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Academic Articles & Research Papers
Showing 13 of 13 academic articles
Tianbao Song, Zongyi Huang, Xin Liu, Jingbo Sun • Entropy
(2025)
This paper introduces a novel variational autoencoder model termed DVAE to prevent posterior collapse in text modeling. DVAE employs a dual-path architecture within its decoder: path A and path B. Path A makes the direct input of text insta...
DOAJ
Open Access (DOAJ)
DOI: 10.3390/e27040423
Khiabani Mohsen Malekalketab, Ismail Wan Khairuzzaman Wan, Ahadmotlaghi Ehsan, Barmaky Akbar • Organizacijų Vadyba: Sisteminiai Tyrimai
(2026)
There is substantial evidence indicating a positive linear relationship among job satisfaction (JS), organisational commitment (OC), and organisational citizenship behaviour (OCB), with OC often serving as a mediating variable. The interact...
DOAJ
Open Access (DOAJ)
DOI: 10.2478/mosr-2026-0006
L. Zhang, J. S. Peng, Z. Song, Z. B. Fan et al. • Journal of Advanced Transportation
(2023)
This study focuses on the psychological characteristics and empirically tests of the factors influencing distracted driving behaviours. This information is used as a reference for an intervention on dangerous driving behaviours. First, a di...
DOAJ
Open Access (DOAJ)
DOI: 10.1155/2023/7664577
Pekka Korhonen, Francis K.C. Hui, Jenni Niku, Sara Taskinen et al. • PeerJ
(2025)
Background Over the past decade, joint species distribution models (JSDMs) and model-based ordination have emerged as powerful tools for the analysis of community ecology data. Generalized linear latent variable models (GLLVMs) offer a flex...
DOAJ
Open Access (DOAJ)
DOI: 10.7717/peerj.20338
Avi Ben P. Andalecio, Edwin S. Martin • Journal of Tourism, Culinary, and Entrepreneurship
(2022)
Community-Based Tourism is seen for having a huge impact on rural communities in the Philippines and many countries. It is generally known for its pro-poor implications that deliver socio-cultural, ecological, and economic benefits to steer...
DOAJ
Open Access (DOAJ)
DOI: 10.37715/jtce.v2i1.2644
Dimitris Rizopoulos • Journal of Statistical Software
(2006)
The R package ltm has been developed for the analysis of multivariate dichotomous and polytomous data using latent variable models, under the Item Response Theory approach. For dichotomous data the Rasch, the Two-Parameter Logistic, and Bi...
DOAJ
Open Access (DOAJ)
DOI: 10.18637/jss.v017.i05
Yue Zhang, Zhiyang Teng • Cambridge University Press eBooks
(2020)
With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are sy...
OPENALEX
DOI: https://doi.org/10.1017/9781108332873
Sebastian Ruder • Arrow@dit (Dublin Institute of Technology)
(2019)
The current generation of neural network-based natural language processing models excels at learning from large amounts of labelled data. Given these capabilities, natural language processing is increasingly applied to new tasks, new domain...
OPENALEX
Open Access
DOI: https://doi.org/10.13025/17598
Michael Isard • IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
(2003)
Probabilistic models have been adopted for many computer vision applications, however inference in high-dimensional spaces remains problematic. As the state-space of a model grows, the dependencies between the dimensions lead to an exponent...
OPENALEX
DOI: https://doi.org/10.1109/cvpr.2003.1211410
Sebastian Nowozin • now publishers, Inc. eBooks
(2010)
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. Structu...
OPENALEX
DOI: https://doi.org/10.1561/9781601984579
Sebastian Nowozin, Christoph H. Lampert • Foundations and Trends® in Computer Graphics and Vision
(2011)
Powerful statistical models that can be learned efficiently from large amounts of data are currently revolutionizing computer vision. These models possess a rich internal structure reflecting task-specific relations and constraints. This mo...
OPENALEX
DOI: https://doi.org/10.1561/0600000033
Vijayamanikandan Vijayarangan, Harshavardhana A. Uranakara, Francisco E. Hernández–Pérez, Hong G. Im • Energy and AI
(2025)
Using information theory, this study provides insights into how the construction of latent space of autoencoder (AE) using deep neural network (DNN) training finds a smooth (non-stiff) low-dimensional manifold in the stiff dynamical system....
DOAJ
Open Access (DOAJ)
DOI: 10.1016/j.egyai.2025.100567
• De Gruyter eBooks
(2020)
This book focuses on the fundamentals of deep learning along with reporting on the current state-of-art research on deep learning. In addition, it provides an insight of deep neural networks in action with illustrative coding examples. Deep...
OPENALEX
DOI: https://doi.org/10.1515/9783110670905