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Found 9 papers, 0 papers with code
Random Forest Kernel for High-Dimension Low Sample Size Classification
High dimension, low sample size (HDLSS) problems are numerous among real-world applications of machine learning.
Multiple Noises in Diffusion Model for Semi-Supervised Multi-Domain Translation
Domain-to-domain translation involves generating a target domain sample given a condition in the source domain.
Towards Long-Term predictions of Turbulence using Neural Operators
This paper explores Neural Operators to predict turbulent flows, focusing on the Fourier Neural Operator (FNO) model.
Domain Translation via Latent Space Mapping
In this paper, we investigate the problem of multi-domain translation: given an element $a$ of domain $A$, we would like to generate a corresponding $b$ sample in another domain $B$, and vice versa.
Random Forest for Dissimilarity-based Multi-view Learning
Many classification problems are naturally multi-view in the sense their data are described through multiple heterogeneous descriptions.
A Novel Random Forest Dissimilarity Measure for Multi-View Learning
Its main challenge is most often to exploit the complementarities between these representations to help solve a classification/regression task.
Dynamic voting in multi-view learning for radiomics applications
Cancer diagnosis and treatment often require a personalized analysis for each patient nowadays, due to the heterogeneity among the different types of tumor and among patients.
Improve the performance of transfer learning without fine-tuning using dissimilarity-based multi-view learning for breast cancer histology images
In the context of ICIAR 2018 Grand Challenge on Breast Cancer Histology Images, we compare one handcrafted feature extractor and five transfer learning feature extractors based on deep learning.
Dissimilarity-based representation for radiomics applications
Radiomics is a term which refers to the analysis of the large amount of quantitative tumor features extracted from medical images to find useful predictive, diagnostic or prognostic information.
