Del 12714 - Lecture Notes in Computer Science

Advances in Knowledge Discovery and Data Mining

25th Pacific-Asia Conference, PAKDD 2021, Virtual Event, May 11–14, 2021, Proceedings, Part III

Häftad, Engelska, 2021

AvKamal Karlapalem,Hong Cheng,Naren Ramakrishnan,R. K. Agrawal,P. Krishna Reddy,Jaideep Srivastava,Tanmoy Chakraborty

1 129 kr

Beställningsvara. Skickas inom 10-15 vardagar. Fri frakt för medlemmar vid köp för minst 249 kr.

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.

Produktinformation

Utgivningsdatum2021-05-08
Mått155 x 235 x 29 mm
Vikt678 g
FormatHäftad
SpråkEngelska
SerieLecture Notes in Computer Science
Antal sidor434
FörlagSpringer Nature Switzerland AG
ISBN9783030757670

Tillhör följande kategorier

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.