Similarity Search and Applications

.- Research Track.
.- An Efficient Framework for Approximate Nearest Neighbor Search on High-dimensional Multi-metric Data.
.- REHAB24-6: Physical Therapy Dataset for Analyzing Pose Estimation Methods.
.- ETDD70: Eye-Tracking Dataset for Classification of Dyslexia using AI-based Methods.
.- Demonstrating the Efficacy of Polyadic Queries.
.- Scalable Polyadic Queries.
.- A Dynamic Evaluation Metric for Feature Selection.
.- Personalized Similarity Models for Evaluating Rehabilitation Exercises from Monocular Videos.
.- Impact of the Neighborhood Parameter on Outlier Detection Algorithms.
.- Optimizing CLIP Models for Image Retrieval with Maintained Joint-Embedding Alignment.
.- Bayesian Estimation Approaches for Local Intrinsic Dimensionality.
.- Towards Personalized Similarity Search for Vector Databases.
.- Information Dissimilarity Measures in Decentralized Knowledge Distillation: A Comparative Analysis.
.- An Empirical Evaluation of Search Strategies for Locality-Sensitive Hashing: Lookup, Voting, and Natural Classifier Search.
.- On the Design of Scalable Outlier Detection Methods using Approximate Nearest Neighbor Graphs.
.- A Topological Evaluation Model for Manifold Learning and Embedding Techniques.
.- Local Intrinsic Dimensionality and the Convergence Order of Fixed-Point Iteration.
.- Identifying Propagating Signals with Spatio-Temporal Clustering in Multivariate Time Series.
.- Robust Statistical Scaling of Outlier Scores: Improving the Quality of Outlier Probabilities for Outliers.
.- Advancing the PAM Algorithm to Semi-Supervised k-Medoids Clustering.
.- Hierarchical Clustering without Pairwise Distances by Incremental Similarity Search.
.- Indexing Challenge.
.- Overview of the SISAP 2024 Indexing Challenge.
.- Scaling Learned Metric Index to 100M Datasets.
.- Grouping Sketches to Index High-Dimensional Data in a Resource Limited Setting.
.- Adapting the Exploration Graph for high throughput in low recall regimes.
.- Top-Down Construction of Locally Monotonic Graphs for Similarity Search.