Zobrazeno 1 - 10
of 7 997
pro vyhledávání: '"Laplacian Eigenmaps"'
Autor:
Tran, Loc Hoang
Face recognition is a very important topic in data science and biometric security research areas. It has multiple applications in military, finance, and retail, to name a few. In this paper, the novel hypergraph Laplacian Eigenmaps will be proposed a
Externí odkaz:
http://arxiv.org/abs/2405.16748
Publikováno v:
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024, pp. 3447-3458
Point cloud matching, a crucial technique in computer vision, medical and robotics fields, is primarily concerned with finding correspondences between pairs of point clouds or voxels. In some practical scenarios, emphasizing local differences is cruc
Externí odkaz:
http://arxiv.org/abs/2402.17372
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Publikováno v:
Kongzhi Yu Xinxi Jishu, Iss 2, Pp 117-125 (2024)
Electric submersible pump (ESP) oil production technology is widely used in non-flowing high-yield wells and high water-cut wells, but equipment faults are prone to occur during operation, and subsequent maintenance may trigger long-term downtime, wh
Externí odkaz:
https://doaj.org/article/33756d1047794406b478e197a2f284ed
Autor:
Wahl, Martin
Given i.i.d. observations uniformly distributed on a closed manifold $\mathcal{M}\subseteq \mathbb{R}^p$, we study the spectral properties of the associated empirical graph Laplacian based on a Gaussian kernel. Our main results are non-asymptotic err
Externí odkaz:
http://arxiv.org/abs/2402.16481
We develop nonparametric regression methods for the case when the true regression function is not necessarily smooth. More specifically, our approach is using the fractional Laplacian and is designed to handle the case when the true regression functi
Externí odkaz:
http://arxiv.org/abs/2402.14985
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Srivastava, Gaurav, Jangid, Mahesh
The complexity of high-dimensional datasets presents significant challenges for machine learning models, including overfitting, computational complexity, and difficulties in interpreting results. To address these challenges, it is essential to identi
Externí odkaz:
http://arxiv.org/abs/2307.15905
Autor:
Green, Alden1 (AUTHOR) aldenjg@stanford.edu, Balakrishnan, Sivaraman2 (AUTHOR), Tibshirani, Ryan J3 (AUTHOR)
Publikováno v:
Information & Inference: A Journal of the IMA. Sep2023, Vol. 12 Issue 3, p2423-2502. 80p.
Autor:
Chen, Kuilin, Lee, Chi-Guhn
Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or meta-learning on labeled meta-training data and cannot be a
Externí odkaz:
http://arxiv.org/abs/2210.03595