Zobrazeno 1 - 10
of 49
pro vyhledávání: '"Bhadra Sahely"'
Groundbreaking inventions and highly significant performance improvements in deep learning based Natural Language Processing are witnessed through the development of transformer based large Pre-trained Language Models (PLMs). The wide availability of
Externí odkaz:
http://arxiv.org/abs/2301.09003
Traditionally, kernel methods rely on the representer theorem which states that the solution to a learning problem is obtained as a linear combination of the data mapped into the reproducing kernel Hilbert space (RKHS). While elegant from theoretical
Externí odkaz:
http://arxiv.org/abs/2108.12199
Publikováno v:
In Natural Language Processing Journal June 2024 7
Studies of networked phenomena, such as interactions in online social media, often rely on incomplete data, either because these phenomena are partially observed, or because the data is too large or expensive to acquire all at once. Analysis of incom
Externí odkaz:
http://arxiv.org/abs/2001.07607
Publikováno v:
Algorithms for Molecular Biology, Vol 4, Iss 1, p 5 (2009)
Abstract Background A genetic network can be represented as a directed graph in which a node corresponds to a gene and a directed edge specifies the direction of influence of one gene on another. The reconstruction of such networks from transcript pr
Externí odkaz:
https://doaj.org/article/482ede298d6d4eec8cfd84a2aa691f50
Time series data is ubiquitous in the real-world problems across various domains including healthcare, social media, and crime surveillance. Detecting anomalies, or irregular and rare events, in time series data, can enable us to find abnormal events
Externí odkaz:
http://arxiv.org/abs/1906.05205
Publikováno v:
In Neurocomputing 28 October 2022 511:22-33
In this paper, we introduce the first method that (1) can complete kernel matrices with completely missing rows and columns as opposed to individual missing kernel values, (2) does not require any of the kernels to be complete a priori, and (3) can t
Externí odkaz:
http://arxiv.org/abs/1602.02518
Publikováno v:
AI and Ethics; 20240101, Issue: Preprints p1-6, 6p
This paper presents gradKCCA, a large-scale sparse non-linear canonical correlation method. Like Kernel Canonical Correlation Analysis (KCCA), our method finds non-linear relations through kernel functions, but it does not rely on a kernel matrix, a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______661::ad7a7fcbbe7bd82bab402c04c6df01a1
https://aaltodoc.aalto.fi/handle/123456789/40288
https://aaltodoc.aalto.fi/handle/123456789/40288