Zobrazeno 1 - 10
of 101 291
pro vyhledávání: '"k-nn"'
Autor:
Haris, Themistoklis
Despite their power, Transformers face challenges with long sequences due to the quadratic complexity of self-attention. To address this limitation, methods like $k$-Nearest-Neighbor ($k$NN) attention have been introduced [Roy, Saffar, Vaswani, Grang
Externí odkaz:
http://arxiv.org/abs/2411.04013
Autor:
Giannopoulos, Panagiotis G.1 (AUTHOR), Dasaklis, Thomas K.1 (AUTHOR) dasaklis@eap.gr, Rachaniotis, Nikolaos2 (AUTHOR)
Publikováno v:
Scientific Reports. 11/14/2024, Vol. 12 Issue 1, p1-13. 13p.
$K$-nearest neighbor language models ($k$NN-LMs), which integrate retrieval with next-word prediction, have demonstrated strong performance in language modeling as well as downstream NLP benchmarks. These results have led researchers to argue that mo
Externí odkaz:
http://arxiv.org/abs/2408.11815
In this paper, we discuss Mahalanobis k-NN: a statistical lens designed to address the challenges of feature matching in learning-based point cloud registration when confronted with an arbitrary density of point clouds, either in the source or target
Externí odkaz:
http://arxiv.org/abs/2409.06267
Autor:
Benrazek, Ala-Eddine, Kouahla, Zineddine, Farou, Brahim, Seridi, Hamid, Kemouguette, Ibtissem
The proliferation of interconnected devices in the Internet of Things (IoT) has led to an exponential increase in data, commonly known as Big IoT Data. Efficient retrieval of this heterogeneous data demands a robust indexing mechanism for effective o
Externí odkaz:
http://arxiv.org/abs/2408.16036
Autor:
Busolin, Francesco, Lucchese, Claudio, Nardini, Franco Maria, Orlando, Salvatore, Perego, Raffaele, Trani, Salvatore
Learned dense representations are a popular family of techniques for encoding queries and documents using high-dimensional embeddings, which enable retrieval by performing approximate k nearest-neighbors search (A-kNN). A popular technique for making
Externí odkaz:
http://arxiv.org/abs/2408.04981
Autor:
Kanagawa, Motonobu
We describe a fast computation method for leave-one-out cross-validation (LOOCV) for $k$-nearest neighbours ($k$-NN) regression. We show that, under a tie-breaking condition for nearest neighbours, the LOOCV estimate of the mean square error for $k$-
Externí odkaz:
http://arxiv.org/abs/2405.04919
Cross-encoder (CE) models which compute similarity by jointly encoding a query-item pair perform better than embedding-based models (dual-encoders) at estimating query-item relevance. Existing approaches perform k-NN search with CE by approximating t
Externí odkaz:
http://arxiv.org/abs/2405.03651
The rise of cloud computing has spurred a trend of transferring data storage and computational tasks to the cloud. To protect confidential information such as customer data and business details, it is essential to encrypt this sensitive data before c
Externí odkaz:
http://arxiv.org/abs/2403.09080
Publikováno v:
Statistics & Probability Letters, 171:109028, 2021
A fast and flexible $k$NN procedure is developed for dealing with a semiparametric functional regression model involving both partial-linear and single-index components. Rates of uniform consistency are presented. Simulated experiments highlight the
Externí odkaz:
http://arxiv.org/abs/2401.14848