sentiment Classification with Attentional Encoder Network.- Capturing User and Product Information for Sentiment Classification via Hierarchical Separated Attention and Neural Collaborative Filtering.- Imbalanced Sentiment Classification Enhanced with Discourse Marker.- Revising Attention with Position for Aspect-level Sentiment Classification.- Surrounding-Based Attention Networks for Aspect-Level Sentiment Classification.- Mid Roll Advertisement Placement using Multi Modal Emotion Analysis.- DCAR: Deep Collaborative Autoencoder for Recommendation with Implicit Feedback.- Jointly Learning to Detect Emotions and Predict Facebook Reactions.- Discriminative Feature Learning for Speech Emotion Recognition.- A Judicial Sentencing Method Based on Fused Deep Neural Networks.- SECaps: A Sequence Enhanced Capsule Model for Charge Prediction.- Learning to Predict Charges for Judgment with Legal Graph.- A Recurrent Attention Network for Judgment Prediction.- Symmetrical Adversarial Training Nets: A Novel Model For Text Generation.- A Novel Image Captioning Method based on Generative Adversarial Networks.- Quality-Diversity Summarization with Unsupervised Autoencoders.- Conditional GANs for Image Captioning with Sentiments.- Neural Poetry: Learning to Generate Poems using Syllables.- Exploring the Advantages of Corpus in Neural Machine Translation of Agglutinative Language.- RL extraction of syntax-based chunks for sentence compression.- Robust Sound Event Classification with Local Time-Frequency Information and Convolutional Neural Networks.- Neuro-Spectral Audio Synthesis: Exploiting characteristics of the Discrete Fourier Transform in the real-time simulation of musical instruments using parallel Neural Networks.- Ensemble of Convolutional Neural Networks for P300 Speller in Brain Computer Interface.- Deep Recurrent Neural Networks with Nonlinear Masking Layers and Two-Level Estimation for Speech Separation.- Auto-Lag Networks for Real Valued Sequence to Sequence Prediction.- LSTM Prediction on Sudden Occurrence of Maintenance Operation of Air-conditioners in Real-time Pricing Adaptive Control.- Dynamic Ensemble Using Previous and Predicted Future Performance for Multi-Step-Ahead Solar Power Forecasting.- Timage - A Robust Time Series Classification Pipeline.- Prediction of the Next Sensor Event and its Time of Occurrence in Smart Homes.- Multi-task Learning Method for Hierarchical Time Series Forecasting.- Demand-prediction architecture for distribution businesses based on multiple RNNs with alternative weight update.- A Study of Deep Learning for Network Traffic Data Forecasting.- Composite Quantile Regression Long Short-Term Memory Network.- Short-Term Temperature Forecasting on a Several Hours Horizon.- Using Long Short-Term Memory for Wavefront Prediction in Adaptive Optics.- Incorporating Adaptive RNN-based Action Inference and Sensory Perception.- Quality of Prediction of Daily Relativistic Electrons Flux at Geostationary Orbit by Machine Learning Methods.- Soft Subspace Growing Neural Gas for DataStream Clustering.- Region Prediction from Hungarian Folk Music Using Convolutional Neural Networks.- Merging DBSCAN and Density Peak for Robust Clustering.- Market basket analysis using Boltzmann machines.- Dimensionality Reduction for Clustering and Cluster Tracking of Cytometry Data.- Improvi


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Product Details
  • ISBN-13: 9783030304898
  • Publisher: Springer International Publishing
  • Publisher Imprint: Springer
  • Height: 234 mm
  • No of Pages: 761
  • Spine Width: 40 mm
  • Weight: 1137 gr
  • ISBN-10: 3030304892
  • Publisher Date: 05 Sep 2019
  • Binding: Paperback
  • Language: English
  • Returnable: Y
  • Sub Title: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17-19, 2019, Proceedings, Part IV
  • Width: 156 mm


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Artificial Neural Networks and Machine Learning - Icann 2019: Text and Time Series
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