Search Results for author:
Found 5 papers, 1 papers with code
Shifts 2.0: Extending The Dataset of Real Distributional Shifts
This creates a need to be able to assess how robustly ML models generalize as well as the quality of their uncertainty estimates.
MAIN: Multihead-Attention Imputation Networks
The problem of missing data, usually absent incurated and competition-standard datasets, is an unfortunate reality for most machine learning models used in industry applications.
Tensor-based Nonlinear Classifier for High-Order Data Analysis
We also introduce a new learning algorithm to train the model, and we evaluate the \textit{Rank}-1 FNN on third-order hyperspectral data.
Tensor-Based Classifiers for Hyperspectral Data Analysis
Then, we introduce learning algorithms to train both the linear and the non-linear classifier in a way to i) to minimize the error over the training samples and ii) the weight coefficients satisfies the {\it rank}-1 canonical decomposition property.
Data-Driven Background Subtraction Algorithm for in-Camera Acceleration in Thermal Imagery
Detection of moving objects in videos is a crucial step towards successful surveillance and monitoring applications.
