Boosting novel category discovery over domains with soft contrastive learning and all in one classifier
Unsupervised domain adaptation (UDA) has proven to be highly effective in transferring
knowledge from a label-rich source domain to a label-scarce target domain. However, the …
knowledge from a label-rich source domain to a label-scarce target domain. However, the …
High-dimensional clustering onto Hamiltonian cycle
Clustering aims to group unlabelled samples based on their similarities. It has become a
significant tool for the analysis of high-dimensional data. However, most of the clustering …
significant tool for the analysis of high-dimensional data. However, most of the clustering …
Udrn: unified dimensional reduction neural network for feature selection and feature projection
Dimensional reduction (DR) maps high-dimensional data into a lower dimensions latent
space with minimized defined optimization objectives. The two independent branches of DR …
space with minimized defined optimization objectives. The two independent branches of DR …
Boosting unsupervised contrastive learning using diffusion-based data augmentation from scratch
Unsupervised contrastive learning methods have recently seen significant improvements,
particularly through data augmentation strategies that aim to produce robust and …
particularly through data augmentation strategies that aim to produce robust and …
[HTML][HTML] Addressing the algorithm selection problem through an attention-based meta-learner approach
E Díaz de León-Hicks, SE Conant-Pablos… - Applied Sciences, 2023 - mdpi.com
In the algorithm selection problem, where the task is to identify the most suitable solving
technique for a particular situation, most methods used as performance mapping …
technique for a particular situation, most methods used as performance mapping …
[HTML][HTML] Structure-preserving visualization for single-cell RNA-Seq profiles using deep manifold transformation with batch-correction
Dimensionality reduction and visualization play an important role in biological data analysis,
such as data interpretation of single-cell RNA sequences (scRNA-seq). It is desired to have …
such as data interpretation of single-cell RNA sequences (scRNA-seq). It is desired to have …
Exploring local interpretability in dimensionality reduction: Analysis and use cases
Dimensionality reduction is a crucial area in artificial intelligence that enables the
visualization and analysis of high-dimensional data. The main use of dimensionality …
visualization and analysis of high-dimensional data. The main use of dimensionality …
Roadmap towards meta-being
Metaverse is a perpetual and persistent multi-user environment that merges physical reality
with digital virtuality. It is widely considered to be the next revolution of the Internet. Digital …
with digital virtuality. It is widely considered to be the next revolution of the Internet. Digital …
Visual analytics of multivariate networks with representation learning and composite variable construction
Multivariate networks are commonly found in realworld data-driven applications. Uncovering
and understanding the relations of interest in multivariate networks is not a trivial task. This …
and understanding the relations of interest in multivariate networks is not a trivial task. This …
[HTML][HTML] Enhancing solar radiation predictions through COA optimized neural networks and PCA dimensionality reduction
TKN Fariz, SS Basha - Energy Reports, 2024 - Elsevier
The inherent variability and uncertainty of solar radiation, influenced by factors such as
seasons, weather, and cloud cover, pose significant challenges in accurately forecasting …
seasons, weather, and cloud cover, pose significant challenges in accurately forecasting …