Zobrazeno 1 - 10
of 334
pro vyhledávání: '"PALPANAS, THEMIS"'
Publikováno v:
The VLDB Journal 33.6 (2024): 1887-1911
Data series indexes are necessary for managing and analyzing the increasing amounts of data series collections that are nowadays available. These indexes support both exact and approximate similarity search, with approximate search providing high-qua
Externí odkaz:
http://arxiv.org/abs/2412.09448
Similarity search is a fundamental operation for analyzing data series (DS), which are ordered sequences of real values. To enhance efficiency, summarization techniques are employed that reduce the dimensionality of DS. SAX-based approaches are the s
Externí odkaz:
http://arxiv.org/abs/2411.17483
Approximate Nearest Neighbor (ANN) search in high-dimensional Euclidean spaces is a fundamental problem with a wide range of applications. However, there is currently no ANN method that performs well in both indexing and query answering performance,
Externí odkaz:
http://arxiv.org/abs/2411.14754
Artificial intelligence (AI) has transformed various fields, significantly impacting our daily lives. A major factor in AI success is high-quality data. In this paper, we present a comprehensive review of the evolution of data quality (DQ) awareness
Externí odkaz:
http://arxiv.org/abs/2411.03007
Foundation models, while highly effective, are often resource-intensive, requiring substantial inference time and memory. This paper addresses the challenge of making these models more accessible with limited computational resources by exploring dime
Externí odkaz:
http://arxiv.org/abs/2409.12264
Graph-based indexes have been widely employed to accelerate approximate similarity search of high-dimensional vectors. However, the performance of graph indexes to answer different queries varies vastly, leading to an unstable quality of service for
Externí odkaz:
http://arxiv.org/abs/2408.13899
Publikováno v:
PVLDB, 17(9): 2241 - 2254, 2024
Locality-sensitive hashing (LSH) is a well-known solution for approximate nearest neighbor (ANN) search in high-dimensional spaces due to its robust theoretical guarantee on query accuracy. Traditional LSH-based methods mainly focus on improving the
Externí odkaz:
http://arxiv.org/abs/2406.10938
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Autor:
Ilbert, Romain, Tiomoko, Malik, Louart, Cosme, Odonnat, Ambroise, Feofanov, Vasilii, Palpanas, Themis, Redko, Ievgen
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions. We formulate a multi-task optimiza
Externí odkaz:
http://arxiv.org/abs/2406.10327
Autor:
Ilbert, Romain, Odonnat, Ambroise, Feofanov, Vasilii, Virmaux, Aladin, Paolo, Giuseppe, Palpanas, Themis, Redko, Ievgen
Transformer-based architectures achieved breakthrough performance in natural language processing and computer vision, yet they remain inferior to simpler linear baselines in multivariate long-term forecasting. To better understand this phenomenon, we
Externí odkaz:
http://arxiv.org/abs/2402.10198
Publikováno v:
Proceedings of the VLDB Endowment, Volume 17, Issue 3, Pages 553-562, 2023
Over the past decade, millions of smart meters have been installed by electricity suppliers worldwide, allowing them to collect a large amount of electricity consumption data, albeit sampled at a low frequency (one point every 30min). One of the impo
Externí odkaz:
http://arxiv.org/abs/2401.05381