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Advances in Knowledge Discovery and Data Mining : 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11-14, 2021, Proceedings, Part III / edited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty.
- Format:
- Book
- Series:
- Computer Science (SpringerNature-11645)
- Lecture notes in computer science. Lecture notes in artificial intelligence ; 12714
- Lecture Notes in Artificial Intelligence ; 12714
- Language:
- English
- Subjects (All):
- Artificial intelligence.
- Social sciences-Data processing.
- Algorithms.
- Education-Data processing.
- Computer science-Mathematics.
- Computer vision.
- Artificial Intelligence.
- Computer Application in Social and Behavioral Sciences.
- Design and Analysis of Algorithms.
- Computers and Education.
- Mathematics of Computing.
- Computer Vision.
- Local Subjects:
- Artificial Intelligence.
- Computer Application in Social and Behavioral Sciences.
- Design and Analysis of Algorithms.
- Computers and Education.
- Mathematics of Computing.
- Computer Vision.
- Physical Description:
- 1 online resource (XXIII, 434 pages) : 142 illustrations, 117 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 3-volume set LNAI 12712-12714 constitutes the proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, PAKDD 2021, which was held during May 11-14, 2021. The 157 papers included in the proceedings were carefully reviewed and selected from a total of 628 submissions. They were organized in topical sections as follows: Part I: Applications of knowledge discovery and data mining of specialized data; Part II: Classical data mining; data mining theory and principles; recommender systems; and text analytics; Part III: Representation learning and embedding, and learning from data.
- Contents:
- Representation Learning and Embedding
- Episode Adaptive Embedding Networks for Few-shot Learning
- Universal Representation for Code
- Self-supervised Adaptive Aggregator Learning on Graph
- A Fast Algorithm for Simultaneous Sparse Approximation
- STEPs-RL: Speech-Text Entanglement for Phonetically Sound Representation Learning
- RW-GCN: Training Graph Convolution Networks with biased random walk for Semi-Supervised Classification
- Loss-aware Pattern Inference: A Correction on the Wrongly Claimed Limitations of Embedding Models
- SST-GNN: Simplified Spatio-temporal Traffic forecasting model using Graph Neural Network
- VIKING: Adversarial Attack on Network Embeddings via Supervised Network Poisoning
- Self-supervised Graph Representation Learning with Variational Inference
- Manifold Approximation and Projection by Maximizing Graph Information
- Learning Attention-based Translational Knowledge Graph Embedding via Nonlinear Dynamic Mapping
- Multi-Grained Dependency Graph Neural Network for Chinese Open Information Extraction
- Human-Understandable Decision Making for Visual Recognition
- LightCAKE: A Lightweight Framework for Context-Aware Knowledge Graph Embedding
- Transferring Domain Knowledge with an Adviser in Continuous Tasks
- Inferring Hierarchical Mixture Structures: A Bayesian Nonparametric Approach
- Quality Control for Hierarchical Classification with Incomplete Annotations
- Learning from Data
- Learning Discriminative Features using Multi-label Dual Space
- AutoCluster: Meta-learning Based Ensemble Method for Automated Unsupervised Clustering
- BanditRank: Learning to Rank Using Contextual Bandits
- A compressed and accelerated SegNet for plant leaf disease segmentation: A Differential Evolution based approach
- Meta-Context Transformers for Domain-Specific Response Generation
- A Multi-task Kernel Learning Algorithm for Survival Analysis
- Meta-data Augmentation based Search Strategy through Generative Adversarial Network for AutoML Model Selection
- Tree-Capsule: Tree-Structured Capsule Network for Improving Relation Extraction
- Rule Injection-based Generative Adversarial Imitation Learning for Knowledge Graph Reasoning
- Hierarchical Self Attention Based Autoencoder for Open-Set Human Activity Recognition
- Reinforced Natural Language Inference for Distantly Supervised Relation Classification
- SaGCN: Structure-aware Graph Convolution Network for Document-level Relation Extraction
- Addressing the class imbalance problem in medical image segmentation via accelerated Tversky loss function
- Incorporating Relational Knowledge in Explainable Fake News Detection
- Incorporating Syntactic Information into Relation Representations for Enhanced Relation Extraction.
- Other Format:
- Printed edition:
- ISBN:
- 978-3-030-75768-7
- 9783030757687
- Access Restriction:
- Restricted for use by site license.
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