Buch, Englisch, 305 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 487 g
15th International Conference, SISAP 2022, Bologna, Italy, October 5-7, 2022, Proceedings
Buch, Englisch, 305 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 487 g
Reihe: Lecture Notes in Computer Science
ISBN: 978-3-031-17848-1
Verlag: Springer International Publishing
SISAP 2022 is an annual international conference for researchers focusing on similarity search challenges and related theoretical/practical problems, as well as the design of content-based similarity search applications. The 15 full papers presented together with 8 short and 2 doctoral symposium papers were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: Applications; Foundations; Indexing and Clustering; Learning; Doctoral Symposium.
Zielgruppe
Research
Autoren/Hrsg.
Weitere Infos & Material
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.
