Results 11 to 20 of about 8,505,406 (249)

Predictability of machine learning framework in cross-section data

Open Physics, 2023
Today, the use of artificial intelligence in electron optics, as in many other fields, has begun to increase. In this scope, we present a machine learning framework to predict experimental cross-section data. Our framework includes 8 deep learning models
Isik Nimet, Eskicioglu Omer Can
doaj   +1 more source

Greedy structure learning from data that contain systematic missing values [PDF]

, 2022
Learning from data that contain missing values represents a common phenomenon in many domains. Relatively few Bayesian Network structure learning algorithms account for missing data, and those that do tend to rely on standard approaches that assume ...
Liu, Y, Constantinou, Anthony C., Anthony C. Constantinou, Liu, Yang, Yang Liu, Constantinou, AC   +5 more
core   +1 more source

Hierarchical graph learning for protein–protein interaction

Nature Communications, 2023
Despite recent progress, machine learning methods remain inadequate in modeling the natural protein-protein interaction (PPI) hierarchy for PPI prediction.
Ziqi Gao, Chenran Jiang, Jiawen Zhang, Xiaosen Jiang, Lanqing Li, Peilin Zhao, Huanming Yang, Yong Huang, Jia Li   +8 more
doaj   +1 more source

A Review of Causality for Learning Algorithms in Medical Image Analysis

, 2022
Medical image analysis is a vibrant research area that offers doctors and medical practitioners invaluable insight and the ability to accurately diagnose and monitor disease. Machine learning provides an additional boost for this area.
Vlontzos, Athanasios, Kainz, Bernhard, Rueckert, Daniel   +2 more
core   +1 more source

Meta-learning for enhancing machines' intelligence in manufacturing [PDF]

Tehnika
Machine learning is usually considered a branch of artificial intelligence that enables machines (computers) to learn from data and improve their performance without explicit programming.
Putnik Goran D.
doaj   +1 more source

Debiased/Double Machine Learning for Instrumental Variable Quantile Regressions

Econometrics, 2021
In this study, we investigate the estimation and inference on a low-dimensional causal parameter in the presence of high-dimensional controls in an instrumental variable quantile regression.
Jau-er Chen, Chien-Hsun Huang, Jia-Jyun Tien   +2 more
doaj   +1 more source

Double machine learning and automated confounder selection: A cautionary tale

Journal of Causal Inference, 2023
Double machine learning (DML) has become an increasingly popular tool for automated variable selection in high-dimensional settings. Even though the ability to deal with a large number of potential covariates can render selection-on-observables ...
Hünermund Paul, Louw Beyers, Caspi Itamar   +2 more
doaj   +1 more source

Band gap predictions of double perovskite oxides using machine learning

Communications Materials, 2023
The compositional and structural variety inherent to oxide perovskites spawn wide-ranging applications. In perovskites, the band gap Eg, a key material parameter for these applications, can be optimally controlled by varying the composition.
Anjana Talapatra, Blas Pedro Uberuaga, Christopher Richard Stanek, Ghanshyam Pilania   +3 more
doaj   +1 more source

DoubleML: An Object-Oriented Implementation of Double Machine Learning in R

Journal of Statistical Software
The R package DoubleML implements the double/debiased machine learning framework of Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, and Robins (2018).
Philipp Bach, Malte S. Kurz, Victor Chernozhukov, Martin Spindler, Sven Klaassen   +4 more
doaj   +1 more source

Learning labelled dependencies in machine translation evaluation [PDF]

, 2009
Recently novel MT evaluation metrics have been presented which go beyond pure string matching, and which correlate better than other existing metrics with human judgements.
He, Yifan, Way, Andy
core   +2 more sources