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Advances in Knowledge Discovery and Data Mining

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Cover of 'Advances in Knowledge Discovery and Data Mining'

Table of Contents

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    Book Overview
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    Chapter 1 FR $$^3$$ 3 LS: A Forecasting Model with Robust and Reduced Redundancy Latent Series
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    Chapter 2 Knowledge-Infused Optimization for Parameter Selection in Numerical Simulations
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    Chapter 3 Material Microstructure Design Using VAE-Regression with a Multimodal Prior
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    Chapter 4 A Weighted Cross-Modal Feature Aggregation Network for Rumor Detection
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    Chapter 5 Quantifying Opinion Rejection: A Method to Detect Social Media Echo Chambers
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    Chapter 6 KiProL: A Knowledge-Injected Prompt Learning Framework for Language Generation
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    Chapter 7 GViG: Generative Visual Grounding Using Prompt-Based Language Modeling for Visual Question Answering
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    Chapter 8 Aspect-Based Fake News Detection
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    Chapter 9 DQAC: Detoxifying Query Auto-completion with Adapters
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    Chapter 10 Graph Neural Network Approach to Semantic Type Detection in Tables
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    Chapter 11 TCGNN: Text-Clustering Graph Neural Networks for Fake News Detection on Social Media
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    Chapter 12 Exploiting Adaptive Contextual Masking for Aspect-Based Sentiment Analysis
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    Chapter 13 An Automated Approach for Generating Conceptual Riddles
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    Chapter 14 DiffFind : Discovering Differential Equations from Time Series
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    Chapter 15 DEAL: Data-Efficient Active Learning for Regression Under Drift
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    Chapter 16 Evolving Super Graph Neural Networks for Large-Scale Time-Series Forecasting
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    Chapter 17 Unlearnable Examples for Time Series
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    Chapter 18 Learning Disentangled Task-Related Representation for Time Series
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    Chapter 19 A Multi-view Feature Construction and Multi-Encoder-Decoder Transformer Architecture for Time Series Classification
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    Chapter 20 Kernel Representation Learning with Dynamic Regime Discovery for Time Series Forecasting
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    Chapter 21 Hyperparameter Tuning MLP’s for Probabilistic Time Series Forecasting
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    Chapter 22 Efficient and Accurate Similarity-Aware Graph Neural Network for Semi-supervised Time Series Classification
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    Chapter 23 STLGRU: Spatio-Temporal Lightweight Graph GRU for Traffic Flow Prediction
Attention for Chapter 10: Graph Neural Network Approach to Semantic Type Detection in Tables
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Chapter title
Graph Neural Network Approach to Semantic Type Detection in Tables
Chapter number 10
Book title
Advances in Knowledge Discovery and Data Mining
Published in
arXiv, January 2024
DOI 10.1007/978-981-97-2266-2_10
Book ISBNs
978-9-81-972265-5, 978-9-81-972266-2
Authors

Hoseinzade, Ehsan, Wang, Ke, Ehsan Hoseinzade, Ke Wang

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Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 02 May 2024.
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#23,176,760
of 25,830,657 outputs
Outputs from arXiv
#693,258
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#294,339
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Outputs of similar age from arXiv
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