The accurate prediction of drought probability in specific regions is cr...
Adversarial attacks expose vulnerabilities of deep learning models by
in...
In machine learning models, the estimation of errors is often complex du...
Given the escalating risks of malicious attacks in the finance sector an...
Determining the degree of confidence of deep learning model in its predi...
The financial industry relies on deep learning models for making importa...
Portfolio management is an essential part of investment decision-making....
Multi-label classification is a natural problem statement for sequential...
Massive samples of event sequences data occur in various domains, includ...
Machine learning models are widely used to solve real-world problems in
...
Determining and predicting reservoir formation properties for newly dril...
The similarity learning problem in the oil & gas industry aims to constr...
Adopting data-based approaches leads to model improvement in numerous Oi...
The performance of modern deep learning-based systems dramatically depen...
There is a constant need for high-performing and computationally efficie...
Ensembling is a popular and effective method for improving machine learn...
Transferring a deep neural network trained on one problem to another req...
The change point is a moment of an abrupt alteration in the data
distrib...
A change points detection aims to catch an abrupt disorder in data
distr...
A memory efficient approach to ensembling neural networks is to share mo...
One of the first steps during the investigation of geological objects is...
Robustness of huge Transformer-based models for natural language process...
Machine learning models using transaction records as inputs are popular ...
Change points are abrupt alterations in the distribution of sequential d...
There is emerging attention towards working with event sequences. In
par...
Fraud in healthcare is widespread, as doctors could prescribe unnecessar...
We examine the applicability of modern neural network architectures to t...
An adversarial attack paradigm explores various scenarios for vulnerabil...
One of the main challenges in the construction of oil and gas wells is t...
During the directional drilling, a bit may sometimes go to a nonproducti...
The key idea of Bayesian optimization is replacing an expensive target
f...
In order to bridge the gap of more than 15m between the drilling bit and...
Engineers widely use Gaussian process regression framework to construct
...
Engineering problems often involve data sources of variable fidelity wit...