Similarity Search and Applications
portes grátis
Similarity Search and Applications
15th International Conference, SISAP 2022, Bologna, Italy, October 5-7, 2022, Proceedings
Bartolini, Ilaria; Falchi, Fabrizio; Lokoc, Jakub; Skopal, Tomas; Patella, Marco; Sapino, Maria Luisa
Springer International Publishing AG
09/2022
305
Mole
Inglês
9783031178481
15 a 20 dias
Descrição não disponível.
?Applications.- Numerical Data Imputation: Choose KNN over Deep Learning.- COSINER: COntext SImilarity data augmentation for Named Entity Recognition.- An Application of Learned Multi-Modal Product Similarity to e-Commerce.- Deep Vision-Language Model for Efficient Multi-modal Similarity Search in Fashion Retrieval.- Stable Anchors for Matching Unlabelled Point Clouds.- Visual Exploration of Human Motion Data.- Foundations.- On Projections to Linear Subspaces.- Concept of Relational Similarity Search.- On the Expected Exclusion Power of Binary Partitions for Metric Search.- Similarity Search with the Distance Density Model.- Generalized Relative Neighborhood Graph (GRNG) for Similarity Search.- A Ptolemaic Partitioning Mechanism.- HubHSP graph: effective data sampling for pivot-based representation strategies.- Indexing and Clustering.- Compacted search tree for graph edit distance computation.- Clustering by Direct Optimization of the Medoid Silhouette.- Automatic Indexing for Similarity Search in ELKI.- Approximate Nearest Neighbor Search on Standard Search Engines.- Evaluation of LID-Aware Graph Embedding Methods for Node Clustering.- Similarity-based Unsupervised Evaluation of Outlier Detection.- Learning.- FastHebb: Scaling Hebbian Training of Deep Neural Networks to ImageNet Level.- Causal Disentanglement with Network Information for Debiased Recommendations.- Causal Disentanglement with Network Information for Debiased Recommendations.- Self-supervised Information Retrieval Trained from Self-generated Sets of Queries and Relevant Documents.- Doctoral Symposium.- Discovering Knowledge Graphs Via Attention-Driven Graph Generation.- Visual Recommendation and Visual Search for Fashion e-commerce.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Computer Science;Informatics;Conference Proceedings;Research;Applications
?Applications.- Numerical Data Imputation: Choose KNN over Deep Learning.- COSINER: COntext SImilarity data augmentation for Named Entity Recognition.- An Application of Learned Multi-Modal Product Similarity to e-Commerce.- Deep Vision-Language Model for Efficient Multi-modal Similarity Search in Fashion Retrieval.- Stable Anchors for Matching Unlabelled Point Clouds.- Visual Exploration of Human Motion Data.- Foundations.- On Projections to Linear Subspaces.- Concept of Relational Similarity Search.- On the Expected Exclusion Power of Binary Partitions for Metric Search.- Similarity Search with the Distance Density Model.- Generalized Relative Neighborhood Graph (GRNG) for Similarity Search.- A Ptolemaic Partitioning Mechanism.- HubHSP graph: effective data sampling for pivot-based representation strategies.- Indexing and Clustering.- Compacted search tree for graph edit distance computation.- Clustering by Direct Optimization of the Medoid Silhouette.- Automatic Indexing for Similarity Search in ELKI.- Approximate Nearest Neighbor Search on Standard Search Engines.- Evaluation of LID-Aware Graph Embedding Methods for Node Clustering.- Similarity-based Unsupervised Evaluation of Outlier Detection.- Learning.- FastHebb: Scaling Hebbian Training of Deep Neural Networks to ImageNet Level.- Causal Disentanglement with Network Information for Debiased Recommendations.- Causal Disentanglement with Network Information for Debiased Recommendations.- Self-supervised Information Retrieval Trained from Self-generated Sets of Queries and Relevant Documents.- Doctoral Symposium.- Discovering Knowledge Graphs Via Attention-Driven Graph Generation.- Visual Recommendation and Visual Search for Fashion e-commerce.
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
