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Advances in Knowledge Discovery and ...
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Srivastava, Jaideep.
Advances in Knowledge Discovery and Data Mining = 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III /
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Advances in Knowledge Discovery and Data Mining/ edited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty.
其他題名:
25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III /
其他作者:
Chakraborty, Tanmoy.
面頁冊數:
XXIII, 434 p. 142 illus., 117 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Image Processing and Computer Vision. -
電子資源:
https://doi.org/10.1007/978-3-030-75768-7
ISBN:
9783030757687
Advances in Knowledge Discovery and Data Mining = 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III /
Advances in Knowledge Discovery and Data Mining
25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III /[electronic resource] :edited by Kamal Karlapalem, Hong Cheng, Naren Ramakrishnan, R. K. Agrawal, P. Krishna Reddy, Jaideep Srivastava, Tanmoy Chakraborty. - 1st ed. 2021. - XXIII, 434 p. 142 illus., 117 illus. in color.online resource. - Lecture Notes in Artificial Intelligence ;12714. - Lecture Notes in Artificial Intelligence ;9285.
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.
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.
ISBN: 9783030757687
Standard No.: 10.1007/978-3-030-75768-7doiSubjects--Topical Terms:
670819
Image Processing and Computer Vision.
LC Class. No.: Q334-342
Dewey Class. No.: 006.3
Advances in Knowledge Discovery and Data Mining = 25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III /
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