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Beschreibung
¿Keynotes.- From Intrinsic Dimensionality to Chaos and Control: Towards a Unified Theoretical View.- The Rise of HNSW: Understanding Key Factors Driving the Adoption.- Towards a Universal Similarity Function: the Information Contrast Model and its Application as Evaluation Metric in Artificial Intelligence Tasks.- Research Track.- Finding HSP Neighbors via an Exact, Hierarchical Approach.- Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash.- Mutual nearest neighbor graph for data analysis: Application to metric space clustering.- An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search.- Unbiased Similarity Estimators using Samples.- Retrieve-and-Rank End-to-End Summarization of Biomedical Studies.- Fine-grained Categorization of Mobile Applications through Semantic Similarity Techniques for Apps Classification.- Runs of Side-SharingTandems in Rectangular Arrays.- Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensions.- Class Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification.- The Dataset-similarity-based Approach to Select Datasets for Evaluation in Similarity Retrieval.- Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback.- Accelerating k-Means Clustering with Cover Trees.- Is Quantized ANN Search Cursed? Case Study of Quantifying Search and Index Quality.- Minwise-Independent Permutations with Insertion and Deletion of Features.- SDOclust: Clustering with Sparse Data Observers.- Solving k-Closest Pairs in High-Dimensional Data using Locality- Sensitive Hashing.- Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval.- Similarity Search with Multiple-Object Queries.- Diversity Similarity Join for Big Data.- Indexing Challenge.- Overview of the SISAP 2023 Indexing Challenge.- Enhancing Approximate Nearest Neighbor Search: Binary-Indexed LSH-Tries, Trie Rebuilding, And Batch Extraction.- General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo.- SISAP 2023 Indexing Challenge - Learned Metric Index.- Computational Enhancements of HNSW Targeted to Very Large Datasets.- CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors.
¿Keynotes.- From Intrinsic Dimensionality to Chaos and Control: Towards a Unified Theoretical View.- The Rise of HNSW: Understanding Key Factors Driving the Adoption.- Towards a Universal Similarity Function: the Information Contrast Model and its Application as Evaluation Metric in Artificial Intelligence Tasks.- Research Track.- Finding HSP Neighbors via an Exact, Hierarchical Approach.- Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash.- Mutual nearest neighbor graph for data analysis: Application to metric space clustering.- An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search.- Unbiased Similarity Estimators using Samples.- Retrieve-and-Rank End-to-End Summarization of Biomedical Studies.- Fine-grained Categorization of Mobile Applications through Semantic Similarity Techniques for Apps Classification.- Runs of Side-SharingTandems in Rectangular Arrays.- Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensions.- Class Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification.- The Dataset-similarity-based Approach to Select Datasets for Evaluation in Similarity Retrieval.- Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback.- Accelerating k-Means Clustering with Cover Trees.- Is Quantized ANN Search Cursed? Case Study of Quantifying Search and Index Quality.- Minwise-Independent Permutations with Insertion and Deletion of Features.- SDOclust: Clustering with Sparse Data Observers.- Solving k-Closest Pairs in High-Dimensional Data using Locality- Sensitive Hashing.- Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval.- Similarity Search with Multiple-Object Queries.- Diversity Similarity Join for Big Data.- Indexing Challenge.- Overview of the SISAP 2023 Indexing Challenge.- Enhancing Approximate Nearest Neighbor Search: Binary-Indexed LSH-Tries, Trie Rebuilding, And Batch Extraction.- General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo.- SISAP 2023 Indexing Challenge - Learned Metric Index.- Computational Enhancements of HNSW Targeted to Very Large Datasets.- CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors.
Inhaltsverzeichnis
¿Keynotes.- From Intrinsic Dimensionality to Chaos and Control: Towards a Unified Theoretical View.- The Rise of HNSW: Understanding Key Factors Driving the Adoption.- Towards a Universal Similarity Function: the Information Contrast Model and its Application as Evaluation Metric in Artificial Intelligence Tasks.- Research Track.- Finding HSP Neighbors via an Exact, Hierarchical Approach.- Approximate Similarity Search for Time Series Data Enhanced by Section Min-Hash.- Mutual nearest neighbor graph for data analysis: Application to metric space clustering.- An Alternating Optimization Scheme for Binary Sketches for Cosine Similarity Search.- Unbiased Similarity Estimators using Samples.- Retrieve-and-Rank End-to-End Summarization of Biomedical Studies.- Fine-grained Categorization of Mobile Applications through Semantic Similarity Techniques for Apps Classification.- Runs of Side-SharingTandems in Rectangular Arrays.- Turbo Scan: Fast Sequential Nearest Neighbor Search in High Dimensions.- Class Representatives Selection in Non-Metric Spaces for Nearest Prototype Classification.- The Dataset-similarity-based Approach to Select Datasets for Evaluation in Similarity Retrieval.- Suitability of Nearest Neighbour Indexes for Multimedia Relevance Feedback.- Accelerating k-Means Clustering with Cover Trees.- Is Quantized ANN Search Cursed? Case Study of Quantifying Search and Index Quality.- Minwise-Independent Permutations with Insertion and Deletion of Features.- SDOclust: Clustering with Sparse Data Observers.- Solving k-Closest Pairs in High-Dimensional Data using Locality- Sensitive Hashing.- Vec2Doc: Transforming Dense Vectors into Sparse Representations for Efficient Information Retrieval.- Similarity Search with Multiple-Object Queries.- Diversity Similarity Join for Big Data.- Indexing Challenge.- Overview of the SISAP 2023 Indexing Challenge.- Enhancing Approximate Nearest Neighbor Search: Binary-Indexed LSH-Tries, Trie Rebuilding, And Batch Extraction.- General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo.- SISAP 2023 Indexing Challenge - Learned Metric Index.- Computational Enhancements of HNSW Targeted to Very Large Datasets.- CRANBERRY: Memory-Effective Search in 100M High-Dimensional CLIP Vectors.
Details
Erscheinungsjahr: 2023
Genre: Informatik, Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: Lecture Notes in Computer Science
Inhalt: xxi
310 S.
11 s/w Illustr.
92 farbige Illustr.
310 p. 103 illus.
92 illus. in color.
ISBN-13: 9783031469930
ISBN-10: 3031469933
Sprache: Englisch
Einband: Kartoniert / Broschiert
Redaktion: Pedreira, Oscar
Estivill-Castro, Vladimir
Herausgeber: Oscar Pedreira/Vladimir Estivill-Castro
Auflage: 1st edition 2023
Hersteller: Springer
Springer International Publishing AG
Lecture Notes in Computer Science
Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, D-69121 Heidelberg, juergen.hartmann@springer.com
Maße: 235 x 155 x 19 mm
Von/Mit: Oscar Pedreira (u. a.)
Erscheinungsdatum: 06.12.2023
Gewicht: 0,505 kg
Artikel-ID: 127727563

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