Results 31 to 40 of about 3,337,492 (248)

Combining cosmological datasets: hyperparameters and Bayesian evidence [PDF]

, 2002
A method is presented for performing joint analyses of cosmological datasets, in which the weight assigned to each dataset is determined directly by it own statistical properties.
Bishop, Bond, Bridle, Bridle, De Bernardis, Erdogdu, Godwin, Halverson, Jaffe, Jaynes, Jaynes, John, Knox, Lahav, Lee, Lewis, M. P. Hobson, Netterfield, O Lahav, Press, S. L. Bridle, Shannon, Sivia   +22 more
core   +3 more sources

From a Visual Scene to a Virtual Representation: A Cross-Domain Review

IEEE Access, 2023
The widespread use of smartphones and other low-cost equipment as recording devices, the massive growth in bandwidth, and the ever-growing demand for new applications with enhanced capabilities, made visual data a must in several scenarios, including ...
Americo Pereira, Pedro Carvalho, Nuno Pereira, Paula Viana, Luis Corte-Real   +4 more
doaj   +1 more source

Hyperparameter Importance Across Datasets

, 2018
With the advent of automated machine learning, automated hyperparameter optimization methods are by now routinely used in data mining. However, this progress is not yet matched by equal progress on automatic analyses that yield information beyond ...
Bergstra J., Bonilla E. V., Brazdil P., Demvsar J., Feurer M., Jamieson K., Joachims T., Klein A., Li L., Loshchilov I., Sobol I. M., van Rijn J. N., van Rijn J. N., Wistuba M.   +13 more
core   +1 more source

Handling Massive N-Gram Datasets Efficiently [PDF]

, 2018
This paper deals with the two fundamental problems concerning the handling of large n-gram language models: indexing, that is compressing the n-gram strings and associated satellite data without compromising their retrieval speed; and estimation, that is
Pibiri, Giulio Ermanno, Venturini, Rossano   +1 more
core   +3 more sources

A Comprehensive Survey on Deep Learning Methods in Human Activity Recognition

Machine Learning and Knowledge Extraction
Human activity recognition (HAR) remains an essential field of research with increasing real-world applications ranging from healthcare to industrial environments.
Michail Kaseris, Ioannis Kostavelis, Sotiris Malassiotis   +2 more
doaj   +1 more source

Datasets

, 2023
The data folder contains data counts from user generated contributions (Twitter, TripAdvisor, Flickr, Wikipedia, Google Maps) and the reference data for attractions in Florida and Carinthia respectively.
Weiqi He, Hao, Bin, Li, Shuaixia, Wenbo Luo, Shuaicheng Liu   +4 more
openaire   +2 more sources

Explainable Machine Learning for Malware Detection on Android Applications

Information
The presence of malicious software (malware), for example, in Android applications (apps), has harmful or irreparable consequences to the user and/or the device.
Catarina Palma, Artur Ferreira, Mário Figueiredo   +2 more
doaj   +1 more source

A Systematic Literature Review of Current Research Trends in Operational and Related Technology Threats, Threat Detection, and Security Insurance

Applied Sciences
The expansion of operation technology (OT) use and its tight integration with classical information and communication technologies have led not only to additional and improved possibilities in monitoring physical/manufacturing processes and the emergency
Nikolaj Goranin, Dainius Čeponis, Antanas Čenys   +2 more
doaj   +1 more source

Natural Intelligence in Search of Ways to Apply Artificial One (Results of the Scientific and Practical Conference “The Use of Artificial Intelligence in Library and Information Activities”)

Библиосфера
The article is an analytical review of reports and presentations made at the scientific conference “Application of Artificial Intelligence in Library and Information Activities” and held by INION RAS on May 29, 2024.
V. K. Stepanov
doaj   +1 more source

Comparison of Recent SnIa datasets

, 2009
We rank the six latest Type Ia supernova (SnIa) datasets (Constitution (C), Union (U), ESSENCE (Davis) (E), Gold06 (G), SNLS 1yr (S) and SDSS-II (D)) in the context of the Chevalier-Polarski-Linder (CPL) parametrization $w(a)=w_0+w_1 (1-a)$, according to
, , A. Shafieloo, A.G. Riess ., A.G. Riess ., A.G. Riess ., A.J. Albrecht ., C. Bogdanos, E. Bertschinger, E.O. Kahya, ESSENCE collaboration, G. Miknaitis ., H. Wei, H.K. Jassal, J.C. Bueno Sanchez, K. Krisciunas ., L Perivolaropoulos, L. Perivolaropoulos, L. Perivolaropoulos, L. Samushia, M. Hicken ., M. Sami, Planck collaboration, R. Durrer, R. Kessler ., R. Lazkoz, R.-J. Yang, R.A. Daly ., S Nesseris, S. del Campo, S. Jha, S. Jha, S. Jha, S. Nesseris, S. Nesseris, S. Qi, S.M. Carroll, SDSS collaboration, Supernova Cosmology Project collaboration, Supernova Cosmology Project collaboration, Supernova Cosmology Project collaboration, Supernova Search Team collaboration, Supernova Search Team collaboration, Supernova Search Team collaboration, Supernova Search Team collaboration, T. Padmanabhan, T.M. Davis ., V. Sahni, W. Hu, W.H. Press et. al., W.J. Percival ., WMAP collaboration, WMAP collaboration   +52 more
core   +1 more source